Research Computing Center Wiki - User contributions [en]

GeneMarkES-Sapelo2

2026-03-24T19:06:30Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
4.71

=== Author / Distributor ===
[http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Description ===
"Gene Prediction in Eukaryotes. Novel genomes can be analyzed by the program GeneMark-ES utilizing unsupervised training."
More details are at [http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

In order to use geneMarker you will need to download a key file and put it into your home directory. Please follow instructions as given below:

1) Go to http://topaz.gatech.edu/GeneMark/license_download.cgi, fill the requested fields are read the license text. Note: you can select any tool and platform actually, e.g. GeneMark-ES/ET/EP+ ver 4.72_lic and LINUX64 kernel 3.10-5, but make sure you select both a software option and an operating system option, otherwise the form will re-set when you enter your information and click "I agree."

2) After pressing "I agree ..." button you will be redirected to a download page. You will need to download the 64bit key file only. Here you have two options:
-A. If you're already logged into Sapelo2, you can right-click or CTRL + click on the 64-bit link and copy the URL, then 'cd' to your /home directory, type 'wget' and paste the URL into your terminal window and the key file gm_key_64.gz will be downloaded to your /home directory. Or,
-B. Click the 64-bit link in your browser. The key file gm_key_64.gz will be downloaded to your local drive, and you can then transfer it to the cluster using a transfer node or Globus. Please refer to [[Transferring Files]].

3) The key file downloaded is in gzip format, on the cluster please unpack it (with gunzip) and move it to ~/.gm_key (the name should be exactly like this, with a dot in the beginning), for example:

<pre class="gcommand">
gunzip gm_key_64.gz
mv gm_key_64 ~/.gm_key
</pre>

where ~ is your home directory. i.e /home/MyID. Once the .gm_key file has been placed in your home dir you should be able to run GeneMarker.

'''Version 4.71'''

Version 4.71, installed at

* /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/
* /apps/eb/GeneMark-ET/4.71-GCCcore-11.2.0/

To use it, please load the module with:
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.3.0
</div>

or
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.2.0
</div>

Here is an example of a shell script sub.sh to run it at the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=geneMarkJob 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load GeneMark-ET/4.71-GCCcore-11.3.0

gmes_petap.pl [options]
</div>

In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load GeneMark-ET/4.71-GCCcore-11.3.0
gmes_petap.pl

# -------------------
Usage: /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/gmes_petap.pl [options] --sequence [filename]

GeneMark-ES Suite version 4.71_lic
Suite includes GeneMark.hmm, GeneMark-ES, GeneMark-ET and GeneMark-EP algorithms.

Input sequence/s should be in FASTA format.

Select one of the gene prediction algorithms:

To run GeneMark-ES self-training algorithm
--ES

To run GeneMark-ET with hints from transcriptome splice alignments
--ET [filename]; file with intron coordinates from RNA-Seq read splice alignment in GFF format
--et_score [number]; default 10; minimum score of intron in initiation of the ET algorithm

To run GeneMark-EP with hints from protein splice alignments
--EP
--dbep [filename]; file with protein database in FASTA format
--ep_score [number,number]; default 4,0.25; minimum score of intron in initiation of the EP algorithm
or
--EP [filename]; file with intron coordinates from protein splice alignment in GFF format

To run GeneMark.hmm predictions using previously derived model
--predict_with [filename]; file with species specific gene prediction parameters

To run ES, ET or EP with branch point model. This option is most useful for fungal genomes
--fungus

To run hmm, ES, ET or EP in PLUS mode (prediction with hints)
--evidence [filename]; file with hints in GFF format

To run algorithms with alternative genetic codes
--gcode [number]; default 1; supported 1 and 6/26

Output formatting options:
--format [label]; default GTF; output gene prediction in GTF of GFF3 format
--work_dir [folder name]; default current working directory .;

Masking option
--soft_mask [number] or [auto]; default auto; to indicate that lowercase letters stand for repeats;
algorithm hard masks only lowercase repeats longer than specified length
In 'auto' mode hard masking threshold is selected by algorithm based on the size of the input genome
--mask_penalty [number] or [auto]; default 0.03;

Run options
--cores [number]; default 1; to run program with multiple threads
--pbs to run on cluster with PBS support
--v verbose

Optional sequence pre-processing parameters
--max_contig [number]; default 5000000; will split input genomic sequence into contigs shorter then max_contig
--min_contig [number]; default 50000 (10000 fungi);
will ignore contigs shorter than min_contig in training
--max_gap [number]; default 5000; will split sequence at gaps longer than max_gap
Letters 'n' and 'N' are interpreted as standing within gaps
--max_mask [number]; default 5000; will split sequence at repeats longer then max_mask
Letters 'x' and 'X' are interpreted as results of hard masking of repeats

Optional parameters
--max_intron [number]; default 10000 (3000 fungi); maximum length of intron
--max_intergenic [number]; default 50000 (10000 fungi); maximum length of intergenic regions
--min_contig_in_predict [number]; default 500; minimum allowed length of contig in prediction step
--min_gene_in_predict [number]; default 300 (120 fungi); minimum allowed gene length in prediction step
--gc_donor [value]; default 0.001; transition probability to GC donor in the range 0..1;
'off' switches GC donor model OFF
--gc3 [number]; GC3 cutoff in training for grasses

Developer options
--training to run only training step of algorithm; applicable to ES, ET or EP
--prediction to run only prediction step of algorithms using species parameters from previously executed training; applicable to ES, ET or EP
--usr_cfg [filename]; use custom configuration from this file
--ini_mod [filename]; use this file with parameters for algorithm initiation
--key_bin
--debug
# -------------------
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [http://exon.gatech.edu/GeneMark/license_download.cgi GeneMarkES]

=== System ===
64-bit Linux

GeneMarkES-Sapelo2

2026-03-24T18:16:34Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
4.71

=== Author / Distributor ===
[http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Description ===
"Gene Prediction in Eukaryotes. Novel genomes can be analyzed by the program GeneMark-ES utilizing unsupervised training."
More details are at [http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

In order to use geneMarker you will need to download a key file and put it into your home directory. Please follow instructions as given below:

1) Go to http://topaz.gatech.edu/GeneMark/license_download.cgi, fill the requested fields are read the license text. Note: you can select any tool and platform actually, e.g. GeneMark-ES/ET/EP+ ver 4.72_lic and LINUX64 kernel 3.10-5, but make sure you select both a software option and an operating system option, otherwise the form will re-set when you enter your information and click "I agree."

2) After pressing "I agree ..." button you will be redirected to a download page. You will need to download the 64bit key file only. The key file gm_key_64.gz will be downloaded to your local drive. Then please transfer it to the cluster using a transfer node or Globus. Please refer to [[Transferring Files]].

3) The key file downloaded is in gzip format, on the cluster please unpack it (with gunzip) and move it to ~/.gm_key (the name should be exactly like this, with a dot in the beginning), for example:

<pre class="gcommand">
gunzip gm_key_64.gz
mv gm_key_64 ~/.gm_key
</pre>

where ~ is your home directory. i.e /home/MyID. Once the .gm_key file has been placed in your home dir you should be able to run GeneMarker.

'''Version 4.71'''

Version 4.71, installed at

* /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/
* /apps/eb/GeneMark-ET/4.71-GCCcore-11.2.0/

To use it, please load the module with:
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.3.0
</div>

or
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.2.0
</div>

Here is an example of a shell script sub.sh to run it at the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=geneMarkJob 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load GeneMark-ET/4.71-GCCcore-11.3.0

gmes_petap.pl [options]
</div>

In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load GeneMark-ET/4.71-GCCcore-11.3.0
gmes_petap.pl

# -------------------
Usage: /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/gmes_petap.pl [options] --sequence [filename]

GeneMark-ES Suite version 4.71_lic
Suite includes GeneMark.hmm, GeneMark-ES, GeneMark-ET and GeneMark-EP algorithms.

Input sequence/s should be in FASTA format.

Select one of the gene prediction algorithms:

To run GeneMark-ES self-training algorithm
--ES

To run GeneMark-ET with hints from transcriptome splice alignments
--ET [filename]; file with intron coordinates from RNA-Seq read splice alignment in GFF format
--et_score [number]; default 10; minimum score of intron in initiation of the ET algorithm

To run GeneMark-EP with hints from protein splice alignments
--EP
--dbep [filename]; file with protein database in FASTA format
--ep_score [number,number]; default 4,0.25; minimum score of intron in initiation of the EP algorithm
or
--EP [filename]; file with intron coordinates from protein splice alignment in GFF format

To run GeneMark.hmm predictions using previously derived model
--predict_with [filename]; file with species specific gene prediction parameters

To run ES, ET or EP with branch point model. This option is most useful for fungal genomes
--fungus

To run hmm, ES, ET or EP in PLUS mode (prediction with hints)
--evidence [filename]; file with hints in GFF format

To run algorithms with alternative genetic codes
--gcode [number]; default 1; supported 1 and 6/26

Output formatting options:
--format [label]; default GTF; output gene prediction in GTF of GFF3 format
--work_dir [folder name]; default current working directory .;

Masking option
--soft_mask [number] or [auto]; default auto; to indicate that lowercase letters stand for repeats;
algorithm hard masks only lowercase repeats longer than specified length
In 'auto' mode hard masking threshold is selected by algorithm based on the size of the input genome
--mask_penalty [number] or [auto]; default 0.03;

Run options
--cores [number]; default 1; to run program with multiple threads
--pbs to run on cluster with PBS support
--v verbose

Optional sequence pre-processing parameters
--max_contig [number]; default 5000000; will split input genomic sequence into contigs shorter then max_contig
--min_contig [number]; default 50000 (10000 fungi);
will ignore contigs shorter than min_contig in training
--max_gap [number]; default 5000; will split sequence at gaps longer than max_gap
Letters 'n' and 'N' are interpreted as standing within gaps
--max_mask [number]; default 5000; will split sequence at repeats longer then max_mask
Letters 'x' and 'X' are interpreted as results of hard masking of repeats

Optional parameters
--max_intron [number]; default 10000 (3000 fungi); maximum length of intron
--max_intergenic [number]; default 50000 (10000 fungi); maximum length of intergenic regions
--min_contig_in_predict [number]; default 500; minimum allowed length of contig in prediction step
--min_gene_in_predict [number]; default 300 (120 fungi); minimum allowed gene length in prediction step
--gc_donor [value]; default 0.001; transition probability to GC donor in the range 0..1;
'off' switches GC donor model OFF
--gc3 [number]; GC3 cutoff in training for grasses

Developer options
--training to run only training step of algorithm; applicable to ES, ET or EP
--prediction to run only prediction step of algorithms using species parameters from previously executed training; applicable to ES, ET or EP
--usr_cfg [filename]; use custom configuration from this file
--ini_mod [filename]; use this file with parameters for algorithm initiation
--key_bin
--debug
# -------------------
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [http://exon.gatech.edu/GeneMark/license_download.cgi GeneMarkES]

=== System ===
64-bit Linux

EGAPx-Sapelo2

2026-01-27T19:20:12Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
N/A

=== Author / Distributor ===
Please see https://github.com/ncbi/egapx

=== Description ===
From https://github.com/ncbi/egapx: "EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

As of 01/06/2026, due to its current implementation of Nextflow, installing EGAPx centrally would potentially run the risk of overwriting important files; it currently also depends on the NCBI C++ toolkit, which has documented security vulnerabilities. As such, this page exists to outline the steps needed to run EGAPx from the /home directory.

First, start an interactive job and, in your /home directory, clone the repository and then cd to the new directory that this creates:
<pre class="gcommand">
git clone https://github.com/ncbi/egapx.git
cd egapx
</pre>

Since the dependencies for EGAPx are already installed, you don't need to create a new virtual environment; simply load the relevant software modules:
<pre class="gcommand">
ml Nextflow/24.10.2
ml Python/3.12.3-GCCcore-13.3.0
ml PyYAML/6.0.2-GCCcore-13.3.0
</pre>
Notes:
* As recently as EGAPx v0.5.0, the developers have been highly inconsistent in their use of version 1 vs. version 2 of Nextflow's domain-specific language, or "DSL," and Nextflow v25 does not tolerate these inconsistencies; therefore, you have to use Nextflow/24.10.2 in order for the pipeline to run.
* As of EGAPx v0.5.0, it appears that the software depends on having at least v13.3.0 of GCC/GCCcore available, so the versions of Python and PyYAML listed above are strongly recommended.


Now you need to run one of the test examples provided by the developers in order to acquire the necessary config files:
<pre class="gcommand">
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
</pre>

Note that it isn't strictly necessary to include "python3" at the start of the command, so long as the 'egapx.py' script in the 'ui/' directory has executable permissions (which it does by default). Running the execution script the first time will put all of the config files into a new subdirectory in your working directory ('egapx/') called 'egapx_config'. Of these, the one to use is 'slurm.config', which you will need to open with your preferred text editor and make the following changes:

The line
<pre class="gscript">
cacheDir = "/data/$USER/singularity"
</pre>
should be changed to
<pre class="gscript">
cacheDir = "$PWD/singularity"
</pre>

while the section
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="/data/$USER/singularity"
SINGULARITY_TMPDIR="/data/$USER/tmp"
}
</pre>

should be changed to:
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="$PWD/singularity"
SINGULARITY_TMPDIR="/$PWD/tmp"
}
</pre>

Finally, in the section called "process," there are only three things that need to be un-commented (remove the "//" at the start of the line) and changed as follows:
<pre class="gscript">
queue = 'batch'
queueSize = 20
</pre>
and
<pre class="gscript">
clusterOptions = ' --ntasks=1 '
</pre>

After this, you can create a job submission script that looks like this:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --job-name=ExampleJobName 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=1 
<nowiki>#</nowiki>SBATCH --mem=4G 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

ml purge
ml Nextflow/24.10.2
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0

python3 PATH/to/egapx/ui/egapx.py PATH/to/input_and_database_options.yaml -e slurm -o PATH/to/Output_Directory

</div>
In the real submission script, all the above underlined values need to be replaced by the actual values, particularly the relevant paths. Please note that, while it is perfectly fine to use EGAPx in your /home directory, the input and output directories (i.e., where files will be read from and written to) should NOT be located in your /home directory; please use somewhere else for input (if using local data) and output files, such your /scratch directory.
 
 
Example input YAML file:
<pre class="gcommand">
genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
</pre>

(Please see https://github.com/ncbi/egapx?tab=readme-ov-file#input-data-format for more information on the correct formatting for input data, and https://github.com/ncbi/egapx?tab=readme-ov-file#input-example for more examples.)

=== Documentation ===

Details and references are at: https://github.com/ncbi/egapx

Full help options
<pre class="gcommand">
$ ui/egapx.py -h
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR] [-r REPORT] [-n] [-st] [-so] [-lo] [-ot ORTHO_TAXID] [-dl] [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME] [filename]

Main script for EGAPx

options:
-h, --help show this help message and exit
-e EXECUTOR, --executor EXECUTOR
Nextflow executor, one of docker, singularity, aws, or local (for NCBI internal use only). Uses corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
Directory for executor config files, default is ./egapx_config. Can be also set as env EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
Working directory for cloud executor
-r REPORT, --report REPORT
Report file prefix for report (.report.html) and timeline (.timeline.html) files, default is in output directory
-n, --dry-run
-st, --stub-run
-so, --summary-only Print result statistics only if available, do not compute result
-lo, --logs-only Collect execution logs if available, put them in output directory, do not compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
func_name

run:
filename YAML file with input: section with at least genome: and reads: parameters
-o OUTPUT, --output OUTPUT
Output path

download:
-dl, --download-only Download external files to local storage, so that future runs can be isolated
</pre>

[[#top|Back to Top]]

Funannotate-Sapelo2

2026-01-14T18:27:28Z

Jordan: /* Running Program */

[[Category:Teaching]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
1.8.17

=== Author / Distributor ===

https://github.com/nextgenusfs/funannotate
https://funannotate.readthedocs.io/en/latest/

=== Description ===
"Funannotate is a genome prediction, annotation, and comparison software package. It was originally written to annotate fungal genomes (small eukaryotes ~ 30 Mb genomes), but has evolved over time to accomodate larger genomes."

More details are at https://funannotate.readthedocs.io/en/latest/

=== Running Program ===

* version 1.8.17 with PASA pipeline with MySQL support is installed as an Apptainer container in /apps/singularity-images/funannotate-1.8.17-MySQL-PASA/ . The image name is funannotate-1.8.17-mysql-production.sif.

To use this version of Funannotate in a batch job, please follow these setup steps in your current job working directory '''before''' submitting a batch job:

<pre class="gscript">
cp /apps/singularity-images/funannotate-1.8.17-MySQL-PASA/*.sh .
cp -r /apps/singularity-images/funannotate-1.8.17-MySQL-PASA/pasa_conf .
</pre>

Note:

Running the above cp commands will copy the following setup files and folder to your current job working directory:

* Three MySQL config scripts: '''1_init.sh''', '''2_create_user_and_db.sh''', and '''3_cleanup.sh'''
* A sample batch job submission script: '''sub.sh'''
* A PASA config folder: '''pasa_conf/'''

Below is the sample batch job submission script ('''sub.sh''') to run funannotate in a batch job using 80 CPU cores on the batch partition:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=funannotate-mysql 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=80 
<nowiki>#</nowiki>SBATCH --mem=80gb 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 

cd $SLURM_SUBMIT_DIR

<nowiki>#</nowiki> Initialize and start MySQL from inside of the container

./1_init.sh && ./2_create_user_and_db.sh

<nowiki>#</nowiki> Verify the installation of dependencies and show their versions (optional)

apptainer exec instance://funannotate-mysql /bin/bash -c "source activate /opt/funannotate && '''funannotate check --show-versions'''"

<nowiki>#</nowiki> Run a full test with PASA pipeline with MySQL support. You can run your own funannotate command line here.

apptainer exec instance://funannotate-mysql /bin/bash -c "source activate /opt/funannotate && '''funannotate test -t all --cpus 80'''"

<nowiki>#</nowiki> Clean up on the compute node by shutting down MySQL

./3_cleanup.sh

</div>

'''Notes:'''
# Funannotate uses the GeneMark gene prediction software, which means you need to have downloaded the key file for GeneMarker and put it into your home directory. Please follow instructions found at [[GeneMarkES-Sapelo2]].
# In the real submission script, at least all the above underlined values in Slurm headers need to be reviewed or to be replaced by the proper values.
# In the real submission script, the above the funannotate command lines in '''bold font''' can be replaced by your own funannotate command lines.

Please refer to [[Running Jobs on Sapelo2]] for more information running jobs on the Sapelo2 cluster.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
https://funannotate.readthedocs.io/en/latest/
https://github.com/PASApipeline/PASApipeline/wiki/setting-up-pasa-mysql
</pre>
[[#top|Back to Top]]

=== Installation ===

Source code is obtained from https://github.com/nextgenusfs/funannotate

=== System ===
64-bit Linux

EGAPx-Sapelo2

2026-01-09T18:36:53Z

Jordan: Updated to reflect that Nextflow v24 is required because the EGAPx developers have not fully updated their scripts to version 2 of Nextflow's domain-specific language (DSL2) and Nextflow v25 doesn't tolerate DSL1 artifacts.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
N/A

=== Author / Distributor ===
Please see https://github.com/ncbi/egapx

=== Description ===
From https://github.com/ncbi/egapx: "EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

As of 01/06/2026, due to its current implementation of Nextflow, installing EGAPx centrally would potentially run the risk of overwriting important files; it currently also depends on the NCBI C++ toolkit, which has documented security vulnerabilities. As such, this page exists to outline the steps needed to run EGAPx from the /home directory.

First, start an interactive job and, in your /home directory, clone the repository and then cd to the new directory that this creates:
<pre class="gcommand">
git clone https://github.com/ncbi/egapx.git
cd egapx
</pre>

Since the dependencies for EGAPx are already installed, you don't need to create a new virtual environment; simply load the relevant software modules:
<pre class="gcommand">
ml Nextflow/24.10.2
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0
</pre>
(Note: while you can almost certainly use other versions of Python and PyYAML, as recent as EGAPx v0.5.0, the developers have been highly inconsistent in their use of version 1 vs. version 2 of Nextflow's domain-specific language, or "DSL," and Nextflow v25 does not tolerate these inconsistencies; therefore, you have to use Nextflow/24.10.2 in order for the pipeline to run.)


Now you need to run one of the test examples provided by the developers in order to acquire the necessary config files:
<pre class="gcommand">
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
</pre>

Note that it isn't strictly necessary to include "python3" at the start of the command, so long as the 'egapx.py' script in the 'ui/' directory has executable permissions (which it does by default). Running the execution script the first time will put all of the config files into a new subdirectory in your working directory ('egapx/') called 'egapx_config'. Of these, the one to use is 'slurm.config', which you will need to open with your preferred text editor and make the following changes:

The line
<pre class="gscript">
cacheDir = "/data/$USER/singularity"
</pre>
should be changed to
<pre class="gscript">
cacheDir = "$PWD/singularity"
</pre>

while the section
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="/data/$USER/singularity"
SINGULARITY_TMPDIR="/data/$USER/tmp"
}
</pre>

should be changed to:
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="$PWD/singularity"
SINGULARITY_TMPDIR="/$PWD/tmp"
}
</pre>

Finally, in the section called "process," there are only three things that need to be un-commented (remove the "//" at the start of the line) and changed as follows:
<pre class="gscript">
queue = 'batch'
queueSize = 20
</pre>
and
<pre class="gscript">
clusterOptions = ' --ntasks=1 '
</pre>

After this, you can create a job submission script that looks like this:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --job-name=ExampleJobName 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=1 
<nowiki>#</nowiki>SBATCH --mem=4G 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

ml purge
ml Nextflow/24.10.2
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0

python3 PATH/to/egapx/ui/egapx.py PATH/to/input_and_database_options.yaml -e slurm -o PATH/to/Output_Directory

</div>
In the real submission script, all the above underlined values need to be replaced by the actual values, particularly the relevant paths. Please note that, while it is perfectly fine to use EGAPx in your /home directory, the input and output directories (i.e., where files will be read from and written to) should NOT be located in your /home directory; please use somewhere else for input (if using local data) and output files, such your /scratch directory.
 
 
Example input YAML file:
<pre class="gcommand">
genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
</pre>

(Please see https://github.com/ncbi/egapx?tab=readme-ov-file#input-data-format for more information on the correct formatting for input data, and https://github.com/ncbi/egapx?tab=readme-ov-file#input-example for more examples.)

=== Documentation ===

Details and references are at: https://github.com/ncbi/egapx

Full help options
<pre class="gcommand">
$ ui/egapx.py -h
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR] [-r REPORT] [-n] [-st] [-so] [-lo] [-ot ORTHO_TAXID] [-dl] [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME] [filename]

Main script for EGAPx

options:
-h, --help show this help message and exit
-e EXECUTOR, --executor EXECUTOR
Nextflow executor, one of docker, singularity, aws, or local (for NCBI internal use only). Uses corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
Directory for executor config files, default is ./egapx_config. Can be also set as env EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
Working directory for cloud executor
-r REPORT, --report REPORT
Report file prefix for report (.report.html) and timeline (.timeline.html) files, default is in output directory
-n, --dry-run
-st, --stub-run
-so, --summary-only Print result statistics only if available, do not compute result
-lo, --logs-only Collect execution logs if available, put them in output directory, do not compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
func_name

run:
filename YAML file with input: section with at least genome: and reads: parameters
-o OUTPUT, --output OUTPUT
Output path

download:
-dl, --download-only Download external files to local storage, so that future runs can be isolated
</pre>

[[#top|Back to Top]]

EGAPx-Sapelo2

2026-01-06T20:29:53Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
N/A

=== Author / Distributor ===
Please see https://github.com/ncbi/egapx

=== Description ===
From https://github.com/ncbi/egapx: "EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

As of 01/06/2026, due to its current implementation of Nextflow, installing EGAPx centrally would potentially run the risk of overwriting important files; it currently also depends on the NCBI C++ toolkit, which has documented security vulnerabilities. As such, this page exists to outline the steps needed to run EGAPx from the /home directory.

First, start an interactive job and, in your /home directory, clone the repository and then cd to the new directory that this creates:
<pre class="gcommand">
git clone https://github.com/ncbi/egapx.git
cd egapx
</pre>

Since the dependencies for EGAPx are already installed, you don't need to create a new virtual environment; simply load the relevant software modules:
<pre class="gcommand">
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0
</pre>

Now you need to run one of the test examples provided by the developers in order to acquire the necessary config files:
<pre class="gcommand">
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
</pre>

Note that it isn't strictly necessary to include "python3" at the start of the command, so long as the 'egapx.py' script in the 'ui/' directory has executable permissions (which it does by default). Running the execution script the first time will put all of the config files into a new subdirectory in your working directory ('egapx/') called 'egapx_config'. Of these, the one to use is 'slurm.config', which you will need to open with your preferred text editor and make the following changes:

The line
<pre class="gscript">
cacheDir = "/data/$USER/singularity"
</pre>
should be changed to
<pre class="gscript">
cacheDir = "$PWD/singularity"
</pre>

while the section
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="/data/$USER/singularity"
SINGULARITY_TMPDIR="/data/$USER/tmp"
}
</pre>

should be changed to:
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="$PWD/singularity"
SINGULARITY_TMPDIR="/$PWD/tmp"
}
</pre>

Finally, in the section called "process," there are only three things that need to be un-commented (remove the "//" at the start of the line) and changed as follows:
<pre class="gscript">
queue = 'batch'
queueSize = 20
</pre>
and
<pre class="gscript">
clusterOptions = ' --ntasks=1 '
</pre>

After this, you can create a job submission script that looks like this:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --job-name=ExampleJobName 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=1 
<nowiki>#</nowiki>SBATCH --mem=4G 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

ml purge
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0

python3 PATH/to/egapx/ui/egapx.py PATH/to/input_and_database_options.yaml -e slurm -o PATH/to/Output_Directory

</div>
In the real submission script, all the above underlined values need to be replaced by the actual values, particularly the relevant paths. Please note that, while it is perfectly fine to use EGAPx in your /home directory, the input and output directories (i.e., where files will be read from and written to) should NOT be located in your /home directory; please use somewhere else for input (if using local data) and output files, such your /scratch directory.
 
 
Example input YAML file:
<pre class="gcommand">
genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
</pre>

(Please see https://github.com/ncbi/egapx?tab=readme-ov-file#input-data-format for more information on the correct formatting for input data, and https://github.com/ncbi/egapx?tab=readme-ov-file#input-example for more examples.)

=== Documentation ===

Details and references are at: https://github.com/ncbi/egapx

Full help options
<pre class="gcommand">
$ ui/egapx.py -h
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR] [-r REPORT] [-n] [-st] [-so] [-lo] [-ot ORTHO_TAXID] [-dl] [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME] [filename]

Main script for EGAPx

options:
-h, --help show this help message and exit
-e EXECUTOR, --executor EXECUTOR
Nextflow executor, one of docker, singularity, aws, or local (for NCBI internal use only). Uses corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
Directory for executor config files, default is ./egapx_config. Can be also set as env EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
Working directory for cloud executor
-r REPORT, --report REPORT
Report file prefix for report (.report.html) and timeline (.timeline.html) files, default is in output directory
-n, --dry-run
-st, --stub-run
-so, --summary-only Print result statistics only if available, do not compute result
-lo, --logs-only Collect execution logs if available, put them in output directory, do not compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
func_name

run:
filename YAML file with input: section with at least genome: and reads: parameters
-o OUTPUT, --output OUTPUT
Output path

download:
-dl, --download-only Download external files to local storage, so that future runs can be isolated
</pre>

[[#top|Back to Top]]

EGAPx-Sapelo2

2026-01-06T20:27:31Z

Jordan: Updated the reasons why we can't install this centrally on Sapelo2.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
N/A

=== Author / Distributor ===
Please see https://github.com/ncbi/egapx

=== Description ===
From https://github.com/ncbi/egapx: "EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

As of 05/20/2025, due to its current implementation of Nextflow, installing EGAPx centrally would potentially run the risk of overwriting important files; it currently also depends on the NCBI C++ toolkit, which has documented security vulnerabilities. As such, this page exists to outline the steps needed to run EGAPx from the /home directory.

First, start an interactive job and, in your /home directory, clone the repository and then cd to the new directory that this creates:
<pre class="gcommand">
git clone https://github.com/ncbi/egapx.git
cd egapx
</pre>

Since the dependencies for EGAPx are already installed, you don't need to create a new virtual environment; simply load the relevant software modules:
<pre class="gcommand">
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0
</pre>

Now you need to run one of the test examples provided by the developers in order to acquire the necessary config files:
<pre class="gcommand">
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
</pre>

Note that it isn't strictly necessary to include "python3" at the start of the command, so long as the 'egapx.py' script in the 'ui/' directory has executable permissions (which it does by default). Running the execution script the first time will put all of the config files into a new subdirectory in your working directory ('egapx/') called 'egapx_config'. Of these, the one to use is 'slurm.config', which you will need to open with your preferred text editor and make the following changes:

The line
<pre class="gscript">
cacheDir = "/data/$USER/singularity"
</pre>
should be changed to
<pre class="gscript">
cacheDir = "$PWD/singularity"
</pre>

while the section
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="/data/$USER/singularity"
SINGULARITY_TMPDIR="/data/$USER/tmp"
}
</pre>

should be changed to:
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="$PWD/singularity"
SINGULARITY_TMPDIR="/$PWD/tmp"
}
</pre>

Finally, in the section called "process," there are only three things that need to be un-commented (remove the "//" at the start of the line) and changed as follows:
<pre class="gscript">
queue = 'batch'
queueSize = 20
</pre>
and
<pre class="gscript">
clusterOptions = ' --ntasks=1 '
</pre>

After this, you can create a job submission script that looks like this:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --job-name=ExampleJobName 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=1 
<nowiki>#</nowiki>SBATCH --mem=4G 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

ml purge
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0

python3 PATH/to/egapx/ui/egapx.py PATH/to/input_and_database_options.yaml -e slurm -o PATH/to/Output_Directory

</div>
In the real submission script, all the above underlined values need to be replaced by the actual values, particularly the relevant paths. Please note that, while it is perfectly fine to use EGAPx in your /home directory, the input and output directories (i.e., where files will be read from and written to) should NOT be located in your /home directory; please use somewhere else for input (if using local data) and output files, such your /scratch directory.
 
 
Example input YAML file:
<pre class="gcommand">
genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
</pre>

(Please see https://github.com/ncbi/egapx?tab=readme-ov-file#input-data-format for more information on the correct formatting for input data, and https://github.com/ncbi/egapx?tab=readme-ov-file#input-example for more examples.)

=== Documentation ===

Details and references are at: https://github.com/ncbi/egapx

Full help options
<pre class="gcommand">
$ ui/egapx.py -h
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR] [-r REPORT] [-n] [-st] [-so] [-lo] [-ot ORTHO_TAXID] [-dl] [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME] [filename]

Main script for EGAPx

options:
-h, --help show this help message and exit
-e EXECUTOR, --executor EXECUTOR
Nextflow executor, one of docker, singularity, aws, or local (for NCBI internal use only). Uses corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
Directory for executor config files, default is ./egapx_config. Can be also set as env EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
Working directory for cloud executor
-r REPORT, --report REPORT
Report file prefix for report (.report.html) and timeline (.timeline.html) files, default is in output directory
-n, --dry-run
-st, --stub-run
-so, --summary-only Print result statistics only if available, do not compute result
-lo, --logs-only Collect execution logs if available, put them in output directory, do not compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
func_name

run:
filename YAML file with input: section with at least genome: and reads: parameters
-o OUTPUT, --output OUTPUT
Output path

download:
-dl, --download-only Download external files to local storage, so that future runs can be isolated
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-12-05T15:05:13Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif

/apps/singularity-images/alphafold-3.0.1-JSON-4.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). Furthemore, the "unifiedmem" and "JSON-4" versions have unified memory enabled, which allows the GPU device to "borrow" host memory from the node for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens; not only does this substantially increase run times, it will also only work on the A100 nodes due to architectural conflicts with the H100 nodes. To use either of these versions with a large protein or other complex, please use the following Slurm header in your submission script in place of the corresponding "--gres" header seen in the sample job below (and note that you can remove the "#SBATCH --constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

Please also note that like many AI tools, AlphaFold is prone to "hallucinations" when dealing with inputs that are too large; this mostly manifests as results with polypeptides predicted to occupy the same 3D space, which obviously makes these results useless. While tests with a complex of up to ~9,700 tokens (the urease complex from Helicobacter pylori) completed successfully, to date, the largest complex observed to run correctly was ~7,300 tokens (the ornithine decarboxylase, specifically SpeF, complex from Salmonella).


AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.1-JSON-4.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

You can find extensive information on formatting input JSON files [https://github.com/google-deepmind/alphafold3/blob/main/docs/input.md here], but the following is a simple example:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-09-25T20:27:14Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif

/apps/singularity-images/alphafold-3.0.1-JSON-4.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). Furthemore, the "unifiedmem" and "JSON-4" versions have unified memory enabled, which allows the GPU device to "borrow" host memory from the node for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens; not only does this substantially increase run times, it will also only work on the A100 nodes due to architectural conflicts with the H100 nodes. To use either of these versions with a large protein or other complex, please use the following Slurm header in your submission script in place of the corresponding "--gres" header seen in the sample job below (and note that you can remove the "#SBATCH --constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

Please also note that like many AI tools, AlphaFold is prone to "hallucinations" when dealing with inputs that are too large; this mostly manifests as results with polypeptides predicted to occupy the same 3D space, which obviously makes these results useless. While tests with a complex of up to ~9,700 tokens (the urease complex from Helicobacter pylori) completed successfully, to date, the largest complex observed to run correctly was ~7,300 tokens (the ornithine decarboxylase, specifically SpeF, complex from Salmonella).


AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-09-24T13:55:49Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif

/apps/singularity-images/alphafold-3.0.1-JSON-4.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). Furthemore, the "unifiedmem" and "JSON-4" versions have unified memory enabled, which allows the GPU device to "borrow" host memory from the node for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens; not only does this substantially increase run times, it will also only work on the A100 nodes due to architectural conflicts with the H100 nodes. To use either of these versions with a large protein or other complex, please use the following Slurm header in your submission script in place of the corresponding "--gres" header seen in the sample job below (and note that you can remove the "#SBATCH --constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

Please also note that like many AI tools, AlphaFold is prone to "hallucinations" when dealing with inputs that are too large; this mostly manifests as results with polypeptides predicted to occupy the same 3D space, which obviously makes these results useless. While tests with a complex of up to ~9,700 tokens (the urease complex from Helicobacter pylori) completed successfully, to date, the largest complex observed to run correctly was ~7,500 tokens (the ornithine decarboxylase, specifically SpeF, complex from Salmonella).


AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-09-12T19:21:28Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif

/apps/singularity-images/alphafold-3.0.1-JSON-4.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). Furthemore, the "unifiedmem" and "JSON-4" versions have unified memory enabled, which allows the GPU device to "borrow" host memory from the node for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens; not only does this substantially increase run times, it will also only work on the A100 nodes due to architectural conflicts with the H100 nodes. To use either of these versions with a large protein or other complex, please use the following Slurm header in your submission script in place of the corresponding "--gres" header seen in the sample job below (and note that you don't need the "constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-09-12T19:20:11Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif

/apps/singularity-images/alphafold-3.0.1-JSON-4.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). Furthemore, the "unifiedmem" and "JSON-4" versions have unified memory enabled, which allows the GPU device to "borrow" host memory from the node for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens; not only does this substantially increase run times, it will also only work on the A100 nodes due to architectural conflicts with the H100 nodes; to use this version, please use the following Slurm header in your submission script in place of the corresponding "--gres" header seen in the sample job below (and note that you don't need the "constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

Bracken-Sapelo2

2025-08-27T15:21:15Z

Jordan: Created this page to correspond to making the database files available in /db/Bracken/

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
3.1

=== Author / Distributor ===
[https://github.com/jenniferlu717/Bracken Bracken]

=== Description ===
"Bracken (Bayesian Reestimation of Abundance with KrakEN) is a highly accurate statistical method that computes the abundance of species in DNA sequences from a metagenomics sample. Braken uses the taxonomy labels assigned by Kraken, a highly accurate metagenomics classification algorithm, to estimate the number of reads originating from each species present in a sample. Kraken classifies reads to the best matching location in the taxonomic tree, but does not estimate abundances of species. We use the Kraken database itself to derive probabilities that describe how much sequence from each genome is identical to other genomes in the database, and combine this information with the assignments for a particular sample to estimate abundance at the species level, the genus level, or above. Combined with the Kraken classifier, Bracken produces accurate species- and genus-level abundance estimates even when a sample contains two or more near-identical species.

NOTE: Bracken is compatible with both Kraken 1 and Kraken 2. However, the default kmer length is different depending on the version of Kraken used. If you use Kraken 1 defaults, specify 31 as the kmer length. If you use Kraken 2 defaults, specify 35 as the kmer length."
More details are at [https://ccb.jhu.edu/software/bracken/ Bracken instructions]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

We currently have the latest version of the following databases for Bracken available in /db/Bracken/20250827/:
<pre class="gcommand">
bacteria
core_nt
eupathdb
fungi
gtdb
pluspf
pluspfp
pluspfp-16
viral
</pre>
The bacteria database has been created for a read length of 100 (which is the default for Bracken), while the fungi and viral databases are for a read length of 150; all other databases can accommodate read lengths of 50, 75, 100, 150, 200, 250, and 300. Note, however, that only core_nt, eupathdb, gtdb, pluspf, pluspfp, and pluspfp-16 are also available for Kraken2 in /db/kraken2/20250814/.

Bracken analysis involves two steps, the first with Kraken 1 or 2 (Kraken2 is recommended as it involves only a single command and we also no longer have any Kraken1 databases available, so if you want to use Kraken1 you will need to acquire these for yourself) to generate a report file based on your FASTQ-formatted metagenomics sample, and the second with Bracken itself to estimate species- or other taxon-level abundances based on this report file. More detailed information can be found in the directions in the [https://github.com/jenniferlu717/Bracken#step-2-run-kraken-1-or-kraken-2-or-krakenuniq-and-generate-a-report-file GitHub] repository, bearing in mind that what you're doing is what the developers call "Step 2" and "Step 3" (their "Step 1" is to generate the Braken database files, but this isn't necessary because we already have them available).

'''Version 3.1'''

Version 3.1, installed at

* /apps/eb/Bracken/3.1-GCCcore-12.3.0/

To use it, please load the module with:
<div class="gscript2">
module load Bracken/3.1-GCCcore-12.3.0
</div>

Note that you need to load Kraken (if you use Kraken1 then you'll need to load it before you load Bracken, run the first step mentioned above, and then unload it before loading Bracken as we do not have Kraken1 installed with a compatible toolchain) and it's a good idea to load a toolchain-compatible version of Python (i.e., Python/3.11.3-GCCcore-12.3.0), as well.

Here is an example of a shell script sub.sh to run Bracken the batch partition using a read length of 150 and the PlusPF Kraken2/Bracken database:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=BrackenJob 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=4 
<nowiki>#</nowiki>SBATCH --mem100gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load Bracken/3.1-GCCcore-12.3.0 
module load Kraken2/2.1.3-gompi-2023a 
module load Python/3.11.3-GCCcore-12.3.0 

<nowiki>#</nowiki>Do Step 1, where "SAMPLE.fastq" is the input file containing your metagenomics sequencing reads 
kraken2 --db /db/kraken2/20250814/pluspf --threads 4 --report SAMPLE.kreport SAMPLE.fastq > SAMPLE.kraken 
 
<nowiki>#</nowiki>Do Step 2 using the .kreport file generated in Step 1 as the input 
bracken -d /db/Bracken/20250827/pluspf -i ./SAMPLE.kreport -r 150 -o ./SAMPLE_150mers.bracken 
</div>

In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values (make sure the value you use for "--cpus-per-task=" in the Slurm headers is the same as that used for "--threads" in the Kraken2 command)

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
$ module load Bracken/3.1-GCCcore-12.3.0
$ bracken -h
Usage: bracken -v -d MY_DB -i INPUT -o OUTPUT -w OUTREPORT -r READ_LEN -l LEVEL -t THRESHOLD
-v Echoes the current software version and exits
MY_DB location of Kraken database
INPUT Kraken REPORT file to use for abundance estimation
OUTPUT file name for Bracken default output
OUTREPORT New Kraken REPORT output file with Bracken read estimates
READ_LEN read length to get all classifications for (default: 100)
LEVEL level to estimate abundance at [options: D,P,C,O,F,G,S,S1,etc] (default: S)
THRESHOLD number of reads required PRIOR to abundance estimation to perform reestimation (default: 0)
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [https://github.com/jenniferlu717/Bracken Bracken]

=== System ===
64-bit Linux

Software

2025-08-27T14:30:04Z

Jordan: /* Partial List of Installed Software */

On Sapelo2 and on the Teaching cluster users have the option to install their own software or use software installed centrally by the GACRC staff. Detailed information on how to find and access software packages installed on Sapelo2 are available at the [[Software on Sapelo2]] page and on the teaching cluster at the [[Software on Teaching Cluster]] page.

Below is a very limited list of software packages installed on Sapelo2 and on the Teaching cluster. We only intend to make an application page for software packages that requires extra documentation not covered in [[Software on Sapelo2]] or [[Software on Teaching Cluster]].

To view a list of all modules installed on Sapelo2/Teaching, along with a short description of each module, please run the following command on Sapelo2/Teaching:
<pre class="gcommand">
module spider
</pre>
You can also use the command above to search for a specific application. For more information, please see [[Lmod]].

The '''file transfer''' nodes ('''xfer''' nodes) do not have environment modules installed. File transfer utilities are installed centrally on the xfer nodes and available on the users' default PATH. Such utilities include bs (basespace-cli), fpsync, rclone, rsync, etc.
https://wiki.gacrc.uga.edu/wiki/Main_Page

'''Note:''' Although we have not made individual software pages for the applications installed on Sapelo2, there are currently over 1300 software packages installed there. For more information, please see [[Software on Sapelo2]].

==Partial List of Installed Software==
Last updated '''{{REVISIONYEAR}}-{{REVISIONMONTH}}-{{REVISIONDAY2}}'''. This is a partial list of installed software so there may be software or versions of software available on Sapelo2 which are not included in this table.
'''Note:''' Click on the icons to the right of the column headings to sort the table.
[[#0|0-9]] [[#A|A]] [[#B|B]] [[#C|C]] [[#D|D]] [[#E|E]] [[#F|F]] [[#G|G]] [[#H|H]] [[#I|I]] [[#J|J]] [[#K|K]] [[#L|L]] [[#M|M]] [[#N|N]] [[#O|O]] [[#P|P]] [[#Q|Q]] [[#R|R]] [[#S|S]] [[#T|T]] [[#U|U]] [[#V|V]] [[#W|W]] [[#X|X]] [[#Y|Y]] [[#Z|Z]]
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" width="200px"| Name
! scope="col" class="unsortable" width="240px"| Version
! scope="col" width="160px"| Category
! scope="col" width="60px"| Cluster
! scope="col" width="20px"| Top
|-













| [[AlphaFold-Sapelo2|AlphaFold 2]] || 2.2.4, 2.3.1, 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->
| [[AlphaFold3-Sapelo2|AlphaFold 3]] || 3.0.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[AMBER-Sapelo2|AMBER]] || 22 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[anvi'o-Sapelo2|anvi'o]] || 4, 5.1, 5.3, 5.4, 6.1, 7, 7.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
















| [[basespace-cli-Sapelo2|basespace-cli]] || 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










|| [[Beast-Sapelo2|Beast]] || 1.10.4, 2.5.1, 2.6.3, 2.7.3, 2.7.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










| [[BioPython-Sapelo2|BioPython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[Biopython-Teaching|Biopython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-




| [[BLAST+-Sapelo2|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] ||[[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST+-Teaching|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[BLAST-Sapelo2|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST-Teaching|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Bowtie2-Sapelo2|Bowtie2]] || 2.2.3, 2.3.3, 2.3.4.1, 2.3.4.2, 2.3.5.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Bowtie2-Teaching|Bowtie2]] || 2.4.1, 2.4.4, 2.4.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Bracken-Sapelo2|Bracken]] || 3.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BRAKER-Sapelo2|BRAKER]] || 2.1.6, 3.0.3, 3.0.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



| [[BUSCO-Sapelo2|BUSCO]] || 2.0, 4.0.5, 5.4.7, 5.5.0, 5.8.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[Cactus-Sapelo2|Cactus]] || 1.2.3, 2.0.3, 2.4.3, 2.4.4, 2.5.0, 2.6.0, 2.6.7, 2.6.9, 2.7.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Canu-Sapelo2|Canu]] || 2.2||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[CellRanger-ATAC-Sapelo2|CellRanger-ATAC]] || 2.1.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[Chimera-Teaching|Chimera]] || 1.16 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-























| [[cryoSPARC-Sapelo2|CryoSPARC]] || 3.3.1 || [[:Category:Engineering|Engineering]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Cromwell-Sapelo2|Cromwell]] || 56 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|[[CUDA-Sapelo2|CUDA]] || 11.3.1, 11.4.1, 11.7.0, 12.0.0, 12.1.1, 12.2.0, 12.4.0, 12.6.0 ||[[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Cytoscape-Sapelo2|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Cytoscape-Teaching|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-



























|-->
| [[dMRIharmonization-Sapelo2|dMRIharmonization]] || 20240227 || [[:Category:Engineering|Engineering]]|| [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-->





| [[EGAPx-Sapelo2|EGAPx]] || N/A || [[:Category:Bioinformatics|Bioinformatics]] || N/A || [[File:Up20.png|20px|link=#top]]
|- -->


| [[EIGENSOFT-Sapelo2|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[EIGENSOFT-Teaching|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Flankophile-Sapelo2|Flankophile]] || 0.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-






































|-
| [[Funannotate-Sapelo2|Funannotate]] || 1.8.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[GAUSSIAN-Sapelo2|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[GAUSSIAN-Teaching|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[GaussView-Sapelo2|GaussView]] || 5, 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[GaussView-Teaching|GaussView]] || 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[GEM-Sapelo2|GEM]] || 1.5.1 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[GeneMarkES-Sapelo2|GeneMarkET]] || 4.71 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





























|[[gocryptfs-Sapelo2|gocryptfs]] || 2.5.2 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[GROMACS-Sapelo2 |GROMACS]] || 2021.5, 2023.1, 2023.4 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


















| [[HISAT2-Sapelo2|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[HISAT2-Teaching|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[HpcGridRunner-Sapelo2|HPCGridRunner]] || 1.0.2 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










|-
| [[iCommands-Sapelo2|iCommands]] || 4.2.8 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[InterProScan-Sapelo2|InterProScan]] || 5.63-95.0, 5.64-96.0, 5.66-98.0 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[IQ-Tree-Sapelo2|IQ-Tree]] || 1.6.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[IQ-Tree-Teaching|IQ-Tree]] || 1.6.12|| [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-








| [[Java-Teaching|Java]] || 1.8.0_241, 11.0.16, 13.0.2, 15.0.1, 17.0.6 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Jupyter-Sapelo2|Jupyter]] || 6.5.6, 7.0.2, 7.2.0, 7.2.3 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Jupyter-Teaching|Jupyter]] || 6.4.12 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[LAMMPS-Sapelo2|LAMMPS]] || 23Jun2022 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



































| [[Magma-Sapelo2|Magma]] || 2.27-1 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-


| [[Maker-Sapelo2|Maker]] || 3.01.04 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[MATLAB-Sapelo2|MATLAB]] || R2023a, R2023b || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[Medusa-Sapelo2 |Medusa]] || 1.6 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-













| [[MetaPhlAn4-Sapelo2|MetaPhlAn4]] || 4.0.3.1, 4.0.6, 4.1.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
























| [[MOTECC_qmdcp-Teaching|MOTECC_qmdcp]] || 90 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Mothur-Sapelo2|Mothur]] || 1.45.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->




















































| [[Ont-Guppy-Sapelo2|Ont-Guppy]] || 6.5.7 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[ORCA-Sapelo2 |ORCA]] || 4.2.1, 5.0.4 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




|[[ORP-Sapelo2|ORP]] || 2.2.6, 2.2.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[OrthoFinder-Sapelo2|OrthoFinder]] || 2.5.4, 2.5.5 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[PAML-Teaching|PAML]] || 4.9j, 4.10.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->









| [[PASA-Sapelo2|PASA]] || 2.5.3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[Perl-Teaching|Perl]] || 5.30.0, 5.30.2, 5.32.0, 5.32.1, 5.34.0, 5.34.1, 5.36.0, 5.36.1 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[Phenix-Sapelo2|Phenix]] || 1.21.2-5419 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

























































|-
| [[Python-Sapelo2 |Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Python-Teaching|Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->






| [[QIIME2-Sapelo2|QIIME2]] || 2020.6, 2023.2, 2023.5.1, 2023.7, 2023.9, 2024.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[R-Sapelo2 |R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 ||[[:Category:Statistics|Statistics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[R-Teaching|R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 || [[:Category:Statistics|Statistics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[racon-Sapelo2 |Racon]] || 1.5.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[RAxML-NG-Sapelo2 |RAxML-NG]] || 1.2.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Sapelo2 |RAxML]] || 8.2.11, 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Teaching|RAxML]] || 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

















| [[RevBayes-Teaching|RevBayes]] || 1.1.0, 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Rosetta-Sapelo2 |Rosetta]] || 2021.16.61629, 2022.46.334 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[Rstudio-Sapelo2|Rstudio]] || 2023.06.1-524 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[SAMtools-Teaching|SAMtools]] || 0.1.20, 1.6, 1.14, 1.16.1, 1.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

| [[SequenceTubeMap-Sapelo2|sequenceTubeMap]] || 20230906 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->









































| [[snpEff-Sapelo2|snpEff]] || 5.0e || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





















| [[SRAToolKit-Sapelo2 |SRAToolKit]] || 3.0.1, 3.0.3 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[StringTie-Teaching|StringTie]] || 2.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Structure-Sapelo2|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Structure-Teaching|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


































| [[Trinity-Sapelo2|Trinity]] || 2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[UBCG-Sapelo2|UBCG]] || 3.0
|[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-














| [[Velvet-Sapelo2|Velvet]] || 1.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->






















|}
[[#top|Back To Top]] [[File:Up20.png|20px|link=#top]]

[[Category:Sapelo2]][[Category:Teaching]][[Category:Software]]

AlphaFold-Sapelo2

2025-08-13T20:56:02Z

Jordan: /* Documentation */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
2.3.2

=== Author / Distributor ===
Please see https://github.com/deepmind/alphafold

=== Description ===
From https://github.com/deepmind/alphafold: "This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo2 please see the [[Lmod]] page. Note that as of the OS upgrade from Rocky 8 to Rocky 9 at the end of July, 2025, the previous Singularity containers for older versions of AlphaFold no longer work and are therefore no longer available to users.

*'''Version 2.3.2'''

Installed with EasyBuild in /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

To use this version of AlphaFold, please first load the module with
<pre class="gscript">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
</pre>

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1. The Python script run_alphafold.py is installed in EBROOTALPHAFOLD/bin and a symbolic link called alphafold points to it and can be used to run the program. The database files are in /db/AlphaFold/2.3.2 (note that /apps/db/ directories no longer exist). You can export the environment variable ALPHAFOLD_DATA_DIR to set the location of the database files. For bash, use
<pre class="gscript">
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
</pre>

'''Note:''' Remember to request a P100, A100, or H100 GPU device. This version requires a GPU device and it should work on the P100, A100, and H100 devices.

'''Sample Job Submission scripts'''

Sample job submission script (sub.sh) to run AlphaFold 2.3.2 in a batch job (with GPU):

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafoldjobname
#SBATCH --partition=gpu_p
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --gres=gpu:A100:1 #You can specify other GPU devices, like 'H100', here
#SBATCH --mem=40gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold [options]

</pre>

where [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well.

An example of the options to use for the alphafold script:
<pre class="gscript">
alphafold --data_dir /db/AlphaFold/2.3.1 --output_dir ./output --model_names model_1 --fasta_paths ./query.fasta --max_template_date 2021-11-17
</pre>

Example of job submission
<pre class="gcommand">
sbatch sub.sh
</pre>

=== A method to accelerate your calculations ===

If you have many AlphaFold calculations, i.e., need to process many FASTA files, you can run them using an array job (see [[Array_Jobs|here]]).

At the same time you can greatly speed up your calculations by running two separate jobs using the CPU and GPU nodes sequentially:

1. Run the first step of AlphaFold (MSA generation) on the CPU nodes on the batch partition.

2. Then run the second part of AlphaFold (structural modeling) using the '''--use_precomputed_msas=true''' option on the GPU nodes on the gpu_p partition. If the computation in this part takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition (see [[GPU|here]]).

In order to achieve this, you need to find a way to stop all running element jobs (or elements) in step 1 after all element jobs in step 1 have completed the MSA generation.

With the help from a GACRC user, we have prepared a shell script called '''check_and_stop_elements.sh''':

1. This script will check for the presence of the "stop string", i.e., '''Running model model_1_multimer_v3_pred_0''', in the .err file of each running element you started in step 1.

2. You will run this script in an interactive session (see [[Running_Jobs_on_Sapelo2#How_to_open_an_interactive_session|here]]) or in an OOD X Desktop session (see [[OnDemand#X_Desktop_Session_.28A.K.A._The_Interactive_X_login_app.29|here]]) to monitor your elements during the time of elements running.

3. It will check your running elements with an interval of 5 minutes. If the "stop string" is found in a .err file of a running element, the script will automatically cancel the element for you, such that you can start step 2 as early as possible.

4. You can stop running the script (i.e. stop monitoring elements in step 1) by typing Ctrl + c keys on your keyboard.

As a quick demo, below is an example job submission script (sub_step1.sh) for running step 1 on batch:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphaMSAs
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --time=12:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

The above input.lst is a single-column text file storing the names of your FASTA files (please refer [[Array_Jobs#Non-Numbered_Input_Files|here]]), for example:
<pre class="gscript">
head -n 5 input.lst

PFago.v8prot.zeyw.fa
PFago.v8prot.zeyx.fa
PFago.v8prot.zeyy.fa
PFago.v8prot.zeyz.fa
PFago.v8prot.zeza.fa
</pre>

and those FASTA files are stored in a folder called inputs in your current job working folder (--fasta_paths=./inputs/$file).

Below is the shell script check_and_stop_elements.sh. You are welcome to copy it for your use:

<pre class="gscript">
#!/bin/bash

ArrayID=$1

while true; do
numOfRunning=$(squeue -l -j $ArrayID | grep RUNNING | wc -l)
echo
echo "Pending elements in array job $ArrayID:"
squeue -l -j $ArrayID | grep PENDING
echo
echo "Number of running elements in array job $ArrayID:"
echo "$numOfRunning"
echo
echo "Checking the stop string \"Running model model_1_multimer_v3_pred_0\" in alphaMSAs.*.err for each running element:"
echo

for j in $(squeue -l -j $ArrayID | grep RUNNING | awk '{print $1}')
do
jobid=$(scontrol show job $j | grep JobId= | awk '{print $1}' | sed -nr 's|JobId=()|\1|p')
echo -n "Checking alphaMSAs.${jobid}.err ... "
grep -m 1 "Running model model_1_multimer_v3_pred_0" alphaMSAs.${jobid}.err 2>&1 1>/dev/null
if [ $? -eq 0 ]; then
echo -en "\e[31mthe stop string is found!\e[0m ... go to cancel job ${jobid} ($j) ... scancel $j ... \e[32mjob canceled.\e[0m\n"
scancel $j
else
echo -e "the stop string is NOT found!"
fi
done
sleep 300
clear
done
</pre>

Before you can run it, please change its mode to executable by:

<pre class="gscript">
chmod 755 ./check_and_stop_elements.sh
</pre>

Then you can open an interactive session or an OOD X Desktop session to run this script to monitor your elements when they are running on compute nodes by:

<pre class="gscript">
./check_and_stop_elements.sh <ARRAY_JOB_ID>
</pre>

Please note. The above ARRAY_JOB_ID is the ID of your array job. For example, sq --me or sacct-gacrc -X reports you an JOBID 28026197_1, where 28026197 is the ID of your array job. So, the above command will be:

<pre class="gscript">
./check_and_stop_elements.sh 28026197
</pre>

Below is example outputs from running the above command line:

<pre class="gscript">
./check_and_stop_elements.sh 28026197

Pending elements in array job 28026197:

Number of running elements in array job 28026197:
6

Checking the stop string "Running model model_1_multimer_v3_pred_0" in alphaMSAs.*.err for each running element:

Checking alphaMSAs.28026197.err ... the stop string is found! ... go to cancel job 28026197 (28026197_10) ... scancel 28026197_10 ... job canceled.
Checking alphaMSAs.28026206.err ... the stop string is found! ... go to cancel job 28026206 (28026197_9) ... scancel 28026197_9 ... job canceled.
Checking alphaMSAs.28026205.err ... the stop string is NOT found!
Checking alphaMSAs.28026201.err ... the stop string is NOT found!
Checking alphaMSAs.28026200.err ... the stop string is NOT found!
Checking alphaMSAs.28026199.err ... the stop string is NOT found!
</pre>

Once all elements in step 1 have completed the MSA generation, you can go to step 2. As mentioned above, if the computation in step 2 takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition. Below is an example job submission script (sub_step2.sh) for running step 2 on the V100 scavenge GPU nodes:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --gres=gpu:V100:1
#SBATCH --time=4:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge

ml AAlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=8

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--use_precomputed_msas=true \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

Please note that we use '''--use_precomputed_msas=true''' option in the above alphafold command line.

=== Documentation ===

Details and references are at https://github.com/deepmind/alphafold.

'''Version 2.3.2:''' Full help options
<pre class="gcommand">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold --helpfull

Full AlphaFold protein structure prediction script.
flags:

/apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1/bin/alphafold:
--[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins.
(default: 'false')
--bfd_database_path: Path to the BFD database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')
--data_dir: Path to directory of supporting data.
(default: '/db/AlphaFold/2.3.2')
--db_preset: <full_dbs|reduced_dbs>: Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs)
(default: 'full_dbs')
--fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique
basename as the basename is used to name the output directories for each prediction.
(a comma separated list)
--hhblits_binary_path: Path to the HHblits executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhblits')
--hhsearch_binary_path: Path to the HHsearch executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhsearch')
--hmmbuild_binary_path: Path to the hmmbuild executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmbuild')
--hmmsearch_binary_path: Path to the hmmsearch executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmsearch')
--jackhmmer_binary_path: Path to the JackHMMER executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/jackhmmer')
--kalign_binary_path: Path to the Kalign executable.
(default: '/apps/eb/Kalign/3.4.0-GCCcore-12.3.0/bin/kalign')
--max_template_date: Maximum template release date to consider. Important if folding historical test sets.
--mgnify_database_path: Path to the MGnify database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/mgnify/mgy_clusters_2022_05.fa')
--model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model
(default: 'monomer')
--models_to_relax: <all|best|none>: The models to run the final relaxation step on. If `all`, all models are relaxed, which may be time consuming. If `best`, only the most confident model is relaxed. If `none`, relaxation is not run. Turning off relaxation might result
in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage.
(default: 'best')
--num_multimer_predictions_per_model: How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer
(default: '5')
(an integer)
--obsolete_pdbs_path: Path to file containing a mapping from obsolete PDB IDs to the PDB IDs of their replacements.
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/obsolete.dat')
--output_dir: Path to a directory that will store the results.
--pdb70_database_path: Path to the PDB70 database for use by HHsearch.
(default: '/db/AlphaFold/2.3.2/pdb70/pdb70')
--pdb_seqres_database_path: Path to the PDB seqres database for use by hmmsearch.
--random_seed: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic.
(an integer)
--small_bfd_database_path: Path to the small version of BFD used with the "reduced_dbs" preset.
--template_mmcif_dir: Path to a directory with template mmCIF structures, each named <pdb_id>.cif
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/mmcif_files')
--uniprot_database_path: Path to the Uniprot database for use by JackHMMer.
--uniref30_database_path: Path to the UniRef30 database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/uniref30/UniRef30_2023_02')
--uniref90_database_path: Path to the Uniref90 database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/uniref90/uniref90.fasta')
--[no]use_gpu_relax: Whether to relax on GPU. Relax on GPU can be much faster than CPU, so it is recommended to enable if possible. GPUs must be available if this setting is enabled.
(default: 'true')
--[no]use_precomputed_msas: Whether to read MSAs that have been written to disk instead of running the MSA tools. The MSA files are looked up in the output directory, so it must stay the same between multiple runs that are to reuse the MSAs. WARNING: This will not check
if the sequence, database or configuration have changed.
(default: 'false')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

tensorflow.python.ops.parallel_for.pfor:
--[no]op_conversion_fallback_to_while_loop: DEPRECATED: Flag is ignored.
(default: 'true')

tensorflow.python.tpu.client.client:
--[no]hbm_oom_exit: Exit the script when the TPU HBM is OOM.
(default: 'true')
--[no]runtime_oom_exit: Exit the script when the TPU runtime is OOM.
(default: 'true')

tensorflow.python.tpu.tensor_tracer_flags:
--delta_threshold: Log if history based diff crosses this threshold.
(default: '0.5')
(a number)
--[no]tt_check_filter: Terminate early to check op name filtering.
(default: 'false')
--[no]tt_single_core_summaries: Report single core metric and avoid aggregation.
(default: 'false')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')

</pre>

[[#top|Back to Top]]

=== Installation ===

*Version 2.3.2: Installed using EasyBuild.

*The database files are installed in /db/AlphaFold/2.3.2/

=== System ===
64-bit Linux

AlphaFold-Sapelo2

2025-08-13T20:53:20Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
2.3.2

=== Author / Distributor ===
Please see https://github.com/deepmind/alphafold

=== Description ===
From https://github.com/deepmind/alphafold: "This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo2 please see the [[Lmod]] page. Note that as of the OS upgrade from Rocky 8 to Rocky 9 at the end of July, 2025, the previous Singularity containers for older versions of AlphaFold no longer work and are therefore no longer available to users.

*'''Version 2.3.2'''

Installed with EasyBuild in /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

To use this version of AlphaFold, please first load the module with
<pre class="gscript">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
</pre>

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1. The Python script run_alphafold.py is installed in EBROOTALPHAFOLD/bin and a symbolic link called alphafold points to it and can be used to run the program. The database files are in /db/AlphaFold/2.3.2 (note that /apps/db/ directories no longer exist). You can export the environment variable ALPHAFOLD_DATA_DIR to set the location of the database files. For bash, use
<pre class="gscript">
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
</pre>

'''Note:''' Remember to request a P100, A100, or H100 GPU device. This version requires a GPU device and it should work on the P100, A100, and H100 devices.

'''Sample Job Submission scripts'''

Sample job submission script (sub.sh) to run AlphaFold 2.3.2 in a batch job (with GPU):

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafoldjobname
#SBATCH --partition=gpu_p
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --gres=gpu:A100:1 #You can specify other GPU devices, like 'H100', here
#SBATCH --mem=40gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold [options]

</pre>

where [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well.

An example of the options to use for the alphafold script:
<pre class="gscript">
alphafold --data_dir /db/AlphaFold/2.3.1 --output_dir ./output --model_names model_1 --fasta_paths ./query.fasta --max_template_date 2021-11-17
</pre>

Example of job submission
<pre class="gcommand">
sbatch sub.sh
</pre>

=== A method to accelerate your calculations ===

If you have many AlphaFold calculations, i.e., need to process many FASTA files, you can run them using an array job (see [[Array_Jobs|here]]).

At the same time you can greatly speed up your calculations by running two separate jobs using the CPU and GPU nodes sequentially:

1. Run the first step of AlphaFold (MSA generation) on the CPU nodes on the batch partition.

2. Then run the second part of AlphaFold (structural modeling) using the '''--use_precomputed_msas=true''' option on the GPU nodes on the gpu_p partition. If the computation in this part takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition (see [[GPU|here]]).

In order to achieve this, you need to find a way to stop all running element jobs (or elements) in step 1 after all element jobs in step 1 have completed the MSA generation.

With the help from a GACRC user, we have prepared a shell script called '''check_and_stop_elements.sh''':

1. This script will check for the presence of the "stop string", i.e., '''Running model model_1_multimer_v3_pred_0''', in the .err file of each running element you started in step 1.

2. You will run this script in an interactive session (see [[Running_Jobs_on_Sapelo2#How_to_open_an_interactive_session|here]]) or in an OOD X Desktop session (see [[OnDemand#X_Desktop_Session_.28A.K.A._The_Interactive_X_login_app.29|here]]) to monitor your elements during the time of elements running.

3. It will check your running elements with an interval of 5 minutes. If the "stop string" is found in a .err file of a running element, the script will automatically cancel the element for you, such that you can start step 2 as early as possible.

4. You can stop running the script (i.e. stop monitoring elements in step 1) by typing Ctrl + c keys on your keyboard.

As a quick demo, below is an example job submission script (sub_step1.sh) for running step 1 on batch:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphaMSAs
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --time=12:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

The above input.lst is a single-column text file storing the names of your FASTA files (please refer [[Array_Jobs#Non-Numbered_Input_Files|here]]), for example:
<pre class="gscript">
head -n 5 input.lst

PFago.v8prot.zeyw.fa
PFago.v8prot.zeyx.fa
PFago.v8prot.zeyy.fa
PFago.v8prot.zeyz.fa
PFago.v8prot.zeza.fa
</pre>

and those FASTA files are stored in a folder called inputs in your current job working folder (--fasta_paths=./inputs/$file).

Below is the shell script check_and_stop_elements.sh. You are welcome to copy it for your use:

<pre class="gscript">
#!/bin/bash

ArrayID=$1

while true; do
numOfRunning=$(squeue -l -j $ArrayID | grep RUNNING | wc -l)
echo
echo "Pending elements in array job $ArrayID:"
squeue -l -j $ArrayID | grep PENDING
echo
echo "Number of running elements in array job $ArrayID:"
echo "$numOfRunning"
echo
echo "Checking the stop string \"Running model model_1_multimer_v3_pred_0\" in alphaMSAs.*.err for each running element:"
echo

for j in $(squeue -l -j $ArrayID | grep RUNNING | awk '{print $1}')
do
jobid=$(scontrol show job $j | grep JobId= | awk '{print $1}' | sed -nr 's|JobId=()|\1|p')
echo -n "Checking alphaMSAs.${jobid}.err ... "
grep -m 1 "Running model model_1_multimer_v3_pred_0" alphaMSAs.${jobid}.err 2>&1 1>/dev/null
if [ $? -eq 0 ]; then
echo -en "\e[31mthe stop string is found!\e[0m ... go to cancel job ${jobid} ($j) ... scancel $j ... \e[32mjob canceled.\e[0m\n"
scancel $j
else
echo -e "the stop string is NOT found!"
fi
done
sleep 300
clear
done
</pre>

Before you can run it, please change its mode to executable by:

<pre class="gscript">
chmod 755 ./check_and_stop_elements.sh
</pre>

Then you can open an interactive session or an OOD X Desktop session to run this script to monitor your elements when they are running on compute nodes by:

<pre class="gscript">
./check_and_stop_elements.sh <ARRAY_JOB_ID>
</pre>

Please note. The above ARRAY_JOB_ID is the ID of your array job. For example, sq --me or sacct-gacrc -X reports you an JOBID 28026197_1, where 28026197 is the ID of your array job. So, the above command will be:

<pre class="gscript">
./check_and_stop_elements.sh 28026197
</pre>

Below is example outputs from running the above command line:

<pre class="gscript">
./check_and_stop_elements.sh 28026197

Pending elements in array job 28026197:

Number of running elements in array job 28026197:
6

Checking the stop string "Running model model_1_multimer_v3_pred_0" in alphaMSAs.*.err for each running element:

Checking alphaMSAs.28026197.err ... the stop string is found! ... go to cancel job 28026197 (28026197_10) ... scancel 28026197_10 ... job canceled.
Checking alphaMSAs.28026206.err ... the stop string is found! ... go to cancel job 28026206 (28026197_9) ... scancel 28026197_9 ... job canceled.
Checking alphaMSAs.28026205.err ... the stop string is NOT found!
Checking alphaMSAs.28026201.err ... the stop string is NOT found!
Checking alphaMSAs.28026200.err ... the stop string is NOT found!
Checking alphaMSAs.28026199.err ... the stop string is NOT found!
</pre>

Once all elements in step 1 have completed the MSA generation, you can go to step 2. As mentioned above, if the computation in step 2 takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition. Below is an example job submission script (sub_step2.sh) for running step 2 on the V100 scavenge GPU nodes:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --gres=gpu:V100:1
#SBATCH --time=4:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge

ml AAlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=8

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--use_precomputed_msas=true \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

Please note that we use '''--use_precomputed_msas=true''' option in the above alphafold command line.

=== Documentation ===

Details and references are at https://github.com/deepmind/alphafold.

'''Version 2.3.2:''' Dull help options
<pre class="gcommand">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold --helpfull

Full AlphaFold protein structure prediction script.
flags:

/apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1/bin/alphafold:
--[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins.
(default: 'false')
--bfd_database_path: Path to the BFD database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')
--data_dir: Path to directory of supporting data.
(default: '/db/AlphaFold/2.3.2')
--db_preset: <full_dbs|reduced_dbs>: Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs)
(default: 'full_dbs')
--fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique
basename as the basename is used to name the output directories for each prediction.
(a comma separated list)
--hhblits_binary_path: Path to the HHblits executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhblits')
--hhsearch_binary_path: Path to the HHsearch executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhsearch')
--hmmbuild_binary_path: Path to the hmmbuild executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmbuild')
--hmmsearch_binary_path: Path to the hmmsearch executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmsearch')
--jackhmmer_binary_path: Path to the JackHMMER executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/jackhmmer')
--kalign_binary_path: Path to the Kalign executable.
(default: '/apps/eb/Kalign/3.4.0-GCCcore-12.3.0/bin/kalign')
--max_template_date: Maximum template release date to consider. Important if folding historical test sets.
--mgnify_database_path: Path to the MGnify database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/mgnify/mgy_clusters_2022_05.fa')
--model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model
(default: 'monomer')
--models_to_relax: <all|best|none>: The models to run the final relaxation step on. If `all`, all models are relaxed, which may be time consuming. If `best`, only the most confident model is relaxed. If `none`, relaxation is not run. Turning off relaxation might result
in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage.
(default: 'best')
--num_multimer_predictions_per_model: How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer
(default: '5')
(an integer)
--obsolete_pdbs_path: Path to file containing a mapping from obsolete PDB IDs to the PDB IDs of their replacements.
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/obsolete.dat')
--output_dir: Path to a directory that will store the results.
--pdb70_database_path: Path to the PDB70 database for use by HHsearch.
(default: '/db/AlphaFold/2.3.2/pdb70/pdb70')
--pdb_seqres_database_path: Path to the PDB seqres database for use by hmmsearch.
--random_seed: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic.
(an integer)
--small_bfd_database_path: Path to the small version of BFD used with the "reduced_dbs" preset.
--template_mmcif_dir: Path to a directory with template mmCIF structures, each named <pdb_id>.cif
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/mmcif_files')
--uniprot_database_path: Path to the Uniprot database for use by JackHMMer.
--uniref30_database_path: Path to the UniRef30 database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/uniref30/UniRef30_2023_02')
--uniref90_database_path: Path to the Uniref90 database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/uniref90/uniref90.fasta')
--[no]use_gpu_relax: Whether to relax on GPU. Relax on GPU can be much faster than CPU, so it is recommended to enable if possible. GPUs must be available if this setting is enabled.
(default: 'true')
--[no]use_precomputed_msas: Whether to read MSAs that have been written to disk instead of running the MSA tools. The MSA files are looked up in the output directory, so it must stay the same between multiple runs that are to reuse the MSAs. WARNING: This will not check
if the sequence, database or configuration have changed.
(default: 'false')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

tensorflow.python.ops.parallel_for.pfor:
--[no]op_conversion_fallback_to_while_loop: DEPRECATED: Flag is ignored.
(default: 'true')

tensorflow.python.tpu.client.client:
--[no]hbm_oom_exit: Exit the script when the TPU HBM is OOM.
(default: 'true')
--[no]runtime_oom_exit: Exit the script when the TPU runtime is OOM.
(default: 'true')

tensorflow.python.tpu.tensor_tracer_flags:
--delta_threshold: Log if history based diff crosses this threshold.
(default: '0.5')
(a number)
--[no]tt_check_filter: Terminate early to check op name filtering.
(default: 'false')
--[no]tt_single_core_summaries: Report single core metric and avoid aggregation.
(default: 'false')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')

</pre>

[[#top|Back to Top]]

=== Installation ===

*Version 2.3.2: Installed using EasyBuild.

*The database files are installed in /db/AlphaFold/2.3.2/

=== System ===
64-bit Linux

AlphaFold-Sapelo2

2025-08-13T20:45:37Z

Jordan: /* Documentation */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
2.3.2

=== Author / Distributor ===
Please see https://github.com/deepmind/alphafold

=== Description ===
From https://github.com/deepmind/alphafold: "This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo2 please see the [[Lmod]] page. Note that as of the OS upgrade from Rocky 8 to Rocky 9 at the end of July, 2025, the previous Singularity containers for older versions of AlphaFold no longer work and are therefore no longer available to users.

*'''Version 2.3.2'''

Installed with EasyBuild in /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

To use this version of AlphaFold, please first load the module with
<pre class="gscript">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
</pre>

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1. The Python script run_alphafold.py is installed in EBROOTALPHAFOLD/bin and a symbolic link called alphafold points to it and can be used to run the program. The database files are in /db/AlphaFold/2.3.2 (note that /apps/db/ directories no longer exist). You can export the environment variable ALPHAFOLD_DATA_DIR to set the location of the database files. For bash, use
<pre class="gscript">
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
</pre>
However, when you load the <code>AlphaFold/2.3.1-foss-2022a-CUDA-11.7.0</code> module, this environment variable will be automatically set.

'''Note:''' If you run this program on the gpu_p partition, please request a P100, A100, or H100 GPU device. This version requires a GPU device and it should work on the P100, A100, and H100 devices.

'''Sample Job Submission scripts'''

Sample job submission script (sub.sh) to run AlphaFold 2.3.2 in a batch job (with GPU):

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafoldjobname
#SBATCH --partition=gpu_p
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --gres=gpu:A100:1 #You can specify other GPU devices, like 'H100', here
#SBATCH --mem=40gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold [options]

</pre>

where [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well.

An example of the options to use for the alphafold script:
<pre class="gscript">
alphafold --data_dir /apps/db/AlphaFold/2.3.1 --output_dir ./output --model_names model_1 --fasta_paths ./query.fasta --max_template_date 2021-11-17
</pre>

Example of job submission
<pre class="gcommand">
sbatch sub.sh
</pre>

=== A method to accelerate your calculations ===

If you have many AlphaFold calculations, i.e., need to process many FASTA files, you can run them using an array job (see [[Array_Jobs|here]]).

At the same time you can greatly speed up your calculations by running two separate jobs using the CPU and GPU nodes sequentially:

1. Run the first step of AlphaFold (MSA generation) on the CPU nodes on the batch partition.

2. Then run the second part of AlphaFold (structural modeling) using the '''--use_precomputed_msas=true''' option on the GPU nodes on the gpu_p partition. If the computation in this part takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition (see [[GPU|here]]).

In order to achieve this, you need to find a way to stop all running element jobs (or elements) in step 1 after all element jobs in step 1 have completed the MSA generation.

With the help from a GACRC user, we have prepared a shell script called '''check_and_stop_elements.sh''':

1. This script will check for the presence of the "stop string", i.e., '''Running model model_1_multimer_v3_pred_0''', in the .err file of each running element you started in step 1.

2. You will run this script in an interactive session (see [[Running_Jobs_on_Sapelo2#How_to_open_an_interactive_session|here]]) or in an OOD X Desktop session (see [[OnDemand#X_Desktop_Session_.28A.K.A._The_Interactive_X_login_app.29|here]]) to monitor your elements during the time of elements running.

3. It will check your running elements with an interval of 5 minutes. If the "stop string" is found in a .err file of a running element, the script will automatically cancel the element for you, such that you can start step 2 as early as possible.

4. You can stop running the script (i.e. stop monitoring elements in step 1) by typing Ctrl + c keys on your keyboard.

As a quick demo, below is an example job submission script (sub_step1.sh) for running step 1 on batch:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphaMSAs
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --time=12:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

The above input.lst is a single-column text file storing the names of your FASTA files (please refer [[Array_Jobs#Non-Numbered_Input_Files|here]]), for example:
<pre class="gscript">
head -n 5 input.lst

PFago.v8prot.zeyw.fa
PFago.v8prot.zeyx.fa
PFago.v8prot.zeyy.fa
PFago.v8prot.zeyz.fa
PFago.v8prot.zeza.fa
</pre>

and those FASTA files are stored in a folder called inputs in your current job working folder (--fasta_paths=./inputs/$file).

Below is the shell script check_and_stop_elements.sh. You are welcome to copy it for your use:

<pre class="gscript">
#!/bin/bash

ArrayID=$1

while true; do
numOfRunning=$(squeue -l -j $ArrayID | grep RUNNING | wc -l)
echo
echo "Pending elements in array job $ArrayID:"
squeue -l -j $ArrayID | grep PENDING
echo
echo "Number of running elements in array job $ArrayID:"
echo "$numOfRunning"
echo
echo "Checking the stop string \"Running model model_1_multimer_v3_pred_0\" in alphaMSAs.*.err for each running element:"
echo

for j in $(squeue -l -j $ArrayID | grep RUNNING | awk '{print $1}')
do
jobid=$(scontrol show job $j | grep JobId= | awk '{print $1}' | sed -nr 's|JobId=()|\1|p')
echo -n "Checking alphaMSAs.${jobid}.err ... "
grep -m 1 "Running model model_1_multimer_v3_pred_0" alphaMSAs.${jobid}.err 2>&1 1>/dev/null
if [ $? -eq 0 ]; then
echo -en "\e[31mthe stop string is found!\e[0m ... go to cancel job ${jobid} ($j) ... scancel $j ... \e[32mjob canceled.\e[0m\n"
scancel $j
else
echo -e "the stop string is NOT found!"
fi
done
sleep 300
clear
done
</pre>

Before you can run it, please change its mode to executable by:

<pre class="gscript">
chmod 755 ./check_and_stop_elements.sh
</pre>

Then you can open an interactive session or an OOD X Desktop session to run this script to monitor your elements when they are running on compute nodes by:

<pre class="gscript">
./check_and_stop_elements.sh <ARRAY_JOB_ID>
</pre>

Please note. The above ARRAY_JOB_ID is the ID of your array job. For example, sq --me or sacct-gacrc -X reports you an JOBID 28026197_1, where 28026197 is the ID of your array job. So, the above command will be:

<pre class="gscript">
./check_and_stop_elements.sh 28026197
</pre>

Below is example outputs from running the above command line:

<pre class="gscript">
./check_and_stop_elements.sh 28026197

Pending elements in array job 28026197:

Number of running elements in array job 28026197:
6

Checking the stop string "Running model model_1_multimer_v3_pred_0" in alphaMSAs.*.err for each running element:

Checking alphaMSAs.28026197.err ... the stop string is found! ... go to cancel job 28026197 (28026197_10) ... scancel 28026197_10 ... job canceled.
Checking alphaMSAs.28026206.err ... the stop string is found! ... go to cancel job 28026206 (28026197_9) ... scancel 28026197_9 ... job canceled.
Checking alphaMSAs.28026205.err ... the stop string is NOT found!
Checking alphaMSAs.28026201.err ... the stop string is NOT found!
Checking alphaMSAs.28026200.err ... the stop string is NOT found!
Checking alphaMSAs.28026199.err ... the stop string is NOT found!
</pre>

Once all elements in step 1 have completed the MSA generation, you can go to step 2. As mentioned above, if the computation in step 2 takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition. Below is an example job submission script (sub_step2.sh) for running step 2 on the V100 scavenge GPU nodes:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --gres=gpu:V100:1
#SBATCH --time=4:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge

ml AAlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=8

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--use_precomputed_msas=true \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

Please note that we use '''--use_precomputed_msas=true''' option in the above alphafold command line.

=== Documentation ===

Details and references are at https://github.com/deepmind/alphafold.

'''Version 2.3.2:''' Dull help options
<pre class="gcommand">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold --helpfull

Full AlphaFold protein structure prediction script.
flags:

/apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1/bin/alphafold:
--[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins.
(default: 'false')
--bfd_database_path: Path to the BFD database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')
--data_dir: Path to directory of supporting data.
(default: '/db/AlphaFold/2.3.2')
--db_preset: <full_dbs|reduced_dbs>: Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs)
(default: 'full_dbs')
--fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique
basename as the basename is used to name the output directories for each prediction.
(a comma separated list)
--hhblits_binary_path: Path to the HHblits executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhblits')
--hhsearch_binary_path: Path to the HHsearch executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhsearch')
--hmmbuild_binary_path: Path to the hmmbuild executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmbuild')
--hmmsearch_binary_path: Path to the hmmsearch executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmsearch')
--jackhmmer_binary_path: Path to the JackHMMER executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/jackhmmer')
--kalign_binary_path: Path to the Kalign executable.
(default: '/apps/eb/Kalign/3.4.0-GCCcore-12.3.0/bin/kalign')
--max_template_date: Maximum template release date to consider. Important if folding historical test sets.
--mgnify_database_path: Path to the MGnify database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/mgnify/mgy_clusters_2022_05.fa')
--model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model
(default: 'monomer')
--models_to_relax: <all|best|none>: The models to run the final relaxation step on. If `all`, all models are relaxed, which may be time consuming. If `best`, only the most confident model is relaxed. If `none`, relaxation is not run. Turning off relaxation might result
in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage.
(default: 'best')
--num_multimer_predictions_per_model: How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer
(default: '5')
(an integer)
--obsolete_pdbs_path: Path to file containing a mapping from obsolete PDB IDs to the PDB IDs of their replacements.
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/obsolete.dat')
--output_dir: Path to a directory that will store the results.
--pdb70_database_path: Path to the PDB70 database for use by HHsearch.
(default: '/db/AlphaFold/2.3.2/pdb70/pdb70')
--pdb_seqres_database_path: Path to the PDB seqres database for use by hmmsearch.
--random_seed: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic.
(an integer)
--small_bfd_database_path: Path to the small version of BFD used with the "reduced_dbs" preset.
--template_mmcif_dir: Path to a directory with template mmCIF structures, each named <pdb_id>.cif
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/mmcif_files')
--uniprot_database_path: Path to the Uniprot database for use by JackHMMer.
--uniref30_database_path: Path to the UniRef30 database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/uniref30/UniRef30_2023_02')
--uniref90_database_path: Path to the Uniref90 database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/uniref90/uniref90.fasta')
--[no]use_gpu_relax: Whether to relax on GPU. Relax on GPU can be much faster than CPU, so it is recommended to enable if possible. GPUs must be available if this setting is enabled.
(default: 'true')
--[no]use_precomputed_msas: Whether to read MSAs that have been written to disk instead of running the MSA tools. The MSA files are looked up in the output directory, so it must stay the same between multiple runs that are to reuse the MSAs. WARNING: This will not check
if the sequence, database or configuration have changed.
(default: 'false')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

tensorflow.python.ops.parallel_for.pfor:
--[no]op_conversion_fallback_to_while_loop: DEPRECATED: Flag is ignored.
(default: 'true')

tensorflow.python.tpu.client.client:
--[no]hbm_oom_exit: Exit the script when the TPU HBM is OOM.
(default: 'true')
--[no]runtime_oom_exit: Exit the script when the TPU runtime is OOM.
(default: 'true')

tensorflow.python.tpu.tensor_tracer_flags:
--delta_threshold: Log if history based diff crosses this threshold.
(default: '0.5')
(a number)
--[no]tt_check_filter: Terminate early to check op name filtering.
(default: 'false')
--[no]tt_single_core_summaries: Report single core metric and avoid aggregation.
(default: 'false')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')

</pre>

[[#top|Back to Top]]

=== Installation ===

*Version 2.3.2: Installed using EasyBuild.

*The database files are installed in /db/AlphaFold/2.3.2/

=== System ===
64-bit Linux

AlphaFold-Sapelo2

2025-08-13T20:45:01Z

Jordan: Overhauled the documentation after the July 2025 maintenance to reflect the fact that we have v2.3.2 installed centrally and nothing else for AF2.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
2.3.2

=== Author / Distributor ===
Please see https://github.com/deepmind/alphafold

=== Description ===
From https://github.com/deepmind/alphafold: "This package provides an implementation of the inference pipeline of AlphaFold v2.0. This is a completely new model that was entered in CASP14 and published in Nature. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo2 please see the [[Lmod]] page. Note that as of the OS upgrade from Rocky 8 to Rocky 9 at the end of July, 2025, the previous Singularity containers for older versions of AlphaFold no longer work and are therefore no longer available to users.

*'''Version 2.3.2'''

Installed with EasyBuild in /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

To use this version of AlphaFold, please first load the module with
<pre class="gscript">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
</pre>

Once you load the module, an environmental variable called EBROOTALPHAFOLD is exported. It stores the AlphaFold installation path on the cluster, i.e., /apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1. The Python script run_alphafold.py is installed in EBROOTALPHAFOLD/bin and a symbolic link called alphafold points to it and can be used to run the program. The database files are in /db/AlphaFold/2.3.2 (note that /apps/db/ directories no longer exist). You can export the environment variable ALPHAFOLD_DATA_DIR to set the location of the database files. For bash, use
<pre class="gscript">
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
</pre>
However, when you load the <code>AlphaFold/2.3.1-foss-2022a-CUDA-11.7.0</code> module, this environment variable will be automatically set.

'''Note:''' If you run this program on the gpu_p partition, please request a P100, A100, or H100 GPU device. This version requires a GPU device and it should work on the P100, A100, and H100 devices.

'''Sample Job Submission scripts'''

Sample job submission script (sub.sh) to run AlphaFold 2.3.2 in a batch job (with GPU):

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafoldjobname
#SBATCH --partition=gpu_p
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=10
#SBATCH --gres=gpu:A100:1 #You can specify other GPU devices, like 'H100', here
#SBATCH --mem=40gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold [options]

</pre>

where [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well.

An example of the options to use for the alphafold script:
<pre class="gscript">
alphafold --data_dir /apps/db/AlphaFold/2.3.1 --output_dir ./output --model_names model_1 --fasta_paths ./query.fasta --max_template_date 2021-11-17
</pre>

Example of job submission
<pre class="gcommand">
sbatch sub.sh
</pre>

=== A method to accelerate your calculations ===

If you have many AlphaFold calculations, i.e., need to process many FASTA files, you can run them using an array job (see [[Array_Jobs|here]]).

At the same time you can greatly speed up your calculations by running two separate jobs using the CPU and GPU nodes sequentially:

1. Run the first step of AlphaFold (MSA generation) on the CPU nodes on the batch partition.

2. Then run the second part of AlphaFold (structural modeling) using the '''--use_precomputed_msas=true''' option on the GPU nodes on the gpu_p partition. If the computation in this part takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition (see [[GPU|here]]).

In order to achieve this, you need to find a way to stop all running element jobs (or elements) in step 1 after all element jobs in step 1 have completed the MSA generation.

With the help from a GACRC user, we have prepared a shell script called '''check_and_stop_elements.sh''':

1. This script will check for the presence of the "stop string", i.e., '''Running model model_1_multimer_v3_pred_0''', in the .err file of each running element you started in step 1.

2. You will run this script in an interactive session (see [[Running_Jobs_on_Sapelo2#How_to_open_an_interactive_session|here]]) or in an OOD X Desktop session (see [[OnDemand#X_Desktop_Session_.28A.K.A._The_Interactive_X_login_app.29|here]]) to monitor your elements during the time of elements running.

3. It will check your running elements with an interval of 5 minutes. If the "stop string" is found in a .err file of a running element, the script will automatically cancel the element for you, such that you can start step 2 as early as possible.

4. You can stop running the script (i.e. stop monitoring elements in step 1) by typing Ctrl + c keys on your keyboard.

As a quick demo, below is an example job submission script (sub_step1.sh) for running step 1 on batch:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphaMSAs
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --time=12:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

The above input.lst is a single-column text file storing the names of your FASTA files (please refer [[Array_Jobs#Non-Numbered_Input_Files|here]]), for example:
<pre class="gscript">
head -n 5 input.lst

PFago.v8prot.zeyw.fa
PFago.v8prot.zeyx.fa
PFago.v8prot.zeyy.fa
PFago.v8prot.zeyz.fa
PFago.v8prot.zeza.fa
</pre>

and those FASTA files are stored in a folder called inputs in your current job working folder (--fasta_paths=./inputs/$file).

Below is the shell script check_and_stop_elements.sh. You are welcome to copy it for your use:

<pre class="gscript">
#!/bin/bash

ArrayID=$1

while true; do
numOfRunning=$(squeue -l -j $ArrayID | grep RUNNING | wc -l)
echo
echo "Pending elements in array job $ArrayID:"
squeue -l -j $ArrayID | grep PENDING
echo
echo "Number of running elements in array job $ArrayID:"
echo "$numOfRunning"
echo
echo "Checking the stop string \"Running model model_1_multimer_v3_pred_0\" in alphaMSAs.*.err for each running element:"
echo

for j in $(squeue -l -j $ArrayID | grep RUNNING | awk '{print $1}')
do
jobid=$(scontrol show job $j | grep JobId= | awk '{print $1}' | sed -nr 's|JobId=()|\1|p')
echo -n "Checking alphaMSAs.${jobid}.err ... "
grep -m 1 "Running model model_1_multimer_v3_pred_0" alphaMSAs.${jobid}.err 2>&1 1>/dev/null
if [ $? -eq 0 ]; then
echo -en "\e[31mthe stop string is found!\e[0m ... go to cancel job ${jobid} ($j) ... scancel $j ... \e[32mjob canceled.\e[0m\n"
scancel $j
else
echo -e "the stop string is NOT found!"
fi
done
sleep 300
clear
done
</pre>

Before you can run it, please change its mode to executable by:

<pre class="gscript">
chmod 755 ./check_and_stop_elements.sh
</pre>

Then you can open an interactive session or an OOD X Desktop session to run this script to monitor your elements when they are running on compute nodes by:

<pre class="gscript">
./check_and_stop_elements.sh <ARRAY_JOB_ID>
</pre>

Please note. The above ARRAY_JOB_ID is the ID of your array job. For example, sq --me or sacct-gacrc -X reports you an JOBID 28026197_1, where 28026197 is the ID of your array job. So, the above command will be:

<pre class="gscript">
./check_and_stop_elements.sh 28026197
</pre>

Below is example outputs from running the above command line:

<pre class="gscript">
./check_and_stop_elements.sh 28026197

Pending elements in array job 28026197:

Number of running elements in array job 28026197:
6

Checking the stop string "Running model model_1_multimer_v3_pred_0" in alphaMSAs.*.err for each running element:

Checking alphaMSAs.28026197.err ... the stop string is found! ... go to cancel job 28026197 (28026197_10) ... scancel 28026197_10 ... job canceled.
Checking alphaMSAs.28026206.err ... the stop string is found! ... go to cancel job 28026206 (28026197_9) ... scancel 28026197_9 ... job canceled.
Checking alphaMSAs.28026205.err ... the stop string is NOT found!
Checking alphaMSAs.28026201.err ... the stop string is NOT found!
Checking alphaMSAs.28026200.err ... the stop string is NOT found!
Checking alphaMSAs.28026199.err ... the stop string is NOT found!
</pre>

Once all elements in step 1 have completed the MSA generation, you can go to step 2. As mentioned above, if the computation in step 2 takes less than 4 hours, you can run the job using the V100 scavenge GPU nodes via batch partition. Below is an example job submission script (sub_step2.sh) for running step 2 on the V100 scavenge GPU nodes:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold
#SBATCH --partition=batch
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=8
#SBATCH --mem=32gb
#SBATCH --gres=gpu:V100:1
#SBATCH --time=4:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err
#SBATCH --array=1-200

cd $SLURM_SUBMIT_DIR

ml purge

ml AAlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
export ALPHAFOLD_DATA_DIR=/db/AlphaFold/2.3.2
export TF_FORCE_UNIFIED_MEMORY=1
export XLA_PYTHON_CLIENT_MEM_FRACTION=8

file=$(awk "NR==${SLURM_ARRAY_TASK_ID}" input.lst)

alphafold \
--run_relax=False \
--data_dir=$ALPHAFOLD_DATA_DIR \
--model_preset=multimer \
--use_precomputed_msas=true \
--num_multimer_predictions_per_model=1 \
--max_template_date=2023-10-01 \
--db_preset=full_dbs \
--output_dir=./outputs/$(basename $file .fa) \
--fasta_paths=./inputs/$file
</pre>

Please note that we use '''--use_precomputed_msas=true''' option in the above alphafold command line.

=== Documentation ===

Details and references are at https://github.com/deepmind/alphafold.

'''Version 2.3.2:''' Short help options
<pre class="gcommand">
ml AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1

alphafold --helpfull

Full AlphaFold protein structure prediction script.
flags:

/apps/eb/AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1/bin/alphafold:
--[no]benchmark: Run multiple JAX model evaluations to obtain a timing that excludes the compilation time, which should be more indicative of the time required for inferencing many proteins.
(default: 'false')
--bfd_database_path: Path to the BFD database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/bfd/bfd_metaclust_clu_complete_id30_c90_final_seq.sorted_opt')
--data_dir: Path to directory of supporting data.
(default: '/db/AlphaFold/2.3.2')
--db_preset: <full_dbs|reduced_dbs>: Choose preset MSA database configuration - smaller genetic database config (reduced_dbs) or full genetic database config (full_dbs)
(default: 'full_dbs')
--fasta_paths: Paths to FASTA files, each containing a prediction target that will be folded one after another. If a FASTA file contains multiple sequences, then it will be folded as a multimer. Paths should be separated by commas. All FASTA paths must have a unique
basename as the basename is used to name the output directories for each prediction.
(a comma separated list)
--hhblits_binary_path: Path to the HHblits executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhblits')
--hhsearch_binary_path: Path to the HHsearch executable.
(default: '/apps/eb/HH-suite/3.3.0-gompi-2023a/bin/hhsearch')
--hmmbuild_binary_path: Path to the hmmbuild executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmbuild')
--hmmsearch_binary_path: Path to the hmmsearch executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/hmmsearch')
--jackhmmer_binary_path: Path to the JackHMMER executable.
(default: '/apps/eb/HMMER/3.4-gompi-2023a/bin/jackhmmer')
--kalign_binary_path: Path to the Kalign executable.
(default: '/apps/eb/Kalign/3.4.0-GCCcore-12.3.0/bin/kalign')
--max_template_date: Maximum template release date to consider. Important if folding historical test sets.
--mgnify_database_path: Path to the MGnify database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/mgnify/mgy_clusters_2022_05.fa')
--model_preset: <monomer|monomer_casp14|monomer_ptm|multimer>: Choose preset model configuration - the monomer model, the monomer model with extra ensembling, monomer model with pTM head, or multimer model
(default: 'monomer')
--models_to_relax: <all|best|none>: The models to run the final relaxation step on. If `all`, all models are relaxed, which may be time consuming. If `best`, only the most confident model is relaxed. If `none`, relaxation is not run. Turning off relaxation might result
in predictions with distracting stereochemical violations but might help in case you are having issues with the relaxation stage.
(default: 'best')
--num_multimer_predictions_per_model: How many predictions (each with a different random seed) will be generated per model. E.g. if this is 2 and there are 5 models then there will be 10 predictions per input. Note: this FLAG only applies if model_preset=multimer
(default: '5')
(an integer)
--obsolete_pdbs_path: Path to file containing a mapping from obsolete PDB IDs to the PDB IDs of their replacements.
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/obsolete.dat')
--output_dir: Path to a directory that will store the results.
--pdb70_database_path: Path to the PDB70 database for use by HHsearch.
(default: '/db/AlphaFold/2.3.2/pdb70/pdb70')
--pdb_seqres_database_path: Path to the PDB seqres database for use by hmmsearch.
--random_seed: The random seed for the data pipeline. By default, this is randomly generated. Note that even if this is set, Alphafold may still not be deterministic, because processes like GPU inference are nondeterministic.
(an integer)
--small_bfd_database_path: Path to the small version of BFD used with the "reduced_dbs" preset.
--template_mmcif_dir: Path to a directory with template mmCIF structures, each named <pdb_id>.cif
(default: '/db/AlphaFold/2.3.2/pdb_mmcif/mmcif_files')
--uniprot_database_path: Path to the Uniprot database for use by JackHMMer.
--uniref30_database_path: Path to the UniRef30 database for use by HHblits.
(default: '/db/AlphaFold/2.3.2/uniref30/UniRef30_2023_02')
--uniref90_database_path: Path to the Uniref90 database for use by JackHMMER.
(default: '/db/AlphaFold/2.3.2/uniref90/uniref90.fasta')
--[no]use_gpu_relax: Whether to relax on GPU. Relax on GPU can be much faster than CPU, so it is recommended to enable if possible. GPUs must be available if this setting is enabled.
(default: 'true')
--[no]use_precomputed_msas: Whether to read MSAs that have been written to disk instead of running the MSA tools. The MSA files are looked up in the output directory, so it must stay the same between multiple runs that are to reuse the MSAs. WARNING: This will not check
if the sequence, database or configuration have changed.
(default: 'false')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

tensorflow.python.ops.parallel_for.pfor:
--[no]op_conversion_fallback_to_while_loop: DEPRECATED: Flag is ignored.
(default: 'true')

tensorflow.python.tpu.client.client:
--[no]hbm_oom_exit: Exit the script when the TPU HBM is OOM.
(default: 'true')
--[no]runtime_oom_exit: Exit the script when the TPU runtime is OOM.
(default: 'true')

tensorflow.python.tpu.tensor_tracer_flags:
--delta_threshold: Log if history based diff crosses this threshold.
(default: '0.5')
(a number)
--[no]tt_check_filter: Terminate early to check op name filtering.
(default: 'false')
--[no]tt_single_core_summaries: Report single core metric and avoid aggregation.
(default: 'false')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')

</pre>

[[#top|Back to Top]]

=== Installation ===

*Version 2.3.2: Installed using EasyBuild.

*The database files are installed in /db/AlphaFold/2.3.2/

=== System ===
64-bit Linux

Software installed on Rocky 9

2025-07-08T18:48:41Z

Jordan: /* List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance) */

As part of our July 29-31,2025 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from Rocky 8 to Rocky 9.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 9 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (Rocky 8) will continue to be available after the maintenance.

===List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance)===

ABySS/2.3.7-foss-2023a
ADMIXTURE/1.3.0
AFNI/25.1.01-foss-2024a
AGAT/1.4.0-GCC-12.3.0
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prodigal/2.6.3-GCCcore-11.3.0
prodigal/2.6.3-GCCcore-12.3.0
prokka/1.14.5-gompi-2023a
protobuf-python/3.19.4-GCCcore-11.3.0
protobuf-python/4.24.0-GCCcore-12.3.0
protobuf-python/4.25.3-GCCcore-13.2.0
protobuf/3.19.4-GCCcore-11.3.0
protobuf/24.0-GCCcore-12.3.0
protobuf/25.3-GCCcore-13.2.0
psutil/5.9.8-GCCcore-12.3.0
psutil/6.0.0-GCCcore-13.3.0
psutil/6.1.0-GCCcore-13.2.0
psycopg/3.1.18-GCCcore-13.2.0
psycopg/3.2.3-GCCcore-13.3.0
psycopg2/2.9.9-GCCcore-12.3.0
pugixml/1.14-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-13.2.0
py-cpuinfo/9.0.0-GCCcore-13.3.0
pyBigWig/0.3.22-gfbf-2023a
pybedtools/0.9.1-foss-2023a
pybind11/2.9.2-GCCcore-11.3.0
pybind11/2.11.1-GCCcore-12.3.0
pybind11/2.11.1-GCCcore-13.2.0
pybind11/2.12.0-GCC-13.3.0
pydantic/1.10.4-GCCcore-11.3.0
pydantic/2.5.3-GCCcore-12.3.0
pydantic/2.6.4-GCCcore-13.2.0
pydantic/2.9.1-GCCcore-13.3.0
pydicom/2.3.0-GCCcore-11.3.0
pydicom/2.4.4-GCCcore-12.3.0
pydicom/3.0.1-GCCcore-13.2.0
pydot/2.0.0-GCCcore-12.3.0
pydot/3.0.3-GCCcore-13.3.0
pyfaidx/0.8.1.1-GCCcore-12.3.0
pyfaidx/0.8.1.2-GCCcore-13.3.0
pyproj/3.4.0-GCCcore-11.3.0
pyproj/3.6.0-GCCcore-12.3.0
pyproj/3.7.0-GCCcore-13.3.0
pyspoa/0.2.1-GCC-12.3.0
pyspoa/0.2.1-GCC-13.2.0
pytest-flakefinder/1.1.0-GCCcore-12.3.0
pytest-flakefinder/1.1.0-GCCcore-13.2.0
pytest-rerunfailures/12.0-GCCcore-12.3.0
pytest-rerunfailures/14.0-GCCcore-13.2.0
pytest-shard/0.1.2-GCCcore-12.3.0
pytest-shard/0.1.2-GCCcore-13.2.0
pytest-xdist/3.3.1-GCCcore-12.3.0
pytest/4.6.11-GCCcore-12.3.0-Python-2.7.18
pytest/7.4.2-GCCcore-12.3.0
pytest/7.4.2-GCCcore-13.2.0
pytest/8.3.3-GCCcore-13.3.0
python-isal/1.1.0-GCCcore-12.3.0
python-isal/1.7.0-GCCcore-13.3.0
python-parasail/1.3.4-foss-2023a
python-xxhash/3.4.1-GCCcore-12.3.0
rasterio/1.4.3-foss-2024a
re2c/2.2-GCCcore-11.3.0
re2c/3.1-GCCcore-12.3.0
re2c/3.1-GCCcore-13.2.0
re2c/3.1-GCCcore-13.3.0
redis-py/4.5.1-foss-2022a
redis-py/5.0.1-GCCcore-12.3.0
redis-py/5.0.9-GCCcore-13.2.0
redis-py/5.1.1-GCCcore-13.3.0
rjags/4-15-foss-2023a-R-4.3.2
ruamel.yaml/0.17.32-GCCcore-12.3.0
ruamel.yaml/0.18.6-GCCcore-13.2.0
ruamel.yaml/0.18.6-GCCcore-13.3.0
samclip/0.4.0-GCCcore-13.3.0
scanpy/1.9.8-foss-2023a
schrodinger/2025-1
scikit-build-core/0.5.0-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-13.2.0
scikit-build-core/0.10.6-GCCcore-13.3.0
scikit-build/0.17.6-GCCcore-12.3.0
scikit-build/0.17.6-GCCcore-13.2.0
scikit-build/0.17.6-GCCcore-13.3.0
scikit-image/0.19.3-foss-2022a
scikit-image/0.22.0-foss-2023a
scikit-image/0.25.0-foss-2024a
scikit-learn/1.1.2-foss-2022a
scikit-learn/1.3.1-gfbf-2023a
scikit-learn/1.3.2-gfbf-2023b
scikit-learn/1.4.0-gfbf-2023b
scikit-learn/1.4.2-gfbf-2023a
scikit-learn/1.5.2-gfbf-2024a
scikit-optimize/0.10.2-foss-2023a
seqtk/1.4-GCC-13.3.0
setuptools-rust/1.6.0-GCCcore-12.3.0
setuptools-rust/1.8.0-GCCcore-13.2.0
setuptools-rust/1.9.0-GCCcore-13.3.0
smithwaterman/20160702-GCCcore-12.3.0
smithwaterman/20160702-GCCcore-13.3.0
snakemake/8.27.0-foss-2024a
snappy/1.1.9-GCCcore-11.3.0
snappy/1.1.10-GCCcore-12.3.0
snappy/1.1.10-GCCcore-13.2.0
snappy/1.2.1-GCCcore-13.3.0
snpEff/5.2c-GCCcore-12.3.0-Java-11
spaln/2.4.13f-GCC-12.3.0
spaln/3.0.6b-GCC-12.3.0
sparsehash/2.0.4-GCCcore-12.3.0
spglib-python/2.0.0-foss-2022a
spglib-python/2.5.0-gfbf-2024a
spglib/2.5.0-GCCcore-12.3.0
spoa/4.1.0-GCC-12.3.0
spoa/4.1.0-GCC-13.2.0
statsmodels/0.14.1-gfbf-2023a
statsmodels/0.14.4-gfbf-2024a
swalign/0.2-GCCcore-13.3.0-Python-2.7.18
sympy/1.12-gfbf-2023a
sympy/1.12-gfbf-2023b
sympy/1.13.3-gfbf-2024a
tRNAscan-SE/2.0.12-foss-2023a
tabix/0.2.6-GCCcore-12.3.0
tabix/0.2.6-GCCcore-13.3.0
tabixpp/1.1.2-GCC-12.3.0
tabixpp/1.1.2-GCC-13.3.0
tantan/50-GCC-12.3.0
tbb/2021.5.0-GCCcore-11.3.0
tbb/2021.11.0-GCCcore-12.3.0
tbb/2021.13.0-GCCcore-13.2.0
tbl2asn/20230713-GCCcore-12.3.0
tcsh/6.24.01-GCCcore-11.3.0
tcsh/6.24.10-GCCcore-12.3.0
tcsh/6.24.13-GCCcore-13.3.0
tensorboard/2.15.1-gfbf-2023a
tensorboard/2.18.0-gfbf-2023b
tensorboardX/2.6.2.2-foss-2023a
tensorstore/0.1.65-foss-2023a
tesseract/5.5.0-GCCcore-13.3.0
texlive/20230313-GCC-12.3.0
tiktoken/0.9.0-GCCcore-13.3.0
time/1.9-GCCcore-11.3.0
time/1.9-GCCcore-13.3.0
tmux/3.4-GCCcore-13.3.0
tokenizers/0.15.2-GCCcore-12.3.0
tokenizers/0.19.1-GCCcore-13.2.0
torchvision/0.16.0-foss-2023a-CUDA-12.1.1
tornado/6.3.2-GCCcore-12.3.0
tornado/6.4-GCCcore-13.2.0
tornado/6.4.1-GCCcore-13.3.0
tqdm/4.64.0-GCCcore-11.3.0
tqdm/4.66.1-GCCcore-12.3.0
tqdm/4.66.2-GCCcore-13.2.0
tqdm/4.66.5-GCCcore-13.3.0
trimAl/1.4.1-GCCcore-12.3.0
trimAl/1.5.0-GCCcore-13.3.0
typing-extensions/4.3.0-GCCcore-11.3.0
typing-extensions/4.9.0-GCCcore-12.3.0
typing-extensions/4.10.0-GCCcore-13.2.0
typing-extensions/4.11.0-GCCcore-13.3.0
ucsc/443
umap-learn/0.5.5-foss-2023a
unifdef/2.12-GCCcore-12.3.0
unimap/0.1-GCCcore-11.3.0
utf8proc/2.8.0-GCCcore-12.3.0
utf8proc/2.9.0-GCCcore-13.2.0
utf8proc/2.9.0-GCCcore-13.3.0
util-linux/2.38-GCCcore-11.3.0
util-linux/2.39-GCCcore-12.3.0
util-linux/2.39-GCCcore-13.2.0
util-linux/2.40-GCCcore-13.3.0
vcflib/1.0.9-gfbf-2023a-R-4.3.2
vcflib/1.0.9-gfbf-2024a-R-4.4.2
versioningit/3.1.2-GCCcore-13.3.0
vg/1.65.0
virtualenv/20.23.1-GCCcore-12.3.0
virtualenv/20.24.6-GCCcore-13.2.0
virtualenv/20.26.2-GCCcore-13.3.0
wandb/0.16.1-GCC-12.3.0
wget/1.21.3-GCCcore-11.3.0
wget/1.24.5-GCCcore-12.3.0
wpebackend-fdo/1.15.90-GCCcore-12.3.0
wrapt/1.16.0-gfbf-2024a
wxPython/4.2.1-foss-2023a
wxWidgets/3.2.2.1-GCC-12.3.0
x264/20220620-GCCcore-11.3.0
x264/20230226-GCCcore-12.3.0
x264/20231019-GCCcore-13.2.0
x264/20240513-GCCcore-13.3.0
x265/3.5-GCCcore-11.3.0
x265/3.5-GCCcore-12.3.0
x265/3.5-GCCcore-13.2.0
x265/3.6-GCCcore-13.3.0
xarray/2024.11.0-gfbf-2024a
xorg-macros/1.19.3-GCCcore-11.3.0
xorg-macros/1.20.0-GCCcore-12.3.0
xorg-macros/1.20.0-GCCcore-13.2.0
xorg-macros/1.20.1-GCCcore-13.3.0
xprop/1.2.8-GCCcore-13.3.0
xproto/7.0.31-GCCcore-11.3.0
xproto/7.0.31-GCCcore-13.3.0
xxHash/0.8.2-GCCcore-12.3.0
xxd/9.0.2112-GCCcore-12.3.0
xxd/9.1.0307-GCCcore-13.2.0
xxd/9.1.1275-GCCcore-13.3.0
zarr/2.18.4-foss-2024a
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12
zlib/1.2.13-GCCcore-12.3.0
zlib/1.2.13-GCCcore-13.2.0
zlib/1.2.13
zlib/1.3.1-GCCcore-13.3.0
zlib/1.3.1
zstd/1.5.2-GCCcore-11.3.0
zstd/1.5.5-GCCcore-12.3.0
zstd/1.5.5-GCCcore-13.2.0
zstd/1.5.6-GCCcore-13.3.0

Software installed on Rocky 9

2025-07-08T18:32:53Z

Jordan: /* List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance) */

As part of our July 29-31,2025 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from Rocky 8 to Rocky 9.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 9 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (Rocky 8) will continue to be available after the maintenance.

===List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance)===

ABySS/2.3.7-foss-2023a
ADMIXTURE/1.3.0
AFNI/25.1.01-foss-2024a
AGAT/1.4.0-GCC-12.3.0
AMOS/3.1.0-foss-2023a
ANIcalculator/1.0-GCCcore-11.3.0
ANSYS/2025R1
ANTLR/2.7.7-GCCcore-12.3.0-Java-11
ANTLR/2.7.7-GCCcore-13.3.0-Java-17
APR-util/1.6.3-GCCcore-13.3.0
APR/1.7.4-GCCcore-13.3.0
ASE/3.22.1-foss-2022a
ASE/3.23.0-gfbf-2024a
ASTRAL/5.7.8-Java-1.8.0_241
ATK/2.38.0-GCCcore-11.3.0
ATK/2.38.0-GCCcore-12.3.0
ATK/2.38.0-GCCcore-13.2.0
AUGUSTUS/3.5.0-foss-2022a
AUGUSTUS/3.5.0-foss-2023a
AUGUSTUS/3.5.0-foss-2024a
AUGUSTUS/3.5.0-20240612-foss-2023a
Abseil/20230125.3-GCCcore-12.3.0
Abseil/20240116.1-GCCcore-13.2.0
Abseil/20240722.0-GCCcore-13.3.0
Albumentations/1.3.0-foss-2022a
Albumentations/1.4.0-foss-2023a
AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
AlphaPulldown/2.0.3-foss-2023a-CUDA-12.1.1
Amber/22.4-foss-2022a-AmberTools-22.5-CUDA-11.7.0
Amber/24.3-foss-2022a-AmberTools-24.10-CUDA-12.1.1
Ancestry_HMM/1.0.2-foss-2024a
Archive-Zip/1.68-GCCcore-12.3.0
Archive-Zip/1.68-GCCcore-13.3.0
Armadillo/11.4.3-foss-2022a
Armadillo/12.6.2-foss-2023a
Armadillo/12.8.0-foss-2023b
Armadillo/14.0.3-foss-2024a
Arrow/14.0.1-gfbf-2023a
Arrow/16.1.0-gfbf-2023b
Arrow/17.0.0-gfbf-2024a
Autoconf/2.71-GCCcore-11.3.0
Autoconf/2.71-GCCcore-12.3.0
Autoconf/2.71-GCCcore-13.2.0
Autoconf/2.71
Autoconf/2.72-GCCcore-13.3.0
Automake/1.16.5-GCCcore-11.3.0
Automake/1.16.5-GCCcore-12.3.0
Automake/1.16.5-GCCcore-13.2.0
Automake/1.16.5-GCCcore-13.3.0
Automake/1.16.5
Autotools/20220317-GCCcore-11.3.0
Autotools/20220317-GCCcore-12.3.0
Autotools/20220317-GCCcore-13.2.0
Autotools/20220317
Autotools/20231222-GCCcore-13.3.0
BBMap/39.01-GCC-11.3.0
BBMap/39.01-GCC-12.3.0
BBMap/39.19-GCC-13.3.0
BCFtools/1.15.1-GCC-11.3.0
BCFtools/1.18-GCC-12.3.0
BCFtools/1.21-GCC-13.3.0
BEDOPS/2.4.41-foss-2023a
BEDTools/2.31.0-GCC-12.3.0
BEDTools/2.31.1-GCC-13.3.0
BLAST+/2.13.0-gompi-2022a
BLAST+/2.14.1-gompi-2023a
BLAST+/2.16.0-gompi-2024a
BLAT/3.7-GCC-11.3.0
BLAT/3.7-GCC-12.3.0
BLIS/0.9.0-GCC-11.3.0
BLIS/0.9.0-GCC-12.3.0
BLIS/0.9.0-GCC-13.2.0
BLIS/1.0-GCC-13.3.0
BRAKER/3.0.8-foss-2023a
BUSCO/5.8.3-foss-2023a
BWA/0.7.17-GCCcore-11.3.0
BWA/0.7.17-GCCcore-12.3.0
BWA/0.7.18-GCCcore-13.2.0
BWA/0.7.18-GCCcore-13.3.0
BamTools/2.5.2-GCC-11.3.0
BamTools/2.5.2-GCC-12.3.0
BamTools/2.5.2-GCC-13.3.0
Bandage/0.9.0-GCCcore-12.3.0
Bazel/6.1.0-GCCcore-12.3.0
Bazel/6.3.1-GCCcore-12.3.0
Beagle/5.4.22Jul22.46e-Java-11
Beast/2.7.7-GCC-12.3.0-CUDA-12.1.1
BeautifulSoup/4.12.2-GCCcore-12.3.0
BeautifulSoup/4.12.2-GCCcore-13.2.0
BeautifulSoup/4.12.3-GCCcore-13.3.0
Bio-DB-HTS/3.01-GCC-12.3.0
Bio-DB-HTS/3.01-GCC-13.3.0
Bio-SearchIO-hmmer/1.7.3-GCC-12.3.0
BioPerl/1.7.2-GCCcore-13.3.0
BioPerl/1.7.8-GCCcore-11.3.0
BioPerl/1.7.8-GCCcore-12.3.0
BioPerl/1.7.8-GCCcore-13.2.0
BioPerl/1.7.8-GCCcore-13.3.0
Biopython/1.79-foss-2022a
Biopython/1.83-foss-2023a
Biopython/1.84-foss-2023b
Biopython/1.84-foss-2024a
Bismark/0.24.2-GCC-12.3.0
Bison/3.8.2-GCCcore-11.3.0
Bison/3.8.2-GCCcore-12.3.0
Bison/3.8.2-GCCcore-13.2.0
Bison/3.8.2-GCCcore-13.3.0
Bison/3.8.2
Blosc/1.21.5-GCCcore-12.3.0
Blosc/1.21.6-GCCcore-13.3.0
Blosc2/2.17.0-GCCcore-13.3.0
Boltz/2.1.1
Boost.MPI/1.79.0-gompi-2022a
Boost.MPI/1.82.0-gompi-2023a
Boost.Python/1.82.0-GCC-12.3.0
Boost.Python/1.85.0-GCC-13.3.0
Boost/1.75.0-GCC-12.3.0
Boost/1.79.0-GCC-11.3.0
Boost/1.82.0-GCC-12.3.0
Boost/1.83.0-GCC-13.2.0
Boost/1.85.0-GCC-13.3.0
Bowtie/1.3.1-GCC-11.3.0
Bowtie/1.3.1-GCC-12.3.0
Bowtie2/2.4.5-GCC-11.3.0
Bowtie2/2.5.1-GCC-12.3.0
Bowtie2/2.5.2-GCC-11.3.0
Bracken/3.1-GCCcore-12.3.0
BrainNetViewer/20191031
Brotli-python/1.1.0-GCCcore-13.3.0
Brotli/1.0.9-GCCcore-11.3.0
Brotli/1.0.9-GCCcore-12.3.0
Brotli/1.1.0-GCCcore-13.2.0
Brotli/1.1.0-GCCcore-13.3.0
Brunsli/0.1-GCCcore-12.3.0
Brunsli/0.1-GCCcore-13.2.0
Brunsli/0.1-GCCcore-13.3.0
CD-HIT/4.8.1-GCC-11.3.0
CD-HIT/4.8.1-GCC-12.3.0
CDBtools/0.99-GCC-12.3.0
CDO/2.4.4-gompi-2024a
CFITSIO/4.3.0-GCCcore-12.3.0
CFITSIO/4.3.1-GCCcore-13.2.0
CFITSIO/4.4.1-GCCcore-13.3.0
CGAL/5.6.1-GCCcore-13.3.0
CMSeq/1.0.4-foss-2024a
CMake/3.18.4
CMake/3.23.1-GCCcore-11.3.0
CMake/3.24.3-GCCcore-11.3.0
CMake/3.26.3-GCCcore-12.3.0
CMake/3.27.6-GCCcore-13.2.0
CMake/3.29.3-GCCcore-13.3.0
CUDA/11.7.0
CUDA/12.1.1
CUDA/12.4.0
CUDA/12.6.0
CUnit/2.1-3-GCCcore-12.3.0
CVXPY/1.4.2-foss-2023a
CapnProto/0.10.2-GCCcore-11.3.0
CapnProto/1.0.1.1-GCCcore-13.2.0
CapnProto/1.1.0-GCCcore-13.3.0
Cartopy/0.22.0-foss-2023a
Catch2/2.13.9-GCCcore-12.3.0
Catch2/2.13.9-GCCcore-13.2.0
Catch2/2.13.10-GCCcore-13.3.0
Cbc/2.10.12-foss-2024a
Cereal/1.3.0
Cereal/1.3.2
Cgl/0.60.8-foss-2024a
CharLS/2.4.2-GCCcore-13.3.0
CheMPS2/1.8.12-foss-2023a
CheMPS2/1.8.12-foss-2023b
CheMPS2/1.8.12-foss-2024a
Check/0.15.2-GCCcore-13.3.0
CheckM-Database/2015_01_16
CheckM/1.2.2-foss-2022a
Circlator/1.5.5-foss-2023a
Clang/13.0.1-GCCcore-11.3.0
Clang/16.0.6-GCCcore-12.3.0-CUDA-12.1.1
Clang/16.0.6-GCCcore-12.3.0
Clang/18.1.8-GCCcore-13.3.0-CUDA-12.6.0
Clang/18.1.8-GCCcore-13.3.0
Clarabel.rs/0.7.1-gfbf-2023a
Clp/1.17.10-foss-2024a
Clustal-Omega/1.2.4-GCC-12.3.0
ClustalW2/2.1-GCC-12.3.0
CodingQuarry/2.0-foss-2023a
CoinUtils/2.11.12-GCC-13.3.0
Compress-Raw-Zlib/2.213-GCCcore-12.3.0
Compress-Raw-Zlib/2.213-GCCcore-13.3.0
Consed/29.0-foss-2022a
CoordgenLibs/3.0.2-gompi-2023a
CoverM/0.4.0
CppUnit/1.15.1-GCCcore-12.3.0
Cufflinks/20190706-GCC-12.3.0
Cython/0.29.37-GCCcore-13.3.0-Python-2.7.18
Cython/3.0.7-GCCcore-12.3.0
Cython/3.0.8-GCCcore-12.3.0
Cython/3.0.10-GCCcore-13.2.0
Cython/3.0.10-GCCcore-13.3.0
DB/18.1.40-GCCcore-11.3.0
DB/18.1.40-GCCcore-12.3.0
DB/18.1.40-GCCcore-13.2.0
DB/18.1.40-GCCcore-13.3.0
DBD-mysql/4.050-GCC-12.3.0
DB_File/1.858-GCCcore-11.3.0
DB_File/1.859-GCCcore-12.3.0
DB_File/1.859-GCCcore-13.2.0
DB_File/1.859-GCCcore-13.3.0
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GMP/6.2.1-GCCcore-11.3.0
GMP/6.2.1-GCCcore-12.3.0
GMP/6.3.0-GCCcore-13.2.0
GMP/6.3.0-GCCcore-13.3.0
GOATOOLS/1.4.5-foss-2023a
GOATOOLS/1.4.12-foss-2024a
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GROMACS/2024.4-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2
GSL/2.7-GCC-11.3.0
GSL/2.7-GCC-12.3.0
GSL/2.7-GCC-13.2.0
GSL/2.8-GCC-13.3.0
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GStreamer/1.20.2-GCC-11.3.0
GStreamer/1.22.5-GCC-12.3.0
GStreamer/1.24.8-GCC-13.3.0
GTDB-Tk/2.3.2-foss-2022a
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GTK3/3.24.37-GCCcore-12.3.0
GTK3/3.24.39-GCCcore-13.2.0
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GTS/0.7.6-GCCcore-13.3.0
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GeneMark-ET/4.72-GCCcore-12.3.0
GeneMark-ETP/1.0.0-GCC-12.3.0
Genome/05012020-foss-2022a
GenomeThreader/1.7.3-Linux_x86_64-64bit
GenomeTools/1.6.2-GCC-11.3.0
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Ghostscript/9.56.1-GCCcore-11.3.0
Ghostscript/10.01.2-GCCcore-12.3.0
Ghostscript/10.02.1-GCCcore-13.2.0
Ghostscript/10.03.1-GCCcore-13.3.0
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Giza/1.4.1-GCCcore-13.2.0
GlimmerHMM/3.0.4c-GCC-12.3.0
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GlobalArrays/5.8.2-intel-2024a
GnuTLS/3.7.8-GCCcore-12.3.0
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Graphene/1.10.8-GCCcore-11.3.0
Graphene/1.10.8-GCCcore-12.3.0
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HDF/4.2.16-2-GCCcore-12.3.0
HDF/4.2.16-2-GCCcore-13.2.0
HDF/4.3.0-GCCcore-13.3.0
HDF5/1.12.2-gompi-2022a
HDF5/1.14.0-gompi-2023a
HDF5/1.14.0-iimpi-2023a
HDF5/1.14.3-gompi-2023b
HDF5/1.14.5-gompi-2024a
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HISAT2/2.2.1-gompi-2022a
HISAT2/2.2.1-gompi-2023a
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HMMER/3.4-gompi-2023a
HMMER/3.4-gompi-2023b
HMMER/3.4-gompi-2024a
HTSeq/2.0.7-foss-2023a
HTSlib/1.15.1-GCC-11.3.0
HTSlib/1.18-GCC-12.3.0
HTSlib/1.19.1-GCC-13.2.0
HTSlib/1.21-GCC-13.3.0
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ICU/73.2-GCCcore-12.3.0
ICU/74.1-GCCcore-13.2.0
ICU/75.1-GCCcore-13.3.0
IDBA-UD/1.1.3-GCCcore-12.3.0
IGV/2.19.1-Java-21
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IPython/8.27.0-GCCcore-13.3.0
IPython/8.28.0-GCCcore-13.3.0
IQ-TREE/2.3.6-gompi-2023a
IRkernel/1.3.2-gfbf-2023a-R-4.3.2
ISA-L/2.30.0-GCCcore-12.3.0
ISA-L/2.31.0-GCCcore-13.2.0
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ImageMagick/7.1.1-15-GCCcore-12.3.0
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Imath/3.1.7-GCCcore-12.3.0
Imath/3.1.9-GCCcore-13.2.0
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Infernal/1.1.5-gompi-2023a
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IsoQuant/3.5.0-foss-2023a
IsoQuant/3.7.0-foss-2023a
IsoSeq/4.3.0
JAGS/4.3.2-foss-2023a
JAGS/4.3.2-foss-2024a
Jansson/2.14-GCC-12.3.0
JasPer/2.0.33-GCCcore-11.3.0
JasPer/4.0.0-GCCcore-12.3.0
JasPer/4.0.0-GCCcore-13.2.0
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Java/1.8.0_311 (1.8)
Java/11.0.20 (11)
Java/17.0.6 (17)
Java/21.0.2
Java/21.0.5 (D:21)
Jellyfish/2.3.0-GCC-11.3.0
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JsonCpp/1.9.5-GCCcore-13.3.0
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Judy/1.0.5-GCCcore-12.3.0
Judy/1.0.5-GCCcore-13.3.0
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Juicer/1.6-foss-2022a-CUDA-11.7.0-juicebox-1.9.9
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Kent_tools/442-GCC-11.3.0
Kent_tools/468-GCC-12.3.0
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LAME/3.100-GCCcore-11.3.0
LAME/3.100-GCCcore-12.3.0
LAME/3.100-GCCcore-13.2.0
LAME/3.100-GCCcore-13.3.0
LAMMPS/29Aug2024-foss-2023b-kokkos
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LDC/1.24.0-x86_64
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LLVM/18.1.8-GCCcore-13.3.0-minimal
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LMDB/0.9.31-GCCcore-13.2.0
LMDB/0.9.31-GCCcore-13.3.0
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LZO/2.10-GCCcore-11.3.0
LZO/2.10-GCCcore-12.3.0
LZO/2.10-GCCcore-13.3.0
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LittleCMS/2.15-GCCcore-12.3.0
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M4/1.4.18
M4/1.4.19-GCCcore-11.3.0
M4/1.4.19-GCCcore-12.3.0
M4/1.4.19-GCCcore-13.2.0
M4/1.4.19-GCCcore-13.3.0
M4/1.4.19
MACS2/2.2.9.1-foss-2023a
MACS3/3.0.1-gfbf-2023a
MAFFT/7.505-GCC-11.3.0-with-extensions
MAFFT/7.520-GCC-12.3.0-with-extensions
MAFFT/7.526-GCC-13.2.0-with-extensions
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MCL/22.282-GCCcore-12.3.0
MCR/R2018a
MCR/R2023a
MCR/R2023b.9
MDI/1.4.29-gompi-2023b
MDTraj/1.9.7-foss-2022a
MDTraj/1.9.9-gfbf-2023a
MEGA/10.0.5
MEGA/11.0.13
MEGAHIT/1.2.9-GCCcore-13.3.0
MEME/5.5.7-gompi-2023b
METABOLIC/4.0-foss-2022a
METIS/5.1.0-GCCcore-11.3.0
METIS/5.1.0-GCCcore-12.3.0
METIS/5.1.0-GCCcore-13.2.0
METIS/5.1.0-GCCcore-13.3.0
MMseqs2/14-7e284-gompi-2022a
MMseqs2/14-7e284-gompi-2023a
MPC/1.3.1-GCCcore-12.3.0
MPC/1.3.1-GCCcore-13.2.0
MPC/1.3.1-GCCcore-13.3.0
MPFR/4.1.0-GCCcore-11.3.0
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MPFR/4.2.1-GCCcore-13.2.0
MPFR/4.2.1-GCCcore-13.3.0
MSTmap/20240122-GCC-12.3.0
MUMPS/5.6.1-foss-2023a-metis
MUMPS/5.7.2-foss-2024a-metis
MUMmer/4.0.0rc1-GCCcore-11.3.0
MUMmer/4.0.0rc1-GCCcore-12.3.0
MUSCLE/5.1.0-GCCcore-12.3.0
Mako/1.2.0-GCCcore-11.3.0
Mako/1.2.4-GCCcore-12.3.0
Mako/1.2.4-GCCcore-13.2.0
Mako/1.3.5-GCCcore-13.3.0
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MariaDB/10.9.3-GCC-11.3.0
MariaDB/11.6.0-GCC-12.3.0
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Mash/2.3-GCC-13.2.0
Mash/2.3-GCC-13.3.0
Maven/3.6.3
Maven/3.9.7
Medusa/1.6-foss-2023a-Python-3.11.3-Java-11
Mesa/22.0.3-GCCcore-11.3.0
Mesa/23.1.4-GCCcore-12.3.0
Mesa/23.1.9-GCCcore-13.2.0
Mesa/24.1.3-GCCcore-13.3.0
Meson/0.58.2-GCCcore-12.3.0
Meson/0.62.1-GCCcore-11.3.0
Meson/0.64.0-GCCcore-12.3.0
Meson/1.1.1-GCCcore-12.3.0
Meson/1.2.3-GCCcore-13.2.0
Meson/1.3.1-GCCcore-12.3.0
Meson/1.4.0-GCCcore-13.3.0
MetaBAT/2.17-GCC-13.3.0
MetaEuk/6-GCC-11.3.0
MetaEuk/6-GCC-12.3.0
Micromamba/2.3.0
Miniconda3/23.5.2-0
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MitoFinder/1.4.2-foss-2023a-Python-2.7.18
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MrBayes/3.2.7-gompi-2023a
MultiQC/1.28-foss-2024a
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NASM/2.16.01-GCCcore-12.3.0
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NASM/2.16.03-GCCcore-13.3.0
NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0
NCCL/2.18.3-GCCcore-11.3.0-CUDA-12.1.1
NCCL/2.18.3-GCCcore-12.3.0-CUDA-12.1.1
NCCL/2.20.5-GCCcore-13.2.0-CUDA-12.4.0
NCCL/2.22.3-GCCcore-13.3.0-CUDA-12.6.0
NCO/5.1.9-foss-2023a
NGS/3.0.1-GCCcore-12.3.0-Java-11
NLTK/3.8.1-foss-2023a
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NLopt/2.7.1-GCCcore-12.3.0
NLopt/2.7.1-GCCcore-13.2.0
NLopt/2.7.1-GCCcore-13.3.0
NSPR/4.34-GCCcore-11.3.0
NSPR/4.35-GCCcore-12.3.0
NSPR/4.35-GCCcore-13.2.0
NSPR/4.35-GCCcore-13.3.0
NSS/3.79-GCCcore-11.3.0
NSS/3.89.1-GCCcore-12.3.0
NSS/3.94-GCCcore-13.2.0
NSS/3.104-GCCcore-13.3.0
NanoPlot/1.43.0-foss-2023a
NextDenovo/2.5.2-20240510-GCCcore-12.3.0
Nextflow/24.10.2
Nextflow/25.04.2
Nextflow/25.04.4
NiBabel/4.0.2-foss-2022a
NiBabel/5.2.0-gfbf-2023a
NiBabel/5.3.2-gfbf-2023b
Ninja/1.10.2-GCCcore-11.3.0
Ninja/1.10.2-GCCcore-12.3.0
Ninja/1.11.1-GCCcore-12.3.0
Ninja/1.11.1-GCCcore-13.2.0
Ninja/1.12.1-GCCcore-13.3.0
ONNX/1.15.0-gfbf-2023a
ONNX/1.17.0-gfbf-2023b
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xproto/7.0.31-GCCcore-11.3.0
xproto/7.0.31-GCCcore-13.3.0
xxHash/0.8.2-GCCcore-12.3.0
xxd/9.0.2112-GCCcore-12.3.0
xxd/9.1.0307-GCCcore-13.2.0
xxd/9.1.1275-GCCcore-13.3.0
zarr/2.18.4-foss-2024a
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12
zlib/1.2.13-GCCcore-12.3.0
zlib/1.2.13-GCCcore-13.2.0
zlib/1.2.13
zlib/1.3.1-GCCcore-13.3.0
zlib/1.3.1
zstd/1.5.2-GCCcore-11.3.0
zstd/1.5.5-GCCcore-12.3.0
zstd/1.5.5-GCCcore-13.2.0
zstd/1.5.6-GCCcore-13.3.0

Software installed on Rocky 9

2025-07-03T18:57:14Z

Jordan: /* List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance) */

As part of our July 29-31,2025 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from Rocky 8 to Rocky 9.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 9 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (Rocky 8) will continue to be available after the maintenance.

===List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance)===

ABySS/2.3.7-foss-2023a
ADMIXTURE/1.3.0
AFNI/25.1.01-foss-2024a
AGAT/1.4.0-GCC-12.3.0
AMOS/3.1.0-foss-2023a
ANIcalculator/1.0-GCCcore-11.3.0
ANSYS/2025R1
ANTLR/2.7.7-GCCcore-12.3.0-Java-11
ANTLR/2.7.7-GCCcore-13.3.0-Java-17
APR-util/1.6.3-GCCcore-13.3.0
APR/1.7.4-GCCcore-13.3.0
ASE/3.22.1-foss-2022a
ASE/3.23.0-gfbf-2024a
ASTRAL/5.7.8-Java-1.8.0_241
ATK/2.38.0-GCCcore-11.3.0
ATK/2.38.0-GCCcore-12.3.0
ATK/2.38.0-GCCcore-13.2.0
AUGUSTUS/3.5.0-foss-2022a
AUGUSTUS/3.5.0-foss-2023a
AUGUSTUS/3.5.0-foss-2024a
AUGUSTUS/3.5.0-20240612-foss-2023a
Abseil/20230125.3-GCCcore-12.3.0
Abseil/20240116.1-GCCcore-13.2.0
Abseil/20240722.0-GCCcore-13.3.0
Albumentations/1.3.0-foss-2022a
Albumentations/1.4.0-foss-2023a
AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
AlphaPulldown/2.0.3-foss-2023a-CUDA-12.1.1
Amber/22.4-foss-2022a-AmberTools-22.5-CUDA-11.7.0
Amber/24.3-foss-2022a-AmberTools-24.10-CUDA-12.1.1
Ancestry_HMM/1.0.2-foss-2024a
Archive-Zip/1.68-GCCcore-12.3.0
Archive-Zip/1.68-GCCcore-13.3.0
Armadillo/11.4.3-foss-2022a
Armadillo/12.6.2-foss-2023a
Armadillo/12.8.0-foss-2023b
Armadillo/14.0.3-foss-2024a
Arrow/14.0.1-gfbf-2023a
Arrow/16.1.0-gfbf-2023b
Arrow/17.0.0-gfbf-2024a
Autoconf/2.71-GCCcore-11.3.0
Autoconf/2.71-GCCcore-12.3.0
Autoconf/2.71-GCCcore-13.2.0
Autoconf/2.71
Autoconf/2.72-GCCcore-13.3.0
Automake/1.16.5-GCCcore-11.3.0
Automake/1.16.5-GCCcore-12.3.0
Automake/1.16.5-GCCcore-13.2.0
Automake/1.16.5-GCCcore-13.3.0
Automake/1.16.5
Autotools/20220317-GCCcore-11.3.0
Autotools/20220317-GCCcore-12.3.0
Autotools/20220317-GCCcore-13.2.0
Autotools/20220317
Autotools/20231222-GCCcore-13.3.0
BBMap/39.01-GCC-11.3.0
BBMap/39.01-GCC-12.3.0
BBMap/39.19-GCC-13.3.0
BCFtools/1.15.1-GCC-11.3.0
BCFtools/1.18-GCC-12.3.0
BCFtools/1.21-GCC-13.3.0
BEDOPS/2.4.41-foss-2023a
BEDTools/2.31.0-GCC-12.3.0
BEDTools/2.31.1-GCC-13.3.0
BLAST+/2.13.0-gompi-2022a
BLAST+/2.14.1-gompi-2023a
BLAST+/2.16.0-gompi-2024a
BLAT/3.7-GCC-11.3.0
BLAT/3.7-GCC-12.3.0
BLIS/0.9.0-GCC-11.3.0
BLIS/0.9.0-GCC-12.3.0
BLIS/0.9.0-GCC-13.2.0
BLIS/1.0-GCC-13.3.0
BRAKER/3.0.8-foss-2023a
BUSCO/5.8.3-foss-2023a
BWA/0.7.17-GCCcore-11.3.0
BWA/0.7.17-GCCcore-12.3.0
BWA/0.7.18-GCCcore-13.2.0
BWA/0.7.18-GCCcore-13.3.0
BamTools/2.5.2-GCC-11.3.0
BamTools/2.5.2-GCC-12.3.0
BamTools/2.5.2-GCC-13.3.0
Bandage/0.9.0-GCCcore-12.3.0
Bazel/6.1.0-GCCcore-12.3.0
Bazel/6.3.1-GCCcore-12.3.0
Beagle/5.4.22Jul22.46e-Java-11
Beast/2.7.7-GCC-12.3.0-CUDA-12.1.1
BeautifulSoup/4.12.2-GCCcore-12.3.0
BeautifulSoup/4.12.2-GCCcore-13.2.0
BeautifulSoup/4.12.3-GCCcore-13.3.0
Bio-DB-HTS/3.01-GCC-12.3.0
Bio-DB-HTS/3.01-GCC-13.3.0
Bio-SearchIO-hmmer/1.7.3-GCC-12.3.0
BioPerl/1.7.2-GCCcore-13.3.0
BioPerl/1.7.8-GCCcore-11.3.0
BioPerl/1.7.8-GCCcore-12.3.0
BioPerl/1.7.8-GCCcore-13.2.0
BioPerl/1.7.8-GCCcore-13.3.0
Biopython/1.79-foss-2022a
Biopython/1.83-foss-2023a
Biopython/1.84-foss-2023b
Biopython/1.84-foss-2024a
Bismark/0.24.2-GCC-12.3.0
Bison/3.8.2-GCCcore-11.3.0
Bison/3.8.2-GCCcore-12.3.0
Bison/3.8.2-GCCcore-13.2.0
Bison/3.8.2-GCCcore-13.3.0
Bison/3.8.2
Blosc/1.21.5-GCCcore-12.3.0
Blosc/1.21.6-GCCcore-13.3.0
Blosc2/2.17.0-GCCcore-13.3.0
Boltz/2.1.1
Boost.MPI/1.79.0-gompi-2022a
Boost.MPI/1.82.0-gompi-2023a
Boost.Python/1.82.0-GCC-12.3.0
Boost.Python/1.85.0-GCC-13.3.0
Boost/1.75.0-GCC-12.3.0
Boost/1.79.0-GCC-11.3.0
Boost/1.82.0-GCC-12.3.0
Boost/1.83.0-GCC-13.2.0
Boost/1.85.0-GCC-13.3.0
Bowtie/1.3.1-GCC-11.3.0
Bowtie/1.3.1-GCC-12.3.0
Bowtie2/2.4.5-GCC-11.3.0
Bowtie2/2.5.1-GCC-12.3.0
Bowtie2/2.5.2-GCC-11.3.0
Bracken/3.1-GCCcore-12.3.0
BrainNetViewer/20191031
Brotli-python/1.1.0-GCCcore-13.3.0
Brotli/1.0.9-GCCcore-11.3.0
Brotli/1.0.9-GCCcore-12.3.0
Brotli/1.1.0-GCCcore-13.2.0
Brotli/1.1.0-GCCcore-13.3.0
Brunsli/0.1-GCCcore-12.3.0
Brunsli/0.1-GCCcore-13.2.0
Brunsli/0.1-GCCcore-13.3.0
CD-HIT/4.8.1-GCC-11.3.0
CD-HIT/4.8.1-GCC-12.3.0
CDBtools/0.99-GCC-12.3.0
CDO/2.4.4-gompi-2024a
CFITSIO/4.3.0-GCCcore-12.3.0
CFITSIO/4.3.1-GCCcore-13.2.0
CFITSIO/4.4.1-GCCcore-13.3.0
CGAL/5.6.1-GCCcore-13.3.0
CMSeq/1.0.4-foss-2024a
CMake/3.18.4
CMake/3.23.1-GCCcore-11.3.0
CMake/3.24.3-GCCcore-11.3.0
CMake/3.26.3-GCCcore-12.3.0
CMake/3.27.6-GCCcore-13.2.0
CMake/3.29.3-GCCcore-13.3.0
CUDA/11.7.0
CUDA/12.1.1
CUDA/12.4.0
CUDA/12.6.0
CUnit/2.1-3-GCCcore-12.3.0
CVXPY/1.4.2-foss-2023a
CapnProto/0.10.2-GCCcore-11.3.0
CapnProto/1.0.1.1-GCCcore-13.2.0
CapnProto/1.1.0-GCCcore-13.3.0
Cartopy/0.22.0-foss-2023a
Catch2/2.13.9-GCCcore-12.3.0
Catch2/2.13.9-GCCcore-13.2.0
Catch2/2.13.10-GCCcore-13.3.0
Cbc/2.10.12-foss-2024a
Cereal/1.3.0
Cereal/1.3.2
Cgl/0.60.8-foss-2024a
CharLS/2.4.2-GCCcore-13.3.0
CheMPS2/1.8.12-foss-2023a
CheMPS2/1.8.12-foss-2023b
CheMPS2/1.8.12-foss-2024a
Check/0.15.2-GCCcore-13.3.0
CheckM-Database/2015_01_16
CheckM/1.2.2-foss-2022a
Circlator/1.5.5-foss-2023a
Clang/13.0.1-GCCcore-11.3.0
Clang/16.0.6-GCCcore-12.3.0-CUDA-12.1.1
Clang/16.0.6-GCCcore-12.3.0
Clang/18.1.8-GCCcore-13.3.0-CUDA-12.6.0
Clang/18.1.8-GCCcore-13.3.0
Clarabel.rs/0.7.1-gfbf-2023a
Clp/1.17.10-foss-2024a
Clustal-Omega/1.2.4-GCC-12.3.0
ClustalW2/2.1-GCC-12.3.0
CodingQuarry/2.0-foss-2023a
CoinUtils/2.11.12-GCC-13.3.0
Compress-Raw-Zlib/2.213-GCCcore-12.3.0
Compress-Raw-Zlib/2.213-GCCcore-13.3.0
Consed/29.0-foss-2022a
CoordgenLibs/3.0.2-gompi-2023a
CoverM/0.4.0
CppUnit/1.15.1-GCCcore-12.3.0
Cufflinks/20190706-GCC-12.3.0
Cython/0.29.37-GCCcore-13.3.0-Python-2.7.18
Cython/3.0.7-GCCcore-12.3.0
Cython/3.0.8-GCCcore-12.3.0
Cython/3.0.10-GCCcore-13.2.0
Cython/3.0.10-GCCcore-13.3.0
DB/18.1.40-GCCcore-11.3.0
DB/18.1.40-GCCcore-12.3.0
DB/18.1.40-GCCcore-13.2.0
DB/18.1.40-GCCcore-13.3.0
DBD-mysql/4.050-GCC-12.3.0
DB_File/1.858-GCCcore-11.3.0
DB_File/1.859-GCCcore-12.3.0
DB_File/1.859-GCCcore-13.2.0
DB_File/1.859-GCCcore-13.3.0
DBus/1.14.0-GCCcore-11.3.0
DBus/1.15.4-GCCcore-12.3.0
DBus/1.15.8-GCCcore-13.2.0
DBus/1.15.8-GCCcore-13.3.0
DIAMOND/2.1.0-GCC-11.3.0
DIAMOND/2.1.8-GCC-12.3.0
DIAMOND/2.1.9-GCC-13.2.0
DIAMOND/2.1.11-GCC-13.3.0
Delly/1.3.3-GCC-13.3.0
DendroPy/4.5.2-GCCcore-11.3.0
DendroPy/4.6.1-GCCcore-12.3.0
Doxygen/1.9.4-GCCcore-11.3.0
Doxygen/1.9.7-GCCcore-12.3.0
Doxygen/1.9.8-GCCcore-13.2.0
Doxygen/1.11.0-GCCcore-13.3.0
ELPA/2024.05.001-foss-2024a
EMBOSS/6.6.0-foss-2023a
ESMF/8.6.0-foss-2023a
ETE/3.1.3-foss-2023a
EVidenceModeler/2.1.0-foss-2023a
EasyBuild/4.7.2
EasyBuild/4.8.0
EasyBuild/4.9.4
Eigen/3.4.0-GCCcore-11.3.0
Eigen/3.4.0-GCCcore-12.3.0
Eigen/3.4.0-GCCcore-13.2.0
Eigen/3.4.0-GCCcore-13.3.0
Exonerate/2.4.0-GCC-12.3.0
Exonerate/2.4.0-GCC-13.3.0
FASTA/36.3.8i-GCC-12.3.0
FASTX-Toolkit/0.0.14-GCC-11.3.0
FFTW.MPI/3.3.10-gompi-2022a
FFTW.MPI/3.3.10-gompi-2023a
FFTW.MPI/3.3.10-gompi-2023b
FFTW.MPI/3.3.10-gompi-2024a
FFTW/3.3.10-GCC-11.3.0
FFTW/3.3.10-GCC-12.3.0
FFTW/3.3.10-GCC-13.2.0
FFTW/3.3.10-GCC-13.3.0
FFmpeg/4.4.2-GCCcore-11.3.0
FFmpeg/6.0-GCCcore-12.3.0
FFmpeg/6.0-GCCcore-13.2.0
FFmpeg/7.0.2-GCCcore-13.3.0
FLAC/1.3.4-GCCcore-11.3.0
FLAC/1.4.2-GCCcore-12.3.0
FLAC/1.4.3-GCCcore-13.2.0
FLAC/1.4.3-GCCcore-13.3.0
FLASH/2.2.00-GCCcore-13.3.0
FastANI/1.33-GCC-11.3.0
FastANI/1.34-GCC-13.2.0
FastK/1.1.0-GCC-12.3.0
FastME/2.1.6.3-GCC-12.3.0
FastQC/0.11.9-Java-11
FastQC/0.12.1-Java-11
FastTree/2.1.11-GCCcore-11.3.0
FastTree/2.1.11-GCCcore-12.3.0
FastTree/2.1.11-GCCcore-13.2.0
Fastaq/3.17.0-GCC-12.3.0
Filtlong/0.2.1-GCC-12.3.0
Fiona/1.8.21-foss-2022a
Fiona/1.9.5-foss-2023a
Fiona/1.10.1-foss-2024a
Flask/2.2.2-GCCcore-11.3.0
Flask/2.3.3-GCCcore-12.3.0
Flask/3.0.3-GCCcore-13.3.0
FlexiBLAS/3.2.0-GCC-11.3.0
FlexiBLAS/3.3.1-GCC-12.3.0
FlexiBLAS/3.3.1-GCC-13.2.0
FlexiBLAS/3.4.4-GCC-13.3.0
Flye/2.9.5-GCC-13.3.0
FreeSurfer/7.4.1-GCCcore-12.3.0
FreeXL/2.0.0-GCCcore-12.3.0
FriBidi/1.0.12-GCCcore-11.3.0
FriBidi/1.0.12-GCCcore-12.3.0
FriBidi/1.0.13-GCCcore-13.2.0
FriBidi/1.0.15-GCCcore-13.3.0
GATK/4.3.0.0-GCCcore-11.3.0-Java-11
GATK/4.3.0.0-GCCcore-12.3.0-Java-11
GATK/4.4.0.0-GCCcore-12.3.0-Java-17
GATK/4.6.0.0-GCCcore-13.2.0-Java-17
GCC/11.3.0
GCC/12.3.0
GCC/13.2.0
GCC/13.3.0
GCCcore/11.3.0
GCCcore/12.3.0
GCCcore/13.2.0
GCCcore/13.3.0
GDAL/3.5.0-foss-2022a
GDAL/3.7.1-foss-2023a
GDAL/3.9.0-foss-2023b
GDAL/3.10.0-foss-2024a
GDGraph/1.56-GCCcore-11.3.0
GDRCopy/2.3-GCCcore-11.3.0
GDRCopy/2.3.1-GCCcore-12.3.0
GDRCopy/2.4-GCCcore-13.2.0
GDRCopy/2.4.1-GCCcore-13.3.0
GEM/1.5.1-foss-2023a
GEMMA/0.98.5-gfbf-2023b
GEOS/3.10.3-GCC-11.3.0
GEOS/3.12.0-GCC-12.3.0
GEOS/3.12.1-GCC-13.2.0
GEOS/3.12.2-GCC-13.3.0
GLM/1.0.1-GCCcore-13.3.0
GLPK/5.0-GCCcore-11.3.0
GLPK/5.0-GCCcore-12.3.0
GLPK/5.0-GCCcore-13.2.0
GLPK/5.0-GCCcore-13.3.0
GLib/2.72.1-GCCcore-11.3.0
GLib/2.77.1-GCCcore-12.3.0
GLib/2.78.1-GCCcore-13.2.0
GLib/2.80.4-GCCcore-13.3.0
GLibmm/2.77.0-GCCcore-12.3.0
GMAP-GSNAP/2023-02-17-GCC-11.3.0
GMAP-GSNAP/2023-04-20-GCC-12.3.0
GMAP-GSNAP/2024-09-18-GCC-13.3.0
GMP/6.2.1-GCCcore-11.3.0
GMP/6.2.1-GCCcore-12.3.0
GMP/6.3.0-GCCcore-13.2.0
GMP/6.3.0-GCCcore-13.3.0
GOATOOLS/1.4.5-foss-2023a
GOATOOLS/1.4.12-foss-2024a
GObject-Introspection/1.72.0-GCCcore-11.3.0
GObject-Introspection/1.76.1-GCCcore-12.3.0
GObject-Introspection/1.78.1-GCCcore-13.2.0
GObject-Introspection/1.80.1-GCCcore-13.3.0
GRASS/8.4.0-foss-2023a
GROMACS/2024.4-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2
GSL/2.7-GCC-11.3.0
GSL/2.7-GCC-12.3.0
GSL/2.7-GCC-13.2.0
GSL/2.8-GCC-13.3.0
GST-plugins-bad/1.20.2-GCC-11.3.0
GST-plugins-bad/1.22.5-GCC-12.3.0
GST-plugins-base/1.20.2-GCC-11.3.0
GST-plugins-base/1.22.5-GCC-12.3.0
GStreamer/1.20.2-GCC-11.3.0
GStreamer/1.22.5-GCC-12.3.0
GStreamer/1.24.8-GCC-13.3.0
GTDB-Tk/2.3.2-foss-2022a
GTK2/2.24.33-GCCcore-11.3.0
GTK3/3.24.37-GCCcore-12.3.0
GTK3/3.24.39-GCCcore-13.2.0
GTK4/4.7.0-GCC-11.3.0
GTK4/4.13.1-GCC-12.3.0
GTS/0.7.6-GCCcore-12.3.0
GTS/0.7.6-GCCcore-13.3.0
Gdk-Pixbuf/2.42.8-GCCcore-11.3.0
Gdk-Pixbuf/2.42.10-GCCcore-12.3.0
Gdk-Pixbuf/2.42.10-GCCcore-13.2.0
Gdk-Pixbuf/2.42.11-GCCcore-13.3.0
GeneMark-ET/4.72-GCCcore-12.3.0
GeneMark-ETP/1.0.0-GCC-12.3.0
Genome/05012020-foss-2022a
GenomeThreader/1.7.3-Linux_x86_64-64bit
GenomeTools/1.6.2-GCC-11.3.0
GetOrganelle/1.7.7.1-foss-2023a
Ghostscript/9.56.1-GCCcore-11.3.0
Ghostscript/10.01.2-GCCcore-12.3.0
Ghostscript/10.02.1-GCCcore-13.2.0
Ghostscript/10.03.1-GCCcore-13.3.0
GitPython/3.1.40-GCCcore-12.3.0
GitPython/3.1.43-GCCcore-13.3.0
Giza/1.4.1-GCCcore-13.2.0
GlimmerHMM/3.0.4c-GCC-12.3.0
GlobalArrays/5.8.2-intel-2023a
GlobalArrays/5.8.2-intel-2024a
GnuTLS/3.7.8-GCCcore-12.3.0
Go/1.22.1
Graphene/1.10.8-GCCcore-11.3.0
Graphene/1.10.8-GCCcore-12.3.0
Graphviz/8.1.0-GCCcore-12.3.0
Graphviz/12.2.0-GCCcore-13.3.0
Greenlet/2.0.2-GCCcore-11.3.0
Greenlet/3.0.3-GCCcore-13.2.0
Greenlet/3.1.1-GCCcore-13.3.0
Guile/3.0.9-GCCcore-12.3.0
Gurobi/11.0.2-GCCcore-12.3.0
HDF/4.2.15-GCCcore-11.3.0
HDF/4.2.16-2-GCCcore-12.3.0
HDF/4.2.16-2-GCCcore-13.2.0
HDF/4.3.0-GCCcore-13.3.0
HDF5/1.12.2-gompi-2022a
HDF5/1.14.0-gompi-2023a
HDF5/1.14.0-iimpi-2023a
HDF5/1.14.3-gompi-2023b
HDF5/1.14.5-gompi-2024a
HH-suite/3.3.0-gompi-2023a
HISAT2/2.2.1-gompi-2022a
HISAT2/2.2.1-gompi-2023a
HMMER/3.3.2-gompi-2022a
HMMER/3.4-gompi-2023a
HMMER/3.4-gompi-2023b
HMMER/3.4-gompi-2024a
HTSeq/2.0.7-foss-2023a
HTSlib/1.15.1-GCC-11.3.0
HTSlib/1.18-GCC-12.3.0
HTSlib/1.19.1-GCC-13.2.0
HTSlib/1.21-GCC-13.3.0
HarfBuzz/4.2.1-GCCcore-11.3.0
HarfBuzz/5.3.1-GCCcore-12.3.0
HarfBuzz/8.2.2-GCCcore-13.2.0
HarfBuzz/9.0.0-GCCcore-13.3.0
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picard/3.3.0-Java-17
pigz/2.8-GCCcore-12.3.0
pigz/2.8-GCCcore-13.3.0
pixman/0.40.0-GCCcore-11.3.0
pixman/0.42.2-GCCcore-12.3.0
pixman/0.42.2-GCCcore-13.2.0
pixman/0.43.4-GCCcore-13.3.0
pkg-config/0.29.2-GCCcore-11.3.0
pkg-config/0.29.2-GCCcore-12.3.0
pkgconf/1.8.0-GCCcore-11.3.0
pkgconf/1.8.0
pkgconf/1.9.5-GCCcore-12.3.0
pkgconf/2.0.3-GCCcore-13.2.0
pkgconf/2.2.0-GCCcore-13.3.0
pkgconfig/1.5.5-GCCcore-11.3.0-python
pkgconfig/1.5.5-GCCcore-12.3.0-python
plotly.py/5.12.0-GCCcore-11.3.0
plotly.py/5.16.0-GCCcore-12.3.0
plotly.py/5.18.0-GCCcore-13.2.0
plotly.py/5.24.1-GCCcore-13.3.0
pocl/1.8-GCC-11.3.0
pocl/4.0-GCC-12.3.0-CUDA-12.1.1
pocl/4.0-GCC-12.3.0
poetry/1.5.1-GCCcore-12.3.0
poetry/1.6.1-GCCcore-13.2.0
poetry/1.7.1-GCCcore-12.3.0
poetry/1.8.3-GCCcore-13.3.0
poppler/23.09.0-GCC-12.3.0
pplacer/1.1.alpha19
pretty-yaml/24.7.0-GCCcore-12.3.0
prodigal/2.6.3-GCCcore-11.3.0
prodigal/2.6.3-GCCcore-12.3.0
prokka/1.14.5-gompi-2023a
protobuf-python/3.19.4-GCCcore-11.3.0
protobuf-python/4.24.0-GCCcore-12.3.0
protobuf-python/4.25.3-GCCcore-13.2.0
protobuf/3.19.4-GCCcore-11.3.0
protobuf/24.0-GCCcore-12.3.0
protobuf/25.3-GCCcore-13.2.0
psutil/5.9.8-GCCcore-12.3.0
psutil/6.0.0-GCCcore-13.3.0
psutil/6.1.0-GCCcore-13.2.0
psycopg/3.1.18-GCCcore-13.2.0
psycopg/3.2.3-GCCcore-13.3.0
psycopg2/2.9.9-GCCcore-12.3.0
pugixml/1.14-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-12.3.0
py-cpuinfo/9.0.0-GCCcore-13.2.0
py-cpuinfo/9.0.0-GCCcore-13.3.0
pyBigWig/0.3.22-gfbf-2023a
pybedtools/0.9.1-foss-2023a
pybind11/2.9.2-GCCcore-11.3.0
pybind11/2.11.1-GCCcore-12.3.0
pybind11/2.11.1-GCCcore-13.2.0
pybind11/2.12.0-GCC-13.3.0
pydantic/1.10.4-GCCcore-11.3.0
pydantic/2.5.3-GCCcore-12.3.0
pydantic/2.6.4-GCCcore-13.2.0
pydantic/2.9.1-GCCcore-13.3.0
pydicom/2.3.0-GCCcore-11.3.0
pydicom/2.4.4-GCCcore-12.3.0
pydicom/3.0.1-GCCcore-13.2.0
pydot/2.0.0-GCCcore-12.3.0
pydot/3.0.3-GCCcore-13.3.0
pyfaidx/0.8.1.1-GCCcore-12.3.0
pyfaidx/0.8.1.2-GCCcore-13.3.0
pyproj/3.4.0-GCCcore-11.3.0
pyproj/3.6.0-GCCcore-12.3.0
pyproj/3.7.0-GCCcore-13.3.0
pyspoa/0.2.1-GCC-12.3.0
pyspoa/0.2.1-GCC-13.2.0
pytest-flakefinder/1.1.0-GCCcore-12.3.0
pytest-flakefinder/1.1.0-GCCcore-13.2.0
pytest-rerunfailures/12.0-GCCcore-12.3.0
pytest-rerunfailures/14.0-GCCcore-13.2.0
pytest-shard/0.1.2-GCCcore-12.3.0
pytest-shard/0.1.2-GCCcore-13.2.0
pytest-xdist/3.3.1-GCCcore-12.3.0
pytest/4.6.11-GCCcore-12.3.0-Python-2.7.18
pytest/7.4.2-GCCcore-12.3.0
pytest/7.4.2-GCCcore-13.2.0
pytest/8.3.3-GCCcore-13.3.0
python-isal/1.1.0-GCCcore-12.3.0
python-isal/1.7.0-GCCcore-13.3.0
python-parasail/1.3.4-foss-2023a
python-xxhash/3.4.1-GCCcore-12.3.0
rasterio/1.4.3-foss-2024a
re2c/2.2-GCCcore-11.3.0
re2c/3.1-GCCcore-12.3.0
re2c/3.1-GCCcore-13.2.0
re2c/3.1-GCCcore-13.3.0
redis-py/4.5.1-foss-2022a
redis-py/5.0.1-GCCcore-12.3.0
redis-py/5.0.9-GCCcore-13.2.0
redis-py/5.1.1-GCCcore-13.3.0
rjags/4-15-foss-2023a-R-4.3.2
ruamel.yaml/0.17.32-GCCcore-12.3.0
ruamel.yaml/0.18.6-GCCcore-13.2.0
ruamel.yaml/0.18.6-GCCcore-13.3.0
samclip/0.4.0-GCCcore-13.3.0
scanpy/1.9.8-foss-2023a
schrodinger/2025-1
scikit-build-core/0.5.0-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-12.3.0
scikit-build-core/0.9.3-GCCcore-13.2.0
scikit-build-core/0.10.6-GCCcore-13.3.0
scikit-build/0.17.6-GCCcore-12.3.0
scikit-build/0.17.6-GCCcore-13.2.0
scikit-build/0.17.6-GCCcore-13.3.0
scikit-image/0.19.3-foss-2022a
scikit-image/0.22.0-foss-2023a
scikit-image/0.25.0-foss-2024a
scikit-learn/1.1.2-foss-2022a
scikit-learn/1.3.1-gfbf-2023a
scikit-learn/1.3.2-gfbf-2023b
scikit-learn/1.4.0-gfbf-2023b
scikit-learn/1.4.2-gfbf-2023a
scikit-learn/1.5.2-gfbf-2024a
scikit-optimize/0.10.2-foss-2023a
seqtk/1.4-GCC-13.3.0
setuptools-rust/1.6.0-GCCcore-12.3.0
setuptools-rust/1.8.0-GCCcore-13.2.0
setuptools-rust/1.9.0-GCCcore-13.3.0
smithwaterman/20160702-GCCcore-12.3.0
smithwaterman/20160702-GCCcore-13.3.0
snakemake/8.27.0-foss-2024a
snappy/1.1.9-GCCcore-11.3.0
snappy/1.1.10-GCCcore-12.3.0
snappy/1.1.10-GCCcore-13.2.0
snappy/1.2.1-GCCcore-13.3.0
snpEff/5.2c-GCCcore-12.3.0-Java-11
spaln/2.4.13f-GCC-12.3.0
spaln/3.0.6b-GCC-12.3.0
sparsehash/2.0.4-GCCcore-12.3.0
spglib-python/2.0.0-foss-2022a
spglib-python/2.5.0-gfbf-2024a
spglib/2.5.0-GCCcore-12.3.0
spoa/4.1.0-GCC-12.3.0
spoa/4.1.0-GCC-13.2.0
statsmodels/0.14.1-gfbf-2023a
statsmodels/0.14.4-gfbf-2024a
swalign/0.2-GCCcore-13.3.0-Python-2.7.18
sympy/1.12-gfbf-2023a
sympy/1.12-gfbf-2023b
sympy/1.13.3-gfbf-2024a
tRNAscan-SE/2.0.12-foss-2023a
tabix/0.2.6-GCCcore-12.3.0
tabix/0.2.6-GCCcore-13.3.0
tabixpp/1.1.2-GCC-12.3.0
tabixpp/1.1.2-GCC-13.3.0
tantan/50-GCC-12.3.0
tbb/2021.5.0-GCCcore-11.3.0
tbb/2021.11.0-GCCcore-12.3.0
tbb/2021.13.0-GCCcore-13.2.0
tbl2asn/20230713-GCCcore-12.3.0
tcsh/6.24.01-GCCcore-11.3.0
tcsh/6.24.10-GCCcore-12.3.0
tcsh/6.24.13-GCCcore-13.3.0
tensorboard/2.15.1-gfbf-2023a
tensorboard/2.18.0-gfbf-2023b
tensorboardX/2.6.2.2-foss-2023a
tensorstore/0.1.65-foss-2023a
tesseract/5.5.0-GCCcore-13.3.0
texlive/20230313-GCC-12.3.0
tiktoken/0.9.0-GCCcore-13.3.0
time/1.9-GCCcore-11.3.0
time/1.9-GCCcore-13.3.0
tmux/3.4-GCCcore-13.3.0
tokenizers/0.15.2-GCCcore-12.3.0
tokenizers/0.19.1-GCCcore-13.2.0
torchvision/0.16.0-foss-2023a-CUDA-12.1.1
tornado/6.3.2-GCCcore-12.3.0
tornado/6.4-GCCcore-13.2.0
tornado/6.4.1-GCCcore-13.3.0
tqdm/4.64.0-GCCcore-11.3.0
tqdm/4.66.1-GCCcore-12.3.0
tqdm/4.66.2-GCCcore-13.2.0
tqdm/4.66.5-GCCcore-13.3.0
trimAl/1.4.1-GCCcore-12.3.0
trimAl/1.5.0-GCCcore-13.3.0
typing-extensions/4.3.0-GCCcore-11.3.0
typing-extensions/4.9.0-GCCcore-12.3.0
typing-extensions/4.10.0-GCCcore-13.2.0
typing-extensions/4.11.0-GCCcore-13.3.0
ucsc/443
umap-learn/0.5.5-foss-2023a
unifdef/2.12-GCCcore-12.3.0
unimap/0.1-GCCcore-11.3.0
utf8proc/2.8.0-GCCcore-12.3.0
utf8proc/2.9.0-GCCcore-13.2.0
utf8proc/2.9.0-GCCcore-13.3.0
util-linux/2.38-GCCcore-11.3.0
util-linux/2.39-GCCcore-12.3.0
util-linux/2.39-GCCcore-13.2.0
util-linux/2.40-GCCcore-13.3.0
vcflib/1.0.9-gfbf-2023a-R-4.3.2
vcflib/1.0.9-gfbf-2024a-R-4.4.2
versioningit/3.1.2-GCCcore-13.3.0
vg/1.65.0
virtualenv/20.23.1-GCCcore-12.3.0
virtualenv/20.24.6-GCCcore-13.2.0
virtualenv/20.26.2-GCCcore-13.3.0
wandb/0.16.1-GCC-12.3.0
wget/1.21.3-GCCcore-11.3.0
wget/1.24.5-GCCcore-12.3.0
wpebackend-fdo/1.15.90-GCCcore-12.3.0
wrapt/1.16.0-gfbf-2024a
wxPython/4.2.1-foss-2023a
wxWidgets/3.2.2.1-GCC-12.3.0
x264/20220620-GCCcore-11.3.0
x264/20230226-GCCcore-12.3.0
x264/20231019-GCCcore-13.2.0
x264/20240513-GCCcore-13.3.0
x265/3.5-GCCcore-11.3.0
x265/3.5-GCCcore-12.3.0
x265/3.5-GCCcore-13.2.0
x265/3.6-GCCcore-13.3.0
xarray/2024.11.0-gfbf-2024a
xorg-macros/1.19.3-GCCcore-11.3.0
xorg-macros/1.20.0-GCCcore-12.3.0
xorg-macros/1.20.0-GCCcore-13.2.0
xorg-macros/1.20.1-GCCcore-13.3.0
xprop/1.2.8-GCCcore-13.3.0
xproto/7.0.31-GCCcore-11.3.0
xproto/7.0.31-GCCcore-13.3.0
xxHash/0.8.2-GCCcore-12.3.0
xxd/9.0.2112-GCCcore-12.3.0
xxd/9.1.0307-GCCcore-13.2.0
xxd/9.1.1275-GCCcore-13.3.0
zarr/2.18.4-foss-2024a
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12
zlib/1.2.13-GCCcore-12.3.0
zlib/1.2.13-GCCcore-13.2.0
zlib/1.2.13
zlib/1.3.1-GCCcore-13.3.0
zlib/1.3.1
zstd/1.5.2-GCCcore-11.3.0
zstd/1.5.5-GCCcore-12.3.0
zstd/1.5.5-GCCcore-13.2.0
zstd/1.5.6-GCCcore-13.3.0

Software installed on Rocky 9

2025-07-02T18:01:32Z

Jordan: /* List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance) */

As part of our July 29-31,2025 maintenance window, the GACRC will be upgrading the Sapelo2 cluster operating system from Rocky 8 to Rocky 9.

Because this is a major OS update, we need to recompile all the applications and ensure that they work with the new version of OS.

Below is a list of the modules already installed on the Rocky 9 system. More software packages continue to be installed. If the software you need is not in this list yet, please feel free to let us know, if you would like us to install it centrally on the updated cluster.

All singularity containers available in /apps/singularity-images on the current Sapelo2 (Rocky 8) will continue to be available after the maintenance.

===List of software already installed centrally on the Rocky 9 system (new Sapelo2, available after the maintenance)===

ABySS/2.3.7-foss-2023a
ADMIXTURE/1.3.0
AFNI/25.1.01-foss-2024a
AGAT/1.4.0-GCC-12.3.0
AMOS/3.1.0-foss-2023a
ANIcalculator/1.0-GCCcore-11.3.0
ANSYS/2025R1
ANTLR/2.7.7-GCCcore-12.3.0-Java-11
ANTLR/2.7.7-GCCcore-13.3.0-Java-17
APR-util/1.6.3-GCCcore-13.3.0
APR/1.7.4-GCCcore-13.3.0
ASE/3.22.1-foss-2022a
ASE/3.23.0-gfbf-2024a
ASTRAL/5.7.8-Java-1.8.0_241
ATK/2.38.0-GCCcore-11.3.0
ATK/2.38.0-GCCcore-12.3.0
ATK/2.38.0-GCCcore-13.2.0
AUGUSTUS/3.5.0-foss-2022a
AUGUSTUS/3.5.0-foss-2023a
AUGUSTUS/3.5.0-foss-2024a
AUGUSTUS/3.5.0-20240612-foss-2023a
Abseil/20230125.3-GCCcore-12.3.0
Abseil/20240116.1-GCCcore-13.2.0
Abseil/20240722.0-GCCcore-13.3.0
Albumentations/1.3.0-foss-2022a
Albumentations/1.4.0-foss-2023a
AlphaFold/2.3.2-foss-2023a-CUDA-12.1.1
Amber/22.4-foss-2022a-AmberTools-22.5-CUDA-11.7.0
Amber/24.3-foss-2022a-AmberTools-24.10-CUDA-12.1.1
Ancestry_HMM/1.0.2-foss-2024a
Archive-Zip/1.68-GCCcore-12.3.0
Archive-Zip/1.68-GCCcore-13.3.0
Armadillo/11.4.3-foss-2022a
Armadillo/12.6.2-foss-2023a
Armadillo/12.8.0-foss-2023b
Armadillo/14.0.3-foss-2024a
Arrow/14.0.1-gfbf-2023a
Arrow/16.1.0-gfbf-2023b
Arrow/17.0.0-gfbf-2024a
Autoconf/2.71-GCCcore-11.3.0
Autoconf/2.71-GCCcore-12.3.0
Autoconf/2.71-GCCcore-13.2.0
Autoconf/2.71
Autoconf/2.72-GCCcore-13.3.0
Automake/1.16.5-GCCcore-11.3.0
Automake/1.16.5-GCCcore-12.3.0
Automake/1.16.5-GCCcore-13.2.0
Automake/1.16.5-GCCcore-13.3.0
Automake/1.16.5
Autotools/20220317-GCCcore-11.3.0
Autotools/20220317-GCCcore-12.3.0
Autotools/20220317-GCCcore-13.2.0
Autotools/20220317
Autotools/20231222-GCCcore-13.3.0
BBMap/39.01-GCC-11.3.0
BBMap/39.01-GCC-12.3.0
BBMap/39.19-GCC-13.3.0
BCFtools/1.15.1-GCC-11.3.0
BCFtools/1.18-GCC-12.3.0
BCFtools/1.21-GCC-13.3.0
BEDOPS/2.4.41-foss-2023a
BEDTools/2.31.0-GCC-12.3.0
BEDTools/2.31.1-GCC-13.3.0
BLAST+/2.13.0-gompi-2022a
BLAST+/2.14.1-gompi-2023a
BLAST+/2.16.0-gompi-2024a
BLAT/3.7-GCC-11.3.0
BLAT/3.7-GCC-12.3.0
BLIS/0.9.0-GCC-11.3.0
BLIS/0.9.0-GCC-12.3.0
BLIS/0.9.0-GCC-13.2.0
BLIS/1.0-GCC-13.3.0
BRAKER/3.0.8-foss-2023a
BUSCO/5.8.3-foss-2023a
BWA/0.7.17-GCCcore-11.3.0
BWA/0.7.17-GCCcore-12.3.0
BWA/0.7.18-GCCcore-13.2.0
BWA/0.7.18-GCCcore-13.3.0
BamTools/2.5.2-GCC-11.3.0
BamTools/2.5.2-GCC-12.3.0
BamTools/2.5.2-GCC-13.3.0
Bandage/0.9.0-GCCcore-12.3.0
Bazel/6.1.0-GCCcore-12.3.0
Bazel/6.3.1-GCCcore-12.3.0
Beagle/5.4.22Jul22.46e-Java-11
Beast/2.7.7-GCC-12.3.0-CUDA-12.1.1
BeautifulSoup/4.12.2-GCCcore-12.3.0
BeautifulSoup/4.12.2-GCCcore-13.2.0
BeautifulSoup/4.12.3-GCCcore-13.3.0
Bio-DB-HTS/3.01-GCC-12.3.0
Bio-DB-HTS/3.01-GCC-13.3.0
Bio-SearchIO-hmmer/1.7.3-GCC-12.3.0
BioPerl/1.7.2-GCCcore-13.3.0
BioPerl/1.7.8-GCCcore-11.3.0
BioPerl/1.7.8-GCCcore-12.3.0
BioPerl/1.7.8-GCCcore-13.2.0
BioPerl/1.7.8-GCCcore-13.3.0
Biopython/1.79-foss-2022a
Biopython/1.83-foss-2023a
Biopython/1.84-foss-2023b
Biopython/1.84-foss-2024a
Bismark/0.24.2-GCC-12.3.0
Bison/3.8.2-GCCcore-11.3.0
Bison/3.8.2-GCCcore-12.3.0
Bison/3.8.2-GCCcore-13.2.0
Bison/3.8.2-GCCcore-13.3.0
Bison/3.8.2
Blosc/1.21.5-GCCcore-12.3.0
Blosc/1.21.6-GCCcore-13.3.0
Blosc2/2.17.0-GCCcore-13.3.0
Boltz/2.1.1
Boost.MPI/1.79.0-gompi-2022a
Boost.MPI/1.82.0-gompi-2023a
Boost.Python/1.82.0-GCC-12.3.0
Boost.Python/1.85.0-GCC-13.3.0
Boost/1.75.0-GCC-12.3.0
Boost/1.79.0-GCC-11.3.0
Boost/1.82.0-GCC-12.3.0
Boost/1.83.0-GCC-13.2.0
Boost/1.85.0-GCC-13.3.0
Bowtie/1.3.1-GCC-11.3.0
Bowtie/1.3.1-GCC-12.3.0
Bowtie2/2.4.5-GCC-11.3.0
Bowtie2/2.5.1-GCC-12.3.0
Bowtie2/2.5.2-GCC-11.3.0
Bracken/3.1-GCCcore-12.3.0
BrainNetViewer/20191031
Brotli-python/1.1.0-GCCcore-13.3.0
Brotli/1.0.9-GCCcore-11.3.0
Brotli/1.0.9-GCCcore-12.3.0
Brotli/1.1.0-GCCcore-13.2.0
Brotli/1.1.0-GCCcore-13.3.0
Brunsli/0.1-GCCcore-12.3.0
Brunsli/0.1-GCCcore-13.2.0
Brunsli/0.1-GCCcore-13.3.0
CD-HIT/4.8.1-GCC-11.3.0
CD-HIT/4.8.1-GCC-12.3.0
CDBtools/0.99-GCC-12.3.0
CDO/2.4.4-gompi-2024a
CFITSIO/4.3.0-GCCcore-12.3.0
CFITSIO/4.3.1-GCCcore-13.2.0
CFITSIO/4.4.1-GCCcore-13.3.0
CGAL/5.6.1-GCCcore-13.3.0
CMSeq/1.0.4-foss-2024a
CMake/3.18.4
CMake/3.23.1-GCCcore-11.3.0
CMake/3.24.3-GCCcore-11.3.0
CMake/3.26.3-GCCcore-12.3.0
CMake/3.27.6-GCCcore-13.2.0
CMake/3.29.3-GCCcore-13.3.0
CUDA/11.7.0
CUDA/12.1.1
CUDA/12.4.0
CUDA/12.6.0
CUnit/2.1-3-GCCcore-12.3.0
CVXPY/1.4.2-foss-2023a
CapnProto/0.10.2-GCCcore-11.3.0
CapnProto/1.0.1.1-GCCcore-13.2.0
CapnProto/1.1.0-GCCcore-13.3.0
Cartopy/0.22.0-foss-2023a
Catch2/2.13.9-GCCcore-12.3.0
Catch2/2.13.9-GCCcore-13.2.0
Catch2/2.13.10-GCCcore-13.3.0
Cbc/2.10.12-foss-2024a
Cereal/1.3.0
Cereal/1.3.2
Cgl/0.60.8-foss-2024a
CharLS/2.4.2-GCCcore-13.3.0
CheMPS2/1.8.12-foss-2023a
CheMPS2/1.8.12-foss-2023b
CheMPS2/1.8.12-foss-2024a
Check/0.15.2-GCCcore-13.3.0
CheckM-Database/2015_01_16
CheckM/1.2.2-foss-2022a
Circlator/1.5.5-foss-2023a
Clang/13.0.1-GCCcore-11.3.0
Clang/16.0.6-GCCcore-12.3.0-CUDA-12.1.1
Clang/16.0.6-GCCcore-12.3.0
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Fiona/1.10.1-foss-2024a
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GCC/12.3.0
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GCCcore/13.3.0
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GMAP-GSNAP/2023-04-20-GCC-12.3.0
GMAP-GSNAP/2024-09-18-GCC-13.3.0
GMP/6.2.1-GCCcore-11.3.0
GMP/6.2.1-GCCcore-12.3.0
GMP/6.3.0-GCCcore-13.2.0
GMP/6.3.0-GCCcore-13.3.0
GOATOOLS/1.4.5-foss-2023a
GOATOOLS/1.4.12-foss-2024a
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GROMACS/2024.4-foss-2023b-CUDA-12.4.0-PLUMED-2.9.2
GSL/2.7-GCC-11.3.0
GSL/2.7-GCC-12.3.0
GSL/2.7-GCC-13.2.0
GSL/2.8-GCC-13.3.0
GST-plugins-bad/1.20.2-GCC-11.3.0
GST-plugins-bad/1.22.5-GCC-12.3.0
GST-plugins-base/1.20.2-GCC-11.3.0
GST-plugins-base/1.22.5-GCC-12.3.0
GStreamer/1.20.2-GCC-11.3.0
GStreamer/1.22.5-GCC-12.3.0
GStreamer/1.24.8-GCC-13.3.0
GTDB-Tk/2.3.2-foss-2022a
GTK2/2.24.33-GCCcore-11.3.0
GTK3/3.24.37-GCCcore-12.3.0
GTK3/3.24.39-GCCcore-13.2.0
GTK4/4.7.0-GCC-11.3.0
GTK4/4.13.1-GCC-12.3.0
GTS/0.7.6-GCCcore-12.3.0
GTS/0.7.6-GCCcore-13.3.0
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GeneMark-ET/4.72-GCCcore-12.3.0
GeneMark-ETP/1.0.0-GCC-12.3.0
Genome/05012020-foss-2022a
GenomeThreader/1.7.3-Linux_x86_64-64bit
GenomeTools/1.6.2-GCC-11.3.0
GetOrganelle/1.7.7.1-foss-2023a
Ghostscript/9.56.1-GCCcore-11.3.0
Ghostscript/10.01.2-GCCcore-12.3.0
Ghostscript/10.02.1-GCCcore-13.2.0
Ghostscript/10.03.1-GCCcore-13.3.0
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GitPython/3.1.43-GCCcore-13.3.0
Giza/1.4.1-GCCcore-13.2.0
GlimmerHMM/3.0.4c-GCC-12.3.0
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GlobalArrays/5.8.2-intel-2024a
GnuTLS/3.7.8-GCCcore-12.3.0
Go/1.22.1
Graphene/1.10.8-GCCcore-11.3.0
Graphene/1.10.8-GCCcore-12.3.0
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Guile/3.0.9-GCCcore-12.3.0
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HDF/4.2.15-GCCcore-11.3.0
HDF/4.2.16-2-GCCcore-12.3.0
HDF/4.2.16-2-GCCcore-13.2.0
HDF/4.3.0-GCCcore-13.3.0
HDF5/1.12.2-gompi-2022a
HDF5/1.14.0-gompi-2023a
HDF5/1.14.0-iimpi-2023a
HDF5/1.14.3-gompi-2023b
HDF5/1.14.5-gompi-2024a
HH-suite/3.3.0-gompi-2023a
HISAT2/2.2.1-gompi-2022a
HISAT2/2.2.1-gompi-2023a
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HMMER/3.4-gompi-2023a
HMMER/3.4-gompi-2023b
HMMER/3.4-gompi-2024a
HTSeq/2.0.7-foss-2023a
HTSlib/1.15.1-GCC-11.3.0
HTSlib/1.18-GCC-12.3.0
HTSlib/1.19.1-GCC-13.2.0
HTSlib/1.21-GCC-13.3.0
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Homer/5.1-foss-2023a-R-4.3.2
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ICU/73.2-GCCcore-12.3.0
ICU/74.1-GCCcore-13.2.0
ICU/75.1-GCCcore-13.3.0
IDBA-UD/1.1.3-GCCcore-12.3.0
IGV/2.19.1-Java-21
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IPython/8.27.0-GCCcore-13.3.0
IPython/8.28.0-GCCcore-13.3.0
IQ-TREE/2.3.6-gompi-2023a
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Infernal/1.1.5-gompi-2023a
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IsoSeq/4.3.0
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JAGS/4.3.2-foss-2024a
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Java/1.8.0_311 (1.8)
Java/11.0.20 (11)
Java/17.0.6 (17)
Java/21.0.2
Java/21.0.5 (D:21)
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Judy/1.0.5-GCCcore-12.3.0
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Kent_tools/468-GCC-12.3.0
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LAME/3.100-GCCcore-11.3.0
LAME/3.100-GCCcore-12.3.0
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LAME/3.100-GCCcore-13.3.0
LAMMPS/29Aug2024-foss-2023b-kokkos
LASTZ/1.04.22-GCC-12.3.0
LDC/1.24.0-x86_64
LDC/1.30.0-GCCcore-11.3.0
LERC/4.0.0-GCCcore-12.3.0
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LLVM/14.0.6-GCCcore-13.3.0-llvmlite
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LLVM/18.1.8-GCCcore-13.3.0-minimal
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LMDB/0.9.31-GCCcore-13.2.0
LMDB/0.9.31-GCCcore-13.3.0
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LTR_retriever/2.9.0-foss-2022a
LZO/2.10-GCCcore-11.3.0
LZO/2.10-GCCcore-12.3.0
LZO/2.10-GCCcore-13.3.0
Leptonica/1.85.0-GCCcore-13.3.0
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LibTIFF/4.6.0-GCCcore-13.3.0
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LiftOn/1.0.5-foss-2023a
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LittleCMS/2.15-GCCcore-12.3.0
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LittleCMS/2.16-GCCcore-13.3.0
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M4/1.4.18
M4/1.4.19-GCCcore-11.3.0
M4/1.4.19-GCCcore-12.3.0
M4/1.4.19-GCCcore-13.2.0
M4/1.4.19-GCCcore-13.3.0
M4/1.4.19
MACS2/2.2.9.1-foss-2023a
MACS3/3.0.1-gfbf-2023a
MAFFT/7.505-GCC-11.3.0-with-extensions
MAFFT/7.520-GCC-12.3.0-with-extensions
MAFFT/7.526-GCC-13.2.0-with-extensions
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MCL/22.282-GCCcore-12.3.0
MCR/R2018a
MCR/R2023a
MCR/R2023b.9
MDI/1.4.29-gompi-2023b
MDTraj/1.9.7-foss-2022a
MDTraj/1.9.9-gfbf-2023a
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MEGA/11.0.13
MEGAHIT/1.2.9-GCCcore-13.3.0
MEME/5.5.7-gompi-2023b
METABOLIC/4.0-foss-2022a
METIS/5.1.0-GCCcore-11.3.0
METIS/5.1.0-GCCcore-12.3.0
METIS/5.1.0-GCCcore-13.2.0
METIS/5.1.0-GCCcore-13.3.0
MMseqs2/14-7e284-gompi-2022a
MMseqs2/14-7e284-gompi-2023a
MPC/1.3.1-GCCcore-12.3.0
MPC/1.3.1-GCCcore-13.2.0
MPC/1.3.1-GCCcore-13.3.0
MPFR/4.1.0-GCCcore-11.3.0
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MPFR/4.2.1-GCCcore-13.2.0
MPFR/4.2.1-GCCcore-13.3.0
MSTmap/20240122-GCC-12.3.0
MUMPS/5.6.1-foss-2023a-metis
MUMPS/5.7.2-foss-2024a-metis
MUMmer/4.0.0rc1-GCCcore-11.3.0
MUMmer/4.0.0rc1-GCCcore-12.3.0
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Mako/1.2.0-GCCcore-11.3.0
Mako/1.2.4-GCCcore-12.3.0
Mako/1.2.4-GCCcore-13.2.0
Mako/1.3.5-GCCcore-13.3.0
Mamba/23.11.0-0
MariaDB/10.9.3-GCC-11.3.0
MariaDB/11.6.0-GCC-12.3.0
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Mash/2.3-GCC-11.3.0
Mash/2.3-GCC-13.2.0
Mash/2.3-GCC-13.3.0
Maven/3.6.3
Maven/3.9.7
Medusa/1.6-foss-2023a-Python-3.11.3-Java-11
Mesa/22.0.3-GCCcore-11.3.0
Mesa/23.1.4-GCCcore-12.3.0
Mesa/23.1.9-GCCcore-13.2.0
Mesa/24.1.3-GCCcore-13.3.0
Meson/0.58.2-GCCcore-12.3.0
Meson/0.62.1-GCCcore-11.3.0
Meson/0.64.0-GCCcore-12.3.0
Meson/1.1.1-GCCcore-12.3.0
Meson/1.2.3-GCCcore-13.2.0
Meson/1.3.1-GCCcore-12.3.0
Meson/1.4.0-GCCcore-13.3.0
MetaBAT/2.17-GCC-13.3.0
MetaEuk/6-GCC-11.3.0
MetaEuk/6-GCC-12.3.0
Micromamba/2.3.0
Miniconda3/23.5.2-0
Miniforge3/24.11.3-0
MitoFinder/1.4.2-foss-2023a-Python-2.7.18
Molden/7.3-GCCcore-13.3.0
Monocle3/1.3.1-foss-2023a-R-4.3.2
MrBayes/3.2.7-gompi-2023a
MultiQC/1.28-foss-2024a
NAMD/2.14-foss-2023a-mpi
NASM/2.15.05-GCCcore-11.3.0
NASM/2.16.01-GCCcore-12.3.0
NASM/2.16.01-GCCcore-13.2.0
NASM/2.16.03-GCCcore-13.3.0
NCCL/2.12.12-GCCcore-11.3.0-CUDA-11.7.0
NCCL/2.18.3-GCCcore-11.3.0-CUDA-12.1.1
NCCL/2.18.3-GCCcore-12.3.0-CUDA-12.1.1
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util-linux/2.39-GCCcore-13.2.0
util-linux/2.40-GCCcore-13.3.0
vcflib/1.0.9-gfbf-2023a-R-4.3.2
vcflib/1.0.9-gfbf-2024a-R-4.4.2
versioningit/3.1.2-GCCcore-13.3.0
vg/1.65.0
virtualenv/20.23.1-GCCcore-12.3.0
virtualenv/20.24.6-GCCcore-13.2.0
virtualenv/20.26.2-GCCcore-13.3.0
wandb/0.16.1-GCC-12.3.0
wget/1.21.3-GCCcore-11.3.0
wget/1.24.5-GCCcore-12.3.0
wpebackend-fdo/1.15.90-GCCcore-12.3.0
wrapt/1.16.0-gfbf-2024a
wxPython/4.2.1-foss-2023a
wxWidgets/3.2.2.1-GCC-12.3.0
x264/20220620-GCCcore-11.3.0
x264/20230226-GCCcore-12.3.0
x264/20231019-GCCcore-13.2.0
x264/20240513-GCCcore-13.3.0
x265/3.5-GCCcore-11.3.0
x265/3.5-GCCcore-12.3.0
x265/3.5-GCCcore-13.2.0
x265/3.6-GCCcore-13.3.0
xarray/2024.11.0-gfbf-2024a
xorg-macros/1.19.3-GCCcore-11.3.0
xorg-macros/1.20.0-GCCcore-12.3.0
xorg-macros/1.20.0-GCCcore-13.2.0
xorg-macros/1.20.1-GCCcore-13.3.0
xprop/1.2.8-GCCcore-13.3.0
xproto/7.0.31-GCCcore-11.3.0
xproto/7.0.31-GCCcore-13.3.0
xxHash/0.8.2-GCCcore-12.3.0
xxd/9.0.2112-GCCcore-12.3.0
xxd/9.1.0307-GCCcore-13.2.0
xxd/9.1.1275-GCCcore-13.3.0
zarr/2.18.4-foss-2024a
zlib/1.2.11
zlib/1.2.12-GCCcore-11.3.0
zlib/1.2.12
zlib/1.2.13-GCCcore-12.3.0
zlib/1.2.13-GCCcore-13.2.0
zlib/1.2.13
zlib/1.3.1-GCCcore-13.3.0
zlib/1.3.1
zstd/1.5.2-GCCcore-11.3.0
zstd/1.5.5-GCCcore-12.3.0
zstd/1.5.5-GCCcore-13.2.0
zstd/1.5.6-GCCcore-13.3.0

SRAToolKit-Sapelo2

2025-05-27T20:40:22Z

Jordan: /* Downloading SRA Data */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===
BioInformatics
=== Program On ===
Sapelo2
=== Version ===
3.0.1, 3.0.3, 3.0.10
===Author / Distributor===
Please see https://github.com/ncbi/sra-tools
===Description===
The SRA Toolkit from NCBI is a collection of tools and libraries for using data in the INSDC Sequence Read Archives. The Sequence Read Archives (SRA) store raw sequence data from "next-generation" sequencing technologies including Illumina, 454, IonTorrent, Complete Genomics, PacBio and OxfordNanopores. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence. The SRA-Toolkit includes the following tools:
{| class="wikitable"
|+
!Command
!Description
|-
|<code>fastq-dump</code>
|Convert SRA data into fastq format
|-
|<code>prefetch</code>
|Allows command-line downloading of SRA, dbGaP, and ADSP data
|-
|<code>sam-dump</code>
|Convert SRA data to sam format
|-
|<code>sra-pileup</code>
|Generate pileup statistics on aligned SRA data
|-
|<code>vdb-config</code>
|Display and modify VDB configuration information
|-
|<code>vdb-decrypt</code>
|Decrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>abi-dump</code>
|Convert SRA data into ABI format (csfasta / qual)
|-
|<code>illumina-dump</code>
|Convert SRA data into Illumina native formats (qseq, etc.)
|-
|<code>sff-dump</code>
|Convert SRA data to sff format
|-
|<code>sra-stat</code>
|Generate statistics about SRA data (quality distribution, etc.)
|-
|<code>vdb-dump</code>
|Output the native VDB format of SRA data.
|-
|<code>vdb-encrypt</code>
|Encrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>vdb-validate</code>
|Validate the integrity of downloaded SRA data
|}

=== Downloading SRA Data ===

The latest installed version of this application is at SRA-Toolkit/3.0.10-gompi-2023a

To use this version, please load the module with
<pre class="gscript">
ml SRA-Toolkit/3.0.10-gompi-2023a
</pre>

You can download SRA data to local directory with the prefetch tool. This program downloads Runs (sequence files in the compressed SRA format) and all additional data necessary to convert the Run from the SRA format to a more commonly used format. You can search for a dataset using the search bar at the top of the SRA homepage. https://www.ncbi.nlm.nih.gov/sra Once you find a dataset you would like to download, search for the "Run number" in the table towards the bottom of the webpage for that dataset. Then create the folder where prefetch will deposit your files. This needs to be an empty folder. It is recommended that you download data into your scratch directory since there is plenty of space there.

Next, run the command:

<pre>vdb-config --interactive</pre>

This will open a screen where you operate the buttons by pressing the letter highlighted in red, or by pressing the tab-key until the wanted button is reached and then pressing the space- or the enter-key. Make sure there is an X by the "'''Enable Remote Access'''" option on the MAIN tab, and X by the "'''enable local file-caching'''" option in the CACHE tab. Then set the "location of user-repository" to the empty folder you created. In the following image the data will be downloaded to /scratch/keekov/prefetchData. These settings are stored in a file called '''user-settings.mkfg''' in a hidden directory called .ncbi in the top level of your home directory.
[[File:Sratools.png|thumb]]

Then press "s" or navigate to the save button and press enter to save. Then press "x" or navigate to the exit button and press enter to exit. Now you can start the data download by running the command prefetch followed by the run number. For example the following downloads the dataset SRR390728.
<pre>prefetch SRR390728</pre>

For more information about the prefetch command refer to the [https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc&f=prefetch documentation]

=== Running Program ===
You can now run other SRA-Tools on the SRA data you've downloaded.
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo please see the [[Lmod]] page.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=SRA-ToolsExample 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=6 #fasterq-dump defaults to using 6 threads 
<nowiki>#</nowiki>SBATCH --mem=24gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

cd $SLURM_SUBMIT_DIR 
ml SRA-Toolkit/3.0.10-gompi-2023a 
prefetch SRR390728 
fasterq-dump /scratch/MyID/SRR390728.sra #You should use the directory you selected in the vdb-config step above 
</div>

Note that if you're downloading paired-end (Illumina) sequencing data and want the paired ends in separate .fastq files, use the '-S' option when extracting the files using fasterq-dump.

=== Documentation ===

Please see https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc for the documentation of each tool.

=== Installation ===

=== System ===
64-bit Linux

SRAToolKit-Sapelo2

2025-05-27T20:39:37Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===
BioInformatics
=== Program On ===
Sapelo2
=== Version ===
3.0.1, 3.0.3, 3.0.10
===Author / Distributor===
Please see https://github.com/ncbi/sra-tools
===Description===
The SRA Toolkit from NCBI is a collection of tools and libraries for using data in the INSDC Sequence Read Archives. The Sequence Read Archives (SRA) store raw sequence data from "next-generation" sequencing technologies including Illumina, 454, IonTorrent, Complete Genomics, PacBio and OxfordNanopores. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence. The SRA-Toolkit includes the following tools:
{| class="wikitable"
|+
!Command
!Description
|-
|<code>fastq-dump</code>
|Convert SRA data into fastq format
|-
|<code>prefetch</code>
|Allows command-line downloading of SRA, dbGaP, and ADSP data
|-
|<code>sam-dump</code>
|Convert SRA data to sam format
|-
|<code>sra-pileup</code>
|Generate pileup statistics on aligned SRA data
|-
|<code>vdb-config</code>
|Display and modify VDB configuration information
|-
|<code>vdb-decrypt</code>
|Decrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>abi-dump</code>
|Convert SRA data into ABI format (csfasta / qual)
|-
|<code>illumina-dump</code>
|Convert SRA data into Illumina native formats (qseq, etc.)
|-
|<code>sff-dump</code>
|Convert SRA data to sff format
|-
|<code>sra-stat</code>
|Generate statistics about SRA data (quality distribution, etc.)
|-
|<code>vdb-dump</code>
|Output the native VDB format of SRA data.
|-
|<code>vdb-encrypt</code>
|Encrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>vdb-validate</code>
|Validate the integrity of downloaded SRA data
|}

=== Downloading SRA Data ===

The last version of this application is at /apps/eb/SRA-Toolkit/3.0.3-gompi-2022a

To use this version, please load the module with
<pre class="gscript">
ml SRA-Toolkit/3.0.3-gompi-2022a
</pre>

You can download SRA data to local directory with the prefetch tool. This program downloads Runs (sequence files in the compressed SRA format) and all additional data necessary to convert the Run from the SRA format to a more commonly used format. You can search for a dataset using the search bar at the top of the SRA homepage. https://www.ncbi.nlm.nih.gov/sra Once you find a dataset you would like to download, search for the "Run number" in the table towards the bottom of the webpage for that dataset. Then create the folder where prefetch will deposit your files. This needs to be an empty folder. It is recommended that you download data into your scratch directory since there is plenty of space there.

Next, run the command:

<pre>vdb-config --interactive</pre>

This will open a screen where you operate the buttons by pressing the letter highlighted in red, or by pressing the tab-key until the wanted button is reached and then pressing the space- or the enter-key. Make sure there is an X by the "'''Enable Remote Access'''" option on the MAIN tab, and X by the "'''enable local file-caching'''" option in the CACHE tab. Then set the "location of user-repository" to the empty folder you created. In the following image the data will be downloaded to /scratch/keekov/prefetchData. These settings are stored in a file called '''user-settings.mkfg''' in a hidden directory called .ncbi in the top level of your home directory.
[[File:Sratools.png|thumb]]

Then press "s" or navigate to the save button and press enter to save. Then press "x" or navigate to the exit button and press enter to exit. Now you can start the data download by running the command prefetch followed by the run number. For example the following downloads the dataset SRR390728.
<pre>prefetch SRR390728</pre>

For more information about the prefetch command refer to the [https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc&f=prefetch documentation]

=== Running Program ===
You can now run other SRA-Tools on the SRA data you've downloaded.
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo please see the [[Lmod]] page.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=SRA-ToolsExample 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=6 #fasterq-dump defaults to using 6 threads 
<nowiki>#</nowiki>SBATCH --mem=24gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

cd $SLURM_SUBMIT_DIR 
ml SRA-Toolkit/3.0.10-gompi-2023a 
prefetch SRR390728 
fasterq-dump /scratch/MyID/SRR390728.sra #You should use the directory you selected in the vdb-config step above 
</div>

Note that if you're downloading paired-end (Illumina) sequencing data and want the paired ends in separate .fastq files, use the '-S' option when extracting the files using fasterq-dump.

=== Documentation ===

Please see https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc for the documentation of each tool.

=== Installation ===

=== System ===
64-bit Linux

SRAToolKit-Sapelo2

2025-05-27T20:35:01Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===
BioInformatics
=== Program On ===
Sapelo2
=== Version ===
3.0.1, 3.0.3, 3.0.10
===Author / Distributor===
Please see https://github.com/ncbi/sra-tools
===Description===
The SRA Toolkit from NCBI is a collection of tools and libraries for using data in the INSDC Sequence Read Archives. The Sequence Read Archives (SRA) store raw sequence data from "next-generation" sequencing technologies including Illumina, 454, IonTorrent, Complete Genomics, PacBio and OxfordNanopores. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence. The SRA-Toolkit includes the following tools:
{| class="wikitable"
|+
!Command
!Description
|-
|<code>fastq-dump</code>
|Convert SRA data into fastq format
|-
|<code>prefetch</code>
|Allows command-line downloading of SRA, dbGaP, and ADSP data
|-
|<code>sam-dump</code>
|Convert SRA data to sam format
|-
|<code>sra-pileup</code>
|Generate pileup statistics on aligned SRA data
|-
|<code>vdb-config</code>
|Display and modify VDB configuration information
|-
|<code>vdb-decrypt</code>
|Decrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>abi-dump</code>
|Convert SRA data into ABI format (csfasta / qual)
|-
|<code>illumina-dump</code>
|Convert SRA data into Illumina native formats (qseq, etc.)
|-
|<code>sff-dump</code>
|Convert SRA data to sff format
|-
|<code>sra-stat</code>
|Generate statistics about SRA data (quality distribution, etc.)
|-
|<code>vdb-dump</code>
|Output the native VDB format of SRA data.
|-
|<code>vdb-encrypt</code>
|Encrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>vdb-validate</code>
|Validate the integrity of downloaded SRA data
|}

=== Downloading SRA Data ===

The last version of this application is at /apps/eb/SRA-Toolkit/3.0.3-gompi-2022a

To use this version, please load the module with
<pre class="gscript">
ml SRA-Toolkit/3.0.3-gompi-2022a
</pre>

You can download SRA data to local directory with the prefetch tool. This program downloads Runs (sequence files in the compressed SRA format) and all additional data necessary to convert the Run from the SRA format to a more commonly used format. You can search for a dataset using the search bar at the top of the SRA homepage. https://www.ncbi.nlm.nih.gov/sra Once you find a dataset you would like to download, search for the "Run number" in the table towards the bottom of the webpage for that dataset. Then create the folder where prefetch will deposit your files. This needs to be an empty folder. It is recommended that you download data into your scratch directory since there is plenty of space there.

Next, run the command:

<pre>vdb-config --interactive</pre>

This will open a screen where you operate the buttons by pressing the letter highlighted in red, or by pressing the tab-key until the wanted button is reached and then pressing the space- or the enter-key. Make sure there is an X by the "'''Enable Remote Access'''" option on the MAIN tab, and X by the "'''enable local file-caching'''" option in the CACHE tab. Then set the "location of user-repository" to the empty folder you created. In the following image the data will be downloaded to /scratch/keekov/prefetchData. These settings are stored in a file called '''user-settings.mkfg''' in a hidden directory called .ncbi in the top level of your home directory.
[[File:Sratools.png|thumb]]

Then press "s" or navigate to the save button and press enter to save. Then press "x" or navigate to the exit button and press enter to exit. Now you can start the data download by running the command prefetch followed by the run number. For example the following downloads the dataset SRR390728.
<pre>prefetch SRR390728</pre>

For more information about the prefetch command refer to the [https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc&f=prefetch documentation]

=== Running Program ===
You can now run other SRA-Tools on the SRA data you've downloaded.
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo please see the [[Lmod]] page.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=SRA-ToolsExample 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=6 #fasterq-dump defaults to using 6 threads 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=fastq-dump.%j.out 
<nowiki>#</nowiki>SBATCH --error=fastq-dump.%j.err 

cd $SLURM_SUBMIT_DIR 
ml SRA-Toolkit/3.0.10-gompi-2023a 
prefetch SRR390728
fasterq-dump /scratch/keekov/SRR390728.sra #You should use the directory you selected in the vdb-config step above 
</div>

=== Documentation ===

Please see https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc for the documentation of each tool.

=== Installation ===

=== System ===
64-bit Linux

SRAToolKit-Sapelo2

2025-05-27T19:35:37Z

Jordan: Edited to include v3.0.10 to the list of available versions.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===
BioInformatics
=== Program On ===
Sapelo2
=== Version ===
3.0.1, 3.0.3, 3.0.10
===Author / Distributor===
Please see https://github.com/ncbi/sra-tools
===Description===
The SRA Toolkit from NCBI is a collection of tools and libraries for using data in the INSDC Sequence Read Archives. The Sequence Read Archives (SRA) store raw sequence data from "next-generation" sequencing technologies including Illumina, 454, IonTorrent, Complete Genomics, PacBio and OxfordNanopores. In addition to raw sequence data, SRA now stores alignment information in the form of read placements on a reference sequence. The SRA-Toolkit includes the following tools:
{| class="wikitable"
|+
!Command
!Description
|-
|<code>fastq-dump</code>
|Convert SRA data into fastq format
|-
|<code>prefetch</code>
|Allows command-line downloading of SRA, dbGaP, and ADSP data
|-
|<code>sam-dump</code>
|Convert SRA data to sam format
|-
|<code>sra-pileup</code>
|Generate pileup statistics on aligned SRA data
|-
|<code>vdb-config</code>
|Display and modify VDB configuration information
|-
|<code>vdb-decrypt</code>
|Decrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>abi-dump</code>
|Convert SRA data into ABI format (csfasta / qual)
|-
|<code>illumina-dump</code>
|Convert SRA data into Illumina native formats (qseq, etc.)
|-
|<code>sff-dump</code>
|Convert SRA data to sff format
|-
|<code>sra-stat</code>
|Generate statistics about SRA data (quality distribution, etc.)
|-
|<code>vdb-dump</code>
|Output the native VDB format of SRA data.
|-
|<code>vdb-encrypt</code>
|Encrypt non-SRA dbGaP data ("phenotype data")
|-
|<code>vdb-validate</code>
|Validate the integrity of downloaded SRA data
|}

=== Downloading SRA Data ===

The last version of this application is at /apps/eb/SRA-Toolkit/3.0.3-gompi-2022a

To use this version, please load the module with
<pre class="gscript">
ml SRA-Toolkit/3.0.3-gompi-2022a
</pre>

You can download SRA data to local directory with the prefetch tool. This program downloads Runs (sequence files in the compressed SRA format) and all additional data necessary to convert the Run from the SRA format to a more commonly used format. You can search for a dataset using the search bar at the top of the SRA homepage. https://www.ncbi.nlm.nih.gov/sra Once you find a dataset you would like to download, search for the "Run number" in the table towards the bottom of the webpage for that dataset. Then create the folder where prefetch will deposit your files. This needs to be an empty folder. It is recommended that you download data into your scratch directory since there is plenty of space there.

Next, run the command:

<pre>vdb-config --interactive</pre>

This will open a screen where you operate the buttons by pressing the letter highlighted in red, or by pressing the tab-key until the wanted button is reached and then pressing the space- or the enter-key. Make sure there is an X by the "'''Enable Remote Access'''" option on the MAIN tab, and X by the "'''enable local file-caching'''" option in the CACHE tab. Then set the "location of user-repository" to the empty folder you created. In the following image the data will be downloaded to /scratch/keekov/prefetchData. These settings are stored in a file called '''user-settings.mkfg''' in a hidden directory called .ncbi in the top level of your home directory.
[[File:Sratools.png|thumb]]

Then press "s" or navigate to the save button and press enter to save. Then press "x" or navigate to the exit button and press enter to exit. Now you can start the data download by running the command prefetch followed by the run number. For example the following downloads the dataset SRR390728.
<pre>prefetch SRR390728</pre>

For more information about the prefetch command refer to the [https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc&f=prefetch documentation]

=== Running Program ===
You can now run other SRA-Tools on the SRA data you've downloaded.
Also refer to [[Running Jobs on Sapelo2]]

For more information on Environment Modules on Sapelo please see the [[Lmod]] page.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=SRA-ToolsExample 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=fastq-dump.%j.out 
<nowiki>#</nowiki>SBATCH --error=fastq-dump.%j.err 

cd $SLURM_SUBMIT_DIR 
ml SRA-Toolkit/3.0.3-gompi-2022a 
fastq-dump -X 5 -Z /scratch/keekov/SRR390728.sra #You should use the directory you selected in the vdb-config step above 
</div>

=== Documentation ===

Please see https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=toolkit_doc for the documentation of each tool.

=== Installation ===

=== System ===
64-bit Linux

EGAPx-Sapelo2

2025-05-20T19:14:42Z

Jordan: Initial creation of this page

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
N/A

=== Version ===
0.3.2-alpha

=== Author / Distributor ===
Please see https://github.com/ncbi/egapx

=== Description ===
From https://github.com/ncbi/egapx: "EGAPx is the publicly accessible version of the updated NCBI Eukaryotic Genome Annotation Pipeline. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

As of 05/20/2025, due to its current implementation of Nextflow, installing EGAPx centrally would potentially run the risk of overwriting important files (this might change in the future when the software is out of alpha/beta). As such, this page exists to outline the steps needed to run EGAPx from the /home directory.

First, start an interactive job and, in your /home directory, clone the repository and then cd to the new directory that this creates:
<pre class="gcommand">
git clone https://github.com/ncbi/egapx.git
cd egapx
</pre>

Since the dependencies for EGAPx are already installed, you don't need to create a new virtual environment; simply load the relevant software modules:
<pre class="gcommand">
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0
</pre>

Now you need to run one of the test examples provided by the developers in order to acquire the necessary config files:
<pre class="gcommand">
python3 ui/egapx.py ./examples/input_D_farinae_small.yaml -o example_out
</pre>

Note that it isn't strictly necessary to include "python3" at the start of the command, so long as the 'egapx.py' script in the 'ui/' directory has executable permissions (which it does by default). Running the execution script the first time will put all of the config files into a new subdirectory in your working directory ('egapx/') called 'egapx_config'. Of these, the one to use is 'slurm.config', which you will need to open with your preferred text editor and make the following changes:

The line
<pre class="gscript">
cacheDir = "/data/$USER/singularity"
</pre>
should be changed to
<pre class="gscript">
cacheDir = "$PWD/singularity"
</pre>

while the section
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="/data/$USER/singularity"
SINGULARITY_TMPDIR="/data/$USER/tmp"
}
</pre>

should be changed to:
<pre class="gscript">
env {
SINGULARITY_CACHEDIR="$PWD/singularity"
SINGULARITY_TMPDIR="/$PWD/tmp"
}
</pre>

Finally, in the section called "process," there are only three things that need to be un-commented (remove the "//" at the start of the line) and changed as follows:
<pre class="gscript">
queue = 'batch'
queueSize = 20
</pre>
and
<pre class="gscript">
clusterOptions = ' --ntasks=1 '
</pre>

After this, you can create a job submission script that looks like this:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --job-name=ExampleJobName 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --cpus-per-task=1 
<nowiki>#</nowiki>SBATCH --mem=4G 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=%x.%j.out 
<nowiki>#</nowiki>SBATCH --error=%x.%j.err 

ml purge
ml Nextflow/23.10.1
ml Python/3.11.3-GCCcore-12.3.0
ml PyYAML/6.0-GCCcore-12.3.0

python3 PATH/to/egapx/ui/egapx.py PATH/to/input_and_database_options.yaml -e slurm -o PATH/to/Output_Directory

</div>
In the real submission script, all the above underlined values need to be replaced by the actual values, particularly the relevant paths. Please note that, while it is perfectly fine to use EGAPx in your /home directory, the input and output directories (i.e., where files will be read from and written to) should NOT be located in your /home directory; please use somewhere else for input (if using local data) and output files, such your /scratch directory.
 
 
Example input YAML file:
<pre class="gcommand">
genome: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/020/809/275/GCF_020809275.1_ASM2080927v1/GCF_020809275.1_ASM2080927v1_genomic.fna.gz
taxid: 6954
reads:
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR8506572.2
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.1
- https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/EGAP/data/Dermatophagoides_farinae_small/SRR9005248.2
</pre>

(Please see https://github.com/ncbi/egapx?tab=readme-ov-file#input-data-format for more information on the correct formatting for input data, and https://github.com/ncbi/egapx?tab=readme-ov-file#input-example for more examples.)

=== Documentation ===

Details and references are at: https://github.com/ncbi/egapx

Full help options
<pre class="gcommand">
$ ui/egapx.py -h
usage: egapx.py [-h] [-o OUTPUT] [-e EXECUTOR] [-c CONFIG_DIR] [-w WORKDIR] [-r REPORT] [-n] [-st] [-so] [-lo] [-ot ORTHO_TAXID] [-dl] [-lc LOCAL_CACHE] [-q] [-v] [-V] [-fn FUNC_NAME] [filename]

Main script for EGAPx

options:
-h, --help show this help message and exit
-e EXECUTOR, --executor EXECUTOR
Nextflow executor, one of docker, singularity, aws, or local (for NCBI internal use only). Uses corresponding Nextflow config file
-c CONFIG_DIR, --config-dir CONFIG_DIR
Directory for executor config files, default is ./egapx_config. Can be also set as env EGAPX_CONFIG_DIR
-w WORKDIR, --workdir WORKDIR
Working directory for cloud executor
-r REPORT, --report REPORT
Report file prefix for report (.report.html) and timeline (.timeline.html) files, default is in output directory
-n, --dry-run
-st, --stub-run
-so, --summary-only Print result statistics only if available, do not compute result
-lo, --logs-only Collect execution logs if available, put them in output directory, do not compute result
-ot ORTHO_TAXID, --ortho-taxid ORTHO_TAXID
Taxid of reference data for orthology tasks
-lc LOCAL_CACHE, --local-cache LOCAL_CACHE
Where to store the downloaded files
-q, --quiet
-v, --verbose
-V, --version Report software version
-fn FUNC_NAME, --func_name FUNC_NAME
func_name

run:
filename YAML file with input: section with at least genome: and reads: parameters
-o OUTPUT, --output OUTPUT
Output path

download:
-dl, --download-only Download external files to local storage, so that future runs can be isolated
</pre>

[[#top|Back to Top]]

Software

2025-05-20T17:57:43Z

Jordan: /* Partial List of Installed Software */

On Sapelo2 and on the Teaching cluster users have the option to install their own software or use software installed centrally by the GACRC staff. Detailed information on how to find and access software packages installed on Sapelo2 are available at the [[Software on Sapelo2]] page and on the teaching cluster at the [[Software on Teaching Cluster]] page.

Below is a very limited list of software packages installed on Sapelo2 and on the Teaching cluster. We only intend to make an application page for software packages that requires extra documentation not covered in [[Software on Sapelo2]] or [[Software on Teaching Cluster]].

To view a list of all modules installed on Sapelo2/Teaching, along with a short description of each module, please run the following command on Sapelo2/Teaching:
<pre class="gcommand">
module spider
</pre>
You can also use the command above to search for a specific application. For more information, please see [[Lmod]].

The '''file transfer''' nodes ('''xfer''' nodes) do not have environment modules installed. File transfer utilities are installed centrally on the xfer nodes and available on the users' default PATH. Such utilities include bs (basespace-cli), fpsync, rclone, rsync, etc.
https://wiki.gacrc.uga.edu/wiki/Main_Page

'''Note:''' Although we have not made individual software pages for the applications installed on Sapelo2, there are currently over 1300 software packages installed there. For more information, please see [[Software on Sapelo2]].

==Partial List of Installed Software==
Last updated '''{{REVISIONYEAR}}-{{REVISIONMONTH}}-{{REVISIONDAY2}}'''. This is a partial list of installed software so there may be software or versions of software available on Sapelo2 which are not included in this table.
'''Note:''' Click on the icons to the right of the column headings to sort the table.
[[#0|0-9]] [[#A|A]] [[#B|B]] [[#C|C]] [[#D|D]] [[#E|E]] [[#F|F]] [[#G|G]] [[#H|H]] [[#I|I]] [[#J|J]] [[#K|K]] [[#L|L]] [[#M|M]] [[#N|N]] [[#O|O]] [[#P|P]] [[#Q|Q]] [[#R|R]] [[#S|S]] [[#T|T]] [[#U|U]] [[#V|V]] [[#W|W]] [[#X|X]] [[#Y|Y]] [[#Z|Z]]
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" width="200px"| Name
! scope="col" class="unsortable" width="240px"| Version
! scope="col" width="160px"| Category
! scope="col" width="60px"| Cluster
! scope="col" width="20px"| Top
|-













| [[AlphaFold-Sapelo2|AlphaFold 2]] || 2.2.4, 2.3.1, 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->
| [[AlphaFold3-Sapelo2|AlphaFold 3]] || 3.0.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[AMBER-Sapelo2|AMBER]] || 22 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[anvi'o-Sapelo2|anvi'o]] || 4, 5.1, 5.3, 5.4, 6.1, 7, 7.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
















| [[basespace-cli-Sapelo2|basespace-cli]] || 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










|| [[Beast-Sapelo2|Beast]] || 1.10.4, 2.5.1, 2.6.3, 2.7.3, 2.7.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










| [[BioPython-Sapelo2|BioPython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[Biopython-Teaching|Biopython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-




| [[BLAST+-Sapelo2|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] ||[[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST+-Teaching|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[BLAST-Sapelo2|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST-Teaching|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Bowtie2-Sapelo2|Bowtie2]] || 2.2.3, 2.3.3, 2.3.4.1, 2.3.4.2, 2.3.5.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Bowtie2-Teaching|Bowtie2]] || 2.4.1, 2.4.4, 2.4.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


| [[BRAKER-Sapelo2|BRAKER]] || 2.1.6, 3.0.3, 3.0.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



| [[BUSCO-Sapelo2|BUSCO]] || 2.0, 4.0.5, 5.4.7, 5.5.0, 5.8.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[Cactus-Sapelo2|Cactus]] || 1.2.3, 2.0.3, 2.4.3, 2.4.4, 2.5.0, 2.6.0, 2.6.7, 2.6.9, 2.7.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Canu-Sapelo2|Canu]] || 2.2||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[CellRanger-ATAC-Sapelo2|CellRanger-ATAC]] || 2.1.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[Chimera-Teaching|Chimera]] || 1.16 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-























| [[cryoSPARC-Sapelo2|CryoSPARC]] || 3.3.1 || [[:Category:Engineering|Engineering]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Cromwell-Sapelo2|Cromwell]] || 56 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|[[CUDA-Sapelo2|CUDA]] || 11.3.1, 11.4.1, 11.7.0, 12.0.0, 12.1.1, 12.2.0, 12.4.0, 12.6.0 ||[[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Cytoscape-Sapelo2|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Cytoscape-Teaching|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-



























|-->
| [[dMRIharmonization-Sapelo2|dMRIharmonization]] || 20240227 || [[:Category:Engineering|Engineering]]|| [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-->





| [[EGAPx-Sapelo2|EGAPx]] || N/A || [[:Category:Bioinformatics|Bioinformatics]] || N/A || [[File:Up20.png|20px|link=#top]]
|- -->


| [[EIGENSOFT-Sapelo2|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[EIGENSOFT-Teaching|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Flankophile-Sapelo2|Flankophile]] || 0.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-






































|-
| [[Funannotate-Sapelo2|Funannotate]] || 1.8.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[GAUSSIAN-Sapelo2|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[GAUSSIAN-Teaching|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[GaussView-Sapelo2|GaussView]] || 5, 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[GaussView-Teaching|GaussView]] || 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[GEM-Sapelo2|GEM]] || 1.5.1 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[GeneMarkES-Sapelo2|GeneMarkET]] || 4.71 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





























|[[gocryptfs-Sapelo2|gocryptfs]] || 2.5.2 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[GROMACS-Sapelo2 |GROMACS]] || 2021.5, 2023.1, 2023.4 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


















| [[HISAT2-Sapelo2|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[HISAT2-Teaching|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[HpcGridRunner-Sapelo2|HPCGridRunner]] || 1.0.2 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










|-
| [[iCommands-Sapelo2|iCommands]] || 4.2.8 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[InterProScan-Sapelo2|InterProScan]] || 5.63-95.0, 5.64-96.0, 5.66-98.0 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[IQ-Tree-Sapelo2|IQ-Tree]] || 1.6.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[IQ-Tree-Teaching|IQ-Tree]] || 1.6.12|| [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-








| [[Java-Teaching|Java]] || 1.8.0_241, 11.0.16, 13.0.2, 15.0.1, 17.0.6 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Jupyter-Sapelo2|Jupyter]] || 6.4.12, 6.5.2, 6.5.4, 6.5.6, 7.0.3, 7.2.0 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Jupyter-Teaching|Jupyter]] || 6.4.12 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[LAMMPS-Sapelo2|LAMMPS]] || 23Jun2022 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



































| [[Magma-Sapelo2|Magma]] || 2.27-1 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-


| [[Maker-Sapelo2|Maker]] || 3.01.04 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[MATLAB-Sapelo2|MATLAB]] || R2023a, R2023b || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[Medusa-Sapelo2 |Medusa]] || 1.6 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-













| [[MetaPhlAn4-Sapelo2|MetaPhlAn4]] || 4.0.3.1, 4.0.6, 4.1.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
























| [[MOTECC_qmdcp-Teaching|MOTECC_qmdcp]] || 90 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Mothur-Sapelo2|Mothur]] || 1.45.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->




















































| [[Ont-Guppy-Sapelo2|Ont-Guppy]] || 6.5.7 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[ORCA-Sapelo2 |ORCA]] || 4.2.1, 5.0.4 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




|[[ORP-Sapelo2|ORP]] || 2.2.6, 2.2.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[OrthoFinder-Sapelo2|OrthoFinder]] || 2.5.4, 2.5.5 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[PAML-Teaching|PAML]] || 4.9j, 4.10.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->























| [[Perl-Teaching|Perl]] || 5.30.0, 5.30.2, 5.32.0, 5.32.1, 5.34.0, 5.34.1, 5.36.0, 5.36.1 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[Phenix-Sapelo2|Phenix]] || 1.21.2-5419 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

























































|-
| [[Python-Sapelo2 |Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Python-Teaching|Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->






| [[QIIME2-Sapelo2|QIIME2]] || 2020.6, 2023.2, 2023.5.1, 2023.7, 2023.9, 2024.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[R-Sapelo2 |R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 ||[[:Category:Statistics|Statistics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[R-Teaching|R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 || [[:Category:Statistics|Statistics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[racon-Sapelo2 |Racon]] || 1.5.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[RAxML-NG-Sapelo2 |RAxML-NG]] || 1.2.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Sapelo2 |RAxML]] || 8.2.11, 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Teaching|RAxML]] || 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

















| [[RevBayes-Teaching|RevBayes]] || 1.1.0, 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Rosetta-Sapelo2 |Rosetta]] || 2021.16.61629, 2022.46.334 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[Rstudio-Sapelo2|Rstudio]] || 2023.06.1-524 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[SAMtools-Teaching|SAMtools]] || 0.1.20, 1.6, 1.14, 1.16.1, 1.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

| [[SequenceTubeMap-Sapelo2|sequenceTubeMap]] || 20230906 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->









































| [[snpEff-Sapelo2|snpEff]] || 5.0e || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





















| [[SRAToolKit-Sapelo2 |SRAToolKit]] || 3.0.1, 3.0.3 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[StringTie-Teaching|StringTie]] || 2.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Structure-Sapelo2|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Structure-Teaching|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


































| [[Trinity-Sapelo2|Trinity]] || 2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[UBCG-Sapelo2|UBCG]] || 3.0
|[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-














| [[Velvet-Sapelo2|Velvet]] || 1.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->






















|}
[[#top|Back To Top]] [[File:Up20.png|20px|link=#top]]

[[Category:Sapelo2]][[Category:Teaching]][[Category:Software]]

BRAKER-Sapelo2

2025-05-06T17:13:57Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
2.1.6, 3.0.3, 3.0.8

=== Author / Distributor ===
[https://github.com/Gaius-Augustus/BRAKER Gaius-Augustus]

=== Description ===
"BRAKER is a pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes."
More details are at [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

'''Version 2.1.6'''

Version 2.1.6, installed at

* /apps/eb/BRAKER/2.1.6-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/2.1.6-foss-2022a
</div>

'''Version 3.0.3'''

Version 3.0.3 installed at

* /apps/eb/BRAKER/3.0.3-foss-2022a-long_reads/
* /apps/eb/BRAKER/3.0.3-foss-2022a/
* /apps/eb/BRAKER/3.0.3-long_reads/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

'''Version 3.0.8'''

Version 3.0.8 installed at

* /apps/eb/BRAKER/3.0.8-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.8-foss-2022a
</div>

'''Notes about Dependencies, Etc.'''

BRAKER is a gene prediction pipeline that predicts the genes in an input genome using either just the genome itself (referred to as "ab initio" mode but in more modern parlance, the equivalent of de novo), or the input genome with RNAseq data, protein data, or a combination of RNAseq and protein data to support and solidify gene predictions. If it detects the input of both RNA and proteins as supporting data, BRAKER will implement GeneMark-ETP rather than the other iterations of GeneMark; one of the scripts that GeneMark-ETP uses to filter results calls DIAMOND (unless BLAST is explicitly specified by the user) without passing the value of the user-specified "--threads" option (see full documentation below) to it, which will cause DIAMOND to auto-detect the number of CPUs present on the node and attempt to run this many threads regardless of how many CPUs have been allocated for the job. Therefore, it is strongly recommended that if you run BRAKER with both RNA and protein data, you request a full 128 CPUs for your BRAKER job; while this may cause your job to wait in the queue for longer, numerous BRAKER jobs have been observed to get "stuck" or outright fail because of DIAMOND attempting to run 128 threads on only a few CPUs. Regardless of the type(s) of input data you use, BRAKER will use one iteration or another of the GeneMark gene prediction software, and thus in order to use BRAKER, you need to have downloaded the key file for GeneMarker and put it into your home directory. Please follow instructions found at [[GeneMarkES-Sapelo2]].

Furthermore, BRAKER makes use of numerous external tools (as can be seen in the "CONFIGURATION OPTIONS" section of the documentation below); while working versions of these are all included with the modules listed above, you also have the option to use your own versions of these (see the example submission script below):

Here is an example of a shell script sub.sh to run BRAKER using a protein database from the batch queue along with your own version of ProtHint (in place of the default one, which does not need to be specified):

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=BRAKER_Job 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=16gb 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load BRAKER/3.0.8-foss-2022a 

braker.pl --genome=Newly_assembled_genome.fasta --prot_seq=Protein_database.faa --PROTHINT_PATH=/Path/to/MY/version/of/ProtHint 
</div>

In the real submission script, be sure to replace the above underlined values with proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load BRAKER/3.0.8-foss-2022a
braker.pl

DESCRIPTION

braker.pl Pipeline for predicting genes with GeneMark-EX and AUGUSTUS with
RNA-Seq and/or proteins

SYNOPSIS

braker.pl [OPTIONS] --genome=genome.fa {--bam=rnaseq.bam | --prot_seq=prot.fa}

INPUT FILE OPTIONS

--genome=genome.fa fasta file with DNA sequences
--bam=rnaseq.bam bam file with spliced alignments from
RNA-Seq
--prot_seq=prot.fa A protein sequence file in multi-fasta
format used to generate protein hints.
Unless otherwise specified, braker.pl will
run in "EP mode" or "ETP mode which uses
ProtHint to generate protein hints and
GeneMark-EP+ or GeneMark-ETP to
train AUGUSTUS.
--hints=hints.gff Alternatively to calling braker.pl with a
bam or protein fasta file, it is possible to
call it with a .gff file that contains
introns extracted from RNA-Seq and/or
protein hints (most frequently coming
from ProtHint). If you wish to use the
ProtHint hints, use its
"prothint_augustus.gff" output file.
This flag also allows the usage of hints
from additional extrinsic sources for gene
prediction with AUGUSTUS. To consider such
additional extrinsic information, you need
to use the flag --extrinsicCfgFiles to
specify parameters for all sources in the
hints file (including the source "E" for
intron hints from RNA-Seq).
In ETP mode, this option can be used together
with --geneMarkGtf and --traingenes to provide
BRAKER with results of a previous GeneMark-ETP
run, so that the GeneMark-ETP step can be
skipped. In this case, specify the hintsfile of
a previous BRAKER run here, or generate a
hintsfile from the GeneMark-ETP working
directory with the script get_etp_hints.py.
--rnaseq_sets_ids=SRR1111,SRR1115 IDs of RNA-Seq sets that are either in
one of the directories specified with
--rnaseq_sets_dir, or that can be downloaded
from SRA. If you want to use local files, you
can use unaligned reads in FASTQ format
(they have to be named ID.fastq if unpaired or
ID_1.fastq, ID_2.fastq if paired), or aligned reads
as a BAM file (named ID.bam).
--rnaseq_sets_dir=/path/to/rna_dir1 Locations where the local files of RNA-Seq data
reside that were specified with --rnaseq_sets_ids.

FREQUENTLY USED OPTIONS

--species=sname Species name. Existing species will not be
overwritten. Uses Sp_1 etc., if no species
is assigned
--AUGUSTUS_ab_initio output ab initio predictions by AUGUSTUS
in addition to predictions with hints by
AUGUSTUS
--softmasking_off Turn off softmasking option (enables by
default, discouraged to disable!)
--esmode Run GeneMark-ES (genome sequence only) and
train AUGUSTUS on long genes predicted by
GeneMark-ES. Final predictions are ab initio
--gff3 Output in GFF3 format (default is gtf
format)
--threads Specifies the maximum number of threads that
can be used during computation. Be aware:
optimize_augustus.pl will use max. 8
threads; augustus will use max. nContigs in
--genome=file threads.
--workingdir=/path/to/wd/ Set path to working directory. In the
working directory results and temporary
files are stored
--nice Execute all system calls within braker.pl
and its submodules with bash "nice"
(default nice value)
--alternatives-from-evidence=true Output alternative transcripts based on
explicit evidence from hints (default is
true).
--fungus GeneMark-EX option: run algorithm with
branch point model (most useful for fungal
genomes)
--crf Execute CRF training for AUGUSTUS;
resulting parameters are only kept for
final predictions if they show higher
accuracy than HMM parameters.
--keepCrf keep CRF parameters even if they are not
better than HMM parameters
--makehub Create track data hub with make_hub.py
for visualizing BRAKER results with the
UCSC GenomeBrowser
--busco_lineage=lineage If you provide a BUSCO lineage, BRAKER will
run compleasm on genome level to generate hints
from BUSCO to enhance BUSCO discovery in the
protein set. Also, if you provide a BUSCO
lineage, BRAKER will run compleasm to assess
the protein sets that go into TSEBRA combination,
and will determine the TSEBRA mode to maximize
BUSCO. Do not provide a busco_lineage if you
want to determina natural BUSCO sensivity of
BRAKER!
--email E-mail address for creating track data hub
--version Print version number of braker.pl
--help Print this help message

CONFIGURATION OPTIONS (TOOLS CALLED BY BRAKER)

--AUGUSTUS_CONFIG_PATH=/path/ Set path to config directory of AUGUSTUS
(if not specified as environment
variable). BRAKER1 will assume that the
directories ../bin and ../scripts of
AUGUSTUS are located relative to the
AUGUSTUS_CONFIG_PATH. If this is not the
case, please specify AUGUSTUS_BIN_PATH
(and AUGUSTUS_SCRIPTS_PATH if required).
The braker.pl commandline argument
--AUGUSTUS_CONFIG_PATH has higher priority
than the environment variable with the
same name.
--AUGUSTUS_BIN_PATH=/path/ Set path to the AUGUSTUS directory that
contains binaries, i.e. augustus and
etraining. This variable must only be set
if AUGUSTUS_CONFIG_PATH does not have
../bin and ../scripts of AUGUSTUS relative
to its location i.e. for global AUGUSTUS
installations. BRAKER1 will assume that
the directory ../scripts of AUGUSTUS is
located relative to the AUGUSTUS_BIN_PATH.
If this is not the case, please specify
--AUGUSTUS_SCRIPTS_PATH.
--AUGUSTUS_SCRIPTS_PATH=/path/ Set path to AUGUSTUS directory that
contains scripts, i.e. splitMfasta.pl.
This variable must only be set if
AUGUSTUS_CONFIG_PATH or AUGUSTUS_BIN_PATH
do not contains the ../scripts directory
of AUGUSTUS relative to their location,
i.e. for special cases of a global
AUGUSTUS installation.
--BAMTOOLS_PATH=/path/to/ Set path to bamtools (if not specified as
environment BAMTOOLS_PATH variable). Has
higher priority than the environment
variable.
--GENEMARK_PATH=/path/to/ Set path to GeneMark-ET (if not specified
as environment GENEMARK_PATH variable).
Has higher priority than environment
variable.
--SAMTOOLS_PATH=/path/to/ Optionally set path to samtools (if not
specified as environment SAMTOOLS_PATH
variable) to fix BAM files automatically,
if necessary. Has higher priority than
environment variable.
--PROTHINT_PATH=/path/to/ Set path to the directory with prothint.py.
(if not specified as PROTHINT_PATH
environment variable). Has higher priority
than environment variable.
--DIAMOND_PATH=/path/to/diamond Set path to diamond, this is an alternative
to NCIB blast; you only need to specify one
out of DIAMOND_PATH or BLAST_PATH, not both.
DIAMOND is a lot faster that BLAST and yields
highly similar results for BRAKER.
--BLAST_PATH=/path/to/blastall Set path to NCBI blastall and formatdb
executables if not specified as
environment variable. Has higher priority
than environment variable.
--COMPLEASM_PATH=/path/to/compleasm Set path to compleasm (if not specified as
environment variable). Has higher priority
than environment variable.
--PYTHON3_PATH=/path/to Set path to python3 executable (if not
specified as envirnonment variable and if
executable is not in your $PATH).
--JAVA_PATH=/path/to Set path to java executable (if not
specified as environment variable and if
executable is not in your $PATH), only
required with flags --UTR=on and --addUTR=on
--GUSHR_PATH=/path/to Set path to gushr.py exectuable (if not
specified as an environment variable and if
executable is not in your $PATH), only required
with the flags --UTR=on and --addUTR=on
--MAKEHUB_PATH=/path/to Set path to make_hub.py (if option --makehub
is used).
--CDBTOOLS_PATH=/path/to cdbfasta/cdbyank are required for running
fix_in_frame_stop_codon_genes.py. Usage of
that script can be skipped with option
'--skip_fixing_broken_genes'.

EXPERT OPTIONS

--augustus_args="--some_arg=bla" One or several command line arguments to
be passed to AUGUSTUS, if several
arguments are given, separate them by
whitespace, i.e.
"--first_arg=sth --second_arg=sth".
--skipGeneMark-ES Skip GeneMark-ES and use provided
GeneMark-ES output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ET Skip GeneMark-ET and use provided
GeneMark-ET output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-EP Skip GeneMark-EP and use provided
GeneMark-EP output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ETP Skip GeneMark-ETP and use provided
GeneMark-ETP output (e.g. provided with
--gmetp_results_dir=GeneMark-ETP/)
--geneMarkGtf=file.gtf If skipGeneMark-ET is used, braker will by
default look in the working directory in
folder GeneMarkET for an already existing
gtf file. Instead, you may provide such a
file from another location. If geneMarkGtf
option is set, skipGeneMark-ES/ET/EP/ETP is
automatically also set. Note that gene and
transcript ids in the final output may not
match the ids in the input genemark.gtf
because BRAKER internally re-assigns these
ids.
In ETP mode, this option hast to be used together
with --traingenes and --hints to provide BRAKER
with results of a previous GeneMark-ETP run.
--gmetp_results_dir Location of results from a previous
GeneMark-ETP run, which will be used to
skip the GeneMark-ETP step. This option
can be used instead of --geneMarkGtf,
--traingenes, and --hints to skip GeneMark.
--rounds The number of optimization rounds used in
optimize_augustus.pl (default 5)
--skipAllTraining Skip GeneMark-EX (training and
prediction), skip AUGUSTUS training, only
runs AUGUSTUS with pre-trained and already
existing parameters (not recommended).
Hints from input are still generated.
This option automatically sets
--useexisting to true.
--useexisting Use the present config and parameter files
if they exist for 'species'; will overwrite
original parameters if BRAKER performs
an AUGUSTUS training.
--filterOutShort It may happen that a "good" training gene,
i.e. one that has intron support from
RNA-Seq in all introns predicted by
GeneMark-EX, is in fact too short. This flag
will discard such genes that have
supported introns and a neighboring
RNA-Seq supported intron upstream of the
start codon within the range of the
maximum CDS size of that gene and with a
multiplicity that is at least as high as
20% of the average intron multiplicity of
that gene.
--skipOptimize Skip optimize parameter step (not
recommended).
--skipIterativePrediction Skip iterative prediction in --epmode (does
not affect other modes, saves a bit of runtime)
--skipGetAnnoFromFasta Skip calling the python3 script
getAnnoFastaFromJoingenes.py from the
AUGUSTUS tool suite. This script requires
python3, biopython and re (regular
expressions) to be installed. It produces
coding sequence and protein FASTA files
from AUGUSTUS gene predictions and provides
information about genes with in-frame stop
codons. If you enable this flag, these files
will not be produced and python3 and
the required modules will not be necessary
for running brkaker.pl.
--skip_fixing_broken_genes If you do not have python3, you can choose
to skip the fixing of stop codon including
genes (not recommended).
--eval=reference.gtf Reference set to evaluate predictions
against (using evaluation scripts from GaTech)
--eval_pseudo=pseudo.gff3 File with pseudogenes that will be excluded
from accuracy evaluation (may be empty file)
--AUGUSTUS_hints_preds=s File with AUGUSTUS hints predictions; will
use this file as basis for UTR training;
only UTR training and prediction is
performed if this option is given.
--flanking_DNA=n Size of flanking region, must only be
specified if --AUGUSTUS_hints_preds is given
(for UTR training in a separate braker.pl
run that builds on top of an existing run)
--verbosity=n 0 -> run braker.pl quiet (no log)
1 -> only log warnings
2 -> also log configuration
3 -> log all major steps
4 -> very verbose, log also small steps
--downsampling_lambda=d The distribution of introns in training
gene structures generated by GeneMark-EX
has a huge weight on single-exon and
few-exon genes. Specifying the lambda
parameter of a poisson distribution will
make braker call a script for downsampling
of training gene structures according to
their number of introns distribution, i.e.
genes with none or few exons will be
downsampled, genes with many exons will be
kept. Default value is 2.
If you want to avoid downsampling, you have
to specify 0.
--checkSoftware Only check whether all required software
is installed, no execution of BRAKER
--nocleanup Skip deletion of all files that are typically not
used in an annotation project after
running braker.pl. (For tracking any
problems with a braker.pl run, you
might want to keep these files, therefore
nocleanup can be activated.)

DEVELOPMENT OPTIONS (PROBABLY STILL DYSFUNCTIONAL)

--splice_sites=patterns list of splice site patterns for UTR
prediction; default: GTAG, extend like this:
--splice_sites=GTAG,ATAC,...
this option only affects UTR training
example generation, not gene prediction
by AUGUSTUS
--overwrite Overwrite existing files (except for
species parameter files) Beware, currently
not implemented properly!
--extrinsicCfgFiles=file1,file2,... Depending on the mode in which braker.pl
is executed, it may require one ore several
extrinsicCfgFiles. Don't use this option
unless you know what you are doing!
--stranded=+,-,+,-,... If UTRs are trained, i.e.~strand-specific
bam-files are supplied and coverage
information is extracted for gene prediction,
create stranded ep hints. The order of
strand specifications must correspond to the
order of bam files. Possible values are
+, -, .
If stranded data is provided, ONLY coverage
data from the stranded data is used to
generate UTR examples! Coverage data from
unstranded data is used in the prediction
step, only.
The stranded label is applied to coverage
data, only. Intron hints are generated
from all libraries treated as "unstranded"
(because splice site filtering eliminates
intron hints from the wrong strand, anyway).
--optCfgFile=ppx.cfg Optional custom config file for AUGUSTUS
for running PPX (currently not
implemented)
--grass Switch this flag on if you are using braker.pl
for predicting genes in grasses with
GeneMark-EX. The flag will enable
GeneMark-EX to handle GC-heterogenicity
within genes more properly.
NOTHING IMPLEMENTED FOR GRASS YET!
--transmasked_fasta=file.fa Transmasked genome FASTA file for GeneMark-EX
(to be used instead of the regular genome
FASTA file).
--min_contig=INT Minimal contig length for GeneMark-EX, could
for example be set to 10000 if transmasked_fasta
option is used because transmasking might
introduce many very short contigs.
--translation_table=INT Change translation table from non-standard
to something else.
DOES NOT WORK YET BECAUSE BRAKER DOESNT
SWITCH TRANSLATION TABLE FOR GENEMARK-EX, YET!
--gc_probability=DECIMAL Probablity for donor splice site pattern GC
for gene prediction with GeneMark-EX,
default value is 0.001
--gm_max_intergenic=INT Adjust maximum allowed size of intergenic
regions in GeneMark-EX. If not used, the value
is automatically determined by GeneMark-EX.
--traingenes=file.gtf Training genes that are used instead of training
genes generated with GeneMark.
In ETP mode, this option can be used together
with --geneMarkGtf and --hints to provide BRAKER
with results of a previous GeneMark-ETP run, so
that the GeneMark-ETP step can be skipped.
In this case, use training.gtf from that run as
argument.
--UTR=on create UTR training examples from RNA-Seq
coverage data; requires options
--bam=rnaseq.bam.
Alternatively, if UTR parameters already
exist, training step will be skipped and
those pre-existing parameters are used.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!
--addUTR=on Adds UTRs from RNA-Seq coverage data to
augustus.hints.gtf file. Does not perform
training of AUGUSTUS or gene prediction with
AUGUSTUS and UTR parameters.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!

EXAMPLE

To run with RNA-Seq

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--bam=accepted_hits.bam
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=rnaseq.gff

To run with protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=prothint_augustus.gff

To run with RNA-Seq and protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --rnaseq_sets_ids=id_rnaseq1,id_rnaseq2 \
--rnaseq_sets_dir=/path/to/local/rnaseq/files
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --bam=id_rnaseq1.bam,id_rnaseq2.bam
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== System ===
64-bit Linux

BRAKER-Sapelo2

2025-05-06T17:13:37Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
2.1.6, 3.0.3, 3.0.8

=== Author / Distributor ===
[https://github.com/Gaius-Augustus/BRAKER Gaius-Augustus]

=== Description ===
"BRAKER is a pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes."
More details are at [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

'''Version 2.1.6'''

Version 2.1.6, installed at

* /apps/eb/BRAKER/2.1.6-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/2.1.6-foss-2022a
</div>

'''Version 3.0.3'''

Version 3.0.3 installed at

* /apps/eb/BRAKER/3.0.3-foss-2022a-long_reads/
* /apps/eb/BRAKER/3.0.3-foss-2022a/
* /apps/eb/BRAKER/3.0.3-long_reads/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

'''Version 3.0.8'''

Version 3.0.8 installed at

* /apps/eb/BRAKER/3.0.8-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.8-foss-2022a
</div>

'''Notes about Dependencies, Etc.'''

BRAKER is a gene prediction pipeline that predicts the genes in an input genome using either just the genome itself (referred to as "ab initio mode but in more modern parlance, the equivalent of de novo), or the input genome with RNAseq data, protein data, or a combination of RNAseq and protein data to support and solidify gene predictions. If it detects the input of both RNA and proteins as supporting data, BRAKER will implement GeneMark-ETP rather than the other iterations of GeneMark; one of the scripts that GeneMark-ETP uses to filter results calls DIAMOND (unless BLAST is explicitly specified by the user) without passing the value of the user-specified "--threads" option (see full documentation below) to it, which will cause DIAMOND to auto-detect the number of CPUs present on the node and attempt to run this many threads regardless of how many CPUs have been allocated for the job. Therefore, it is strongly recommended that if you run BRAKER with both RNA and protein data, you request a full 128 CPUs for your BRAKER job; while this may cause your job to wait in the queue for longer, numerous BRAKER jobs have been observed to get "stuck" or outright fail because of DIAMOND attempting to run 128 threads on only a few CPUs. Regardless of the type(s) of input data you use, BRAKER will use one iteration or another of the GeneMark gene prediction software, and thus in order to use BRAKER, you need to have downloaded the key file for GeneMarker and put it into your home directory. Please follow instructions found at [[GeneMarkES-Sapelo2]].

Furthermore, BRAKER makes use of numerous external tools (as can be seen in the "CONFIGURATION OPTIONS" section of the documentation below); while working versions of these are all included with the modules listed above, you also have the option to use your own versions of these (see the example submission script below):

Here is an example of a shell script sub.sh to run BRAKER using a protein database from the batch queue along with your own version of ProtHint (in place of the default one, which does not need to be specified):

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=BRAKER_Job 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=16gb 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load BRAKER/3.0.8-foss-2022a 

braker.pl --genome=Newly_assembled_genome.fasta --prot_seq=Protein_database.faa --PROTHINT_PATH=/Path/to/MY/version/of/ProtHint 
</div>

In the real submission script, be sure to replace the above underlined values with proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load BRAKER/3.0.8-foss-2022a
braker.pl

DESCRIPTION

braker.pl Pipeline for predicting genes with GeneMark-EX and AUGUSTUS with
RNA-Seq and/or proteins

SYNOPSIS

braker.pl [OPTIONS] --genome=genome.fa {--bam=rnaseq.bam | --prot_seq=prot.fa}

INPUT FILE OPTIONS

--genome=genome.fa fasta file with DNA sequences
--bam=rnaseq.bam bam file with spliced alignments from
RNA-Seq
--prot_seq=prot.fa A protein sequence file in multi-fasta
format used to generate protein hints.
Unless otherwise specified, braker.pl will
run in "EP mode" or "ETP mode which uses
ProtHint to generate protein hints and
GeneMark-EP+ or GeneMark-ETP to
train AUGUSTUS.
--hints=hints.gff Alternatively to calling braker.pl with a
bam or protein fasta file, it is possible to
call it with a .gff file that contains
introns extracted from RNA-Seq and/or
protein hints (most frequently coming
from ProtHint). If you wish to use the
ProtHint hints, use its
"prothint_augustus.gff" output file.
This flag also allows the usage of hints
from additional extrinsic sources for gene
prediction with AUGUSTUS. To consider such
additional extrinsic information, you need
to use the flag --extrinsicCfgFiles to
specify parameters for all sources in the
hints file (including the source "E" for
intron hints from RNA-Seq).
In ETP mode, this option can be used together
with --geneMarkGtf and --traingenes to provide
BRAKER with results of a previous GeneMark-ETP
run, so that the GeneMark-ETP step can be
skipped. In this case, specify the hintsfile of
a previous BRAKER run here, or generate a
hintsfile from the GeneMark-ETP working
directory with the script get_etp_hints.py.
--rnaseq_sets_ids=SRR1111,SRR1115 IDs of RNA-Seq sets that are either in
one of the directories specified with
--rnaseq_sets_dir, or that can be downloaded
from SRA. If you want to use local files, you
can use unaligned reads in FASTQ format
(they have to be named ID.fastq if unpaired or
ID_1.fastq, ID_2.fastq if paired), or aligned reads
as a BAM file (named ID.bam).
--rnaseq_sets_dir=/path/to/rna_dir1 Locations where the local files of RNA-Seq data
reside that were specified with --rnaseq_sets_ids.

FREQUENTLY USED OPTIONS

--species=sname Species name. Existing species will not be
overwritten. Uses Sp_1 etc., if no species
is assigned
--AUGUSTUS_ab_initio output ab initio predictions by AUGUSTUS
in addition to predictions with hints by
AUGUSTUS
--softmasking_off Turn off softmasking option (enables by
default, discouraged to disable!)
--esmode Run GeneMark-ES (genome sequence only) and
train AUGUSTUS on long genes predicted by
GeneMark-ES. Final predictions are ab initio
--gff3 Output in GFF3 format (default is gtf
format)
--threads Specifies the maximum number of threads that
can be used during computation. Be aware:
optimize_augustus.pl will use max. 8
threads; augustus will use max. nContigs in
--genome=file threads.
--workingdir=/path/to/wd/ Set path to working directory. In the
working directory results and temporary
files are stored
--nice Execute all system calls within braker.pl
and its submodules with bash "nice"
(default nice value)
--alternatives-from-evidence=true Output alternative transcripts based on
explicit evidence from hints (default is
true).
--fungus GeneMark-EX option: run algorithm with
branch point model (most useful for fungal
genomes)
--crf Execute CRF training for AUGUSTUS;
resulting parameters are only kept for
final predictions if they show higher
accuracy than HMM parameters.
--keepCrf keep CRF parameters even if they are not
better than HMM parameters
--makehub Create track data hub with make_hub.py
for visualizing BRAKER results with the
UCSC GenomeBrowser
--busco_lineage=lineage If you provide a BUSCO lineage, BRAKER will
run compleasm on genome level to generate hints
from BUSCO to enhance BUSCO discovery in the
protein set. Also, if you provide a BUSCO
lineage, BRAKER will run compleasm to assess
the protein sets that go into TSEBRA combination,
and will determine the TSEBRA mode to maximize
BUSCO. Do not provide a busco_lineage if you
want to determina natural BUSCO sensivity of
BRAKER!
--email E-mail address for creating track data hub
--version Print version number of braker.pl
--help Print this help message

CONFIGURATION OPTIONS (TOOLS CALLED BY BRAKER)

--AUGUSTUS_CONFIG_PATH=/path/ Set path to config directory of AUGUSTUS
(if not specified as environment
variable). BRAKER1 will assume that the
directories ../bin and ../scripts of
AUGUSTUS are located relative to the
AUGUSTUS_CONFIG_PATH. If this is not the
case, please specify AUGUSTUS_BIN_PATH
(and AUGUSTUS_SCRIPTS_PATH if required).
The braker.pl commandline argument
--AUGUSTUS_CONFIG_PATH has higher priority
than the environment variable with the
same name.
--AUGUSTUS_BIN_PATH=/path/ Set path to the AUGUSTUS directory that
contains binaries, i.e. augustus and
etraining. This variable must only be set
if AUGUSTUS_CONFIG_PATH does not have
../bin and ../scripts of AUGUSTUS relative
to its location i.e. for global AUGUSTUS
installations. BRAKER1 will assume that
the directory ../scripts of AUGUSTUS is
located relative to the AUGUSTUS_BIN_PATH.
If this is not the case, please specify
--AUGUSTUS_SCRIPTS_PATH.
--AUGUSTUS_SCRIPTS_PATH=/path/ Set path to AUGUSTUS directory that
contains scripts, i.e. splitMfasta.pl.
This variable must only be set if
AUGUSTUS_CONFIG_PATH or AUGUSTUS_BIN_PATH
do not contains the ../scripts directory
of AUGUSTUS relative to their location,
i.e. for special cases of a global
AUGUSTUS installation.
--BAMTOOLS_PATH=/path/to/ Set path to bamtools (if not specified as
environment BAMTOOLS_PATH variable). Has
higher priority than the environment
variable.
--GENEMARK_PATH=/path/to/ Set path to GeneMark-ET (if not specified
as environment GENEMARK_PATH variable).
Has higher priority than environment
variable.
--SAMTOOLS_PATH=/path/to/ Optionally set path to samtools (if not
specified as environment SAMTOOLS_PATH
variable) to fix BAM files automatically,
if necessary. Has higher priority than
environment variable.
--PROTHINT_PATH=/path/to/ Set path to the directory with prothint.py.
(if not specified as PROTHINT_PATH
environment variable). Has higher priority
than environment variable.
--DIAMOND_PATH=/path/to/diamond Set path to diamond, this is an alternative
to NCIB blast; you only need to specify one
out of DIAMOND_PATH or BLAST_PATH, not both.
DIAMOND is a lot faster that BLAST and yields
highly similar results for BRAKER.
--BLAST_PATH=/path/to/blastall Set path to NCBI blastall and formatdb
executables if not specified as
environment variable. Has higher priority
than environment variable.
--COMPLEASM_PATH=/path/to/compleasm Set path to compleasm (if not specified as
environment variable). Has higher priority
than environment variable.
--PYTHON3_PATH=/path/to Set path to python3 executable (if not
specified as envirnonment variable and if
executable is not in your $PATH).
--JAVA_PATH=/path/to Set path to java executable (if not
specified as environment variable and if
executable is not in your $PATH), only
required with flags --UTR=on and --addUTR=on
--GUSHR_PATH=/path/to Set path to gushr.py exectuable (if not
specified as an environment variable and if
executable is not in your $PATH), only required
with the flags --UTR=on and --addUTR=on
--MAKEHUB_PATH=/path/to Set path to make_hub.py (if option --makehub
is used).
--CDBTOOLS_PATH=/path/to cdbfasta/cdbyank are required for running
fix_in_frame_stop_codon_genes.py. Usage of
that script can be skipped with option
'--skip_fixing_broken_genes'.

EXPERT OPTIONS

--augustus_args="--some_arg=bla" One or several command line arguments to
be passed to AUGUSTUS, if several
arguments are given, separate them by
whitespace, i.e.
"--first_arg=sth --second_arg=sth".
--skipGeneMark-ES Skip GeneMark-ES and use provided
GeneMark-ES output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ET Skip GeneMark-ET and use provided
GeneMark-ET output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-EP Skip GeneMark-EP and use provided
GeneMark-EP output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ETP Skip GeneMark-ETP and use provided
GeneMark-ETP output (e.g. provided with
--gmetp_results_dir=GeneMark-ETP/)
--geneMarkGtf=file.gtf If skipGeneMark-ET is used, braker will by
default look in the working directory in
folder GeneMarkET for an already existing
gtf file. Instead, you may provide such a
file from another location. If geneMarkGtf
option is set, skipGeneMark-ES/ET/EP/ETP is
automatically also set. Note that gene and
transcript ids in the final output may not
match the ids in the input genemark.gtf
because BRAKER internally re-assigns these
ids.
In ETP mode, this option hast to be used together
with --traingenes and --hints to provide BRAKER
with results of a previous GeneMark-ETP run.
--gmetp_results_dir Location of results from a previous
GeneMark-ETP run, which will be used to
skip the GeneMark-ETP step. This option
can be used instead of --geneMarkGtf,
--traingenes, and --hints to skip GeneMark.
--rounds The number of optimization rounds used in
optimize_augustus.pl (default 5)
--skipAllTraining Skip GeneMark-EX (training and
prediction), skip AUGUSTUS training, only
runs AUGUSTUS with pre-trained and already
existing parameters (not recommended).
Hints from input are still generated.
This option automatically sets
--useexisting to true.
--useexisting Use the present config and parameter files
if they exist for 'species'; will overwrite
original parameters if BRAKER performs
an AUGUSTUS training.
--filterOutShort It may happen that a "good" training gene,
i.e. one that has intron support from
RNA-Seq in all introns predicted by
GeneMark-EX, is in fact too short. This flag
will discard such genes that have
supported introns and a neighboring
RNA-Seq supported intron upstream of the
start codon within the range of the
maximum CDS size of that gene and with a
multiplicity that is at least as high as
20% of the average intron multiplicity of
that gene.
--skipOptimize Skip optimize parameter step (not
recommended).
--skipIterativePrediction Skip iterative prediction in --epmode (does
not affect other modes, saves a bit of runtime)
--skipGetAnnoFromFasta Skip calling the python3 script
getAnnoFastaFromJoingenes.py from the
AUGUSTUS tool suite. This script requires
python3, biopython and re (regular
expressions) to be installed. It produces
coding sequence and protein FASTA files
from AUGUSTUS gene predictions and provides
information about genes with in-frame stop
codons. If you enable this flag, these files
will not be produced and python3 and
the required modules will not be necessary
for running brkaker.pl.
--skip_fixing_broken_genes If you do not have python3, you can choose
to skip the fixing of stop codon including
genes (not recommended).
--eval=reference.gtf Reference set to evaluate predictions
against (using evaluation scripts from GaTech)
--eval_pseudo=pseudo.gff3 File with pseudogenes that will be excluded
from accuracy evaluation (may be empty file)
--AUGUSTUS_hints_preds=s File with AUGUSTUS hints predictions; will
use this file as basis for UTR training;
only UTR training and prediction is
performed if this option is given.
--flanking_DNA=n Size of flanking region, must only be
specified if --AUGUSTUS_hints_preds is given
(for UTR training in a separate braker.pl
run that builds on top of an existing run)
--verbosity=n 0 -> run braker.pl quiet (no log)
1 -> only log warnings
2 -> also log configuration
3 -> log all major steps
4 -> very verbose, log also small steps
--downsampling_lambda=d The distribution of introns in training
gene structures generated by GeneMark-EX
has a huge weight on single-exon and
few-exon genes. Specifying the lambda
parameter of a poisson distribution will
make braker call a script for downsampling
of training gene structures according to
their number of introns distribution, i.e.
genes with none or few exons will be
downsampled, genes with many exons will be
kept. Default value is 2.
If you want to avoid downsampling, you have
to specify 0.
--checkSoftware Only check whether all required software
is installed, no execution of BRAKER
--nocleanup Skip deletion of all files that are typically not
used in an annotation project after
running braker.pl. (For tracking any
problems with a braker.pl run, you
might want to keep these files, therefore
nocleanup can be activated.)

DEVELOPMENT OPTIONS (PROBABLY STILL DYSFUNCTIONAL)

--splice_sites=patterns list of splice site patterns for UTR
prediction; default: GTAG, extend like this:
--splice_sites=GTAG,ATAC,...
this option only affects UTR training
example generation, not gene prediction
by AUGUSTUS
--overwrite Overwrite existing files (except for
species parameter files) Beware, currently
not implemented properly!
--extrinsicCfgFiles=file1,file2,... Depending on the mode in which braker.pl
is executed, it may require one ore several
extrinsicCfgFiles. Don't use this option
unless you know what you are doing!
--stranded=+,-,+,-,... If UTRs are trained, i.e.~strand-specific
bam-files are supplied and coverage
information is extracted for gene prediction,
create stranded ep hints. The order of
strand specifications must correspond to the
order of bam files. Possible values are
+, -, .
If stranded data is provided, ONLY coverage
data from the stranded data is used to
generate UTR examples! Coverage data from
unstranded data is used in the prediction
step, only.
The stranded label is applied to coverage
data, only. Intron hints are generated
from all libraries treated as "unstranded"
(because splice site filtering eliminates
intron hints from the wrong strand, anyway).
--optCfgFile=ppx.cfg Optional custom config file for AUGUSTUS
for running PPX (currently not
implemented)
--grass Switch this flag on if you are using braker.pl
for predicting genes in grasses with
GeneMark-EX. The flag will enable
GeneMark-EX to handle GC-heterogenicity
within genes more properly.
NOTHING IMPLEMENTED FOR GRASS YET!
--transmasked_fasta=file.fa Transmasked genome FASTA file for GeneMark-EX
(to be used instead of the regular genome
FASTA file).
--min_contig=INT Minimal contig length for GeneMark-EX, could
for example be set to 10000 if transmasked_fasta
option is used because transmasking might
introduce many very short contigs.
--translation_table=INT Change translation table from non-standard
to something else.
DOES NOT WORK YET BECAUSE BRAKER DOESNT
SWITCH TRANSLATION TABLE FOR GENEMARK-EX, YET!
--gc_probability=DECIMAL Probablity for donor splice site pattern GC
for gene prediction with GeneMark-EX,
default value is 0.001
--gm_max_intergenic=INT Adjust maximum allowed size of intergenic
regions in GeneMark-EX. If not used, the value
is automatically determined by GeneMark-EX.
--traingenes=file.gtf Training genes that are used instead of training
genes generated with GeneMark.
In ETP mode, this option can be used together
with --geneMarkGtf and --hints to provide BRAKER
with results of a previous GeneMark-ETP run, so
that the GeneMark-ETP step can be skipped.
In this case, use training.gtf from that run as
argument.
--UTR=on create UTR training examples from RNA-Seq
coverage data; requires options
--bam=rnaseq.bam.
Alternatively, if UTR parameters already
exist, training step will be skipped and
those pre-existing parameters are used.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!
--addUTR=on Adds UTRs from RNA-Seq coverage data to
augustus.hints.gtf file. Does not perform
training of AUGUSTUS or gene prediction with
AUGUSTUS and UTR parameters.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!

EXAMPLE

To run with RNA-Seq

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--bam=accepted_hits.bam
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=rnaseq.gff

To run with protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=prothint_augustus.gff

To run with RNA-Seq and protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --rnaseq_sets_ids=id_rnaseq1,id_rnaseq2 \
--rnaseq_sets_dir=/path/to/local/rnaseq/files
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --bam=id_rnaseq1.bam,id_rnaseq2.bam
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== System ===
64-bit Linux

BRAKER-Sapelo2

2025-05-06T17:06:55Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
2.1.6, 3.0.3, 3.0.8

=== Author / Distributor ===
[https://github.com/Gaius-Augustus/BRAKER Gaius-Augustus]

=== Description ===
"BRAKER is a pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes."
More details are at [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

'''Version 2.1.6'''

Version 2.1.6, installed at

* /apps/eb/BRAKER/2.1.6-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/2.1.6-foss-2022a
</div>

'''Version 3.0.3'''

Version 3.0.3 installed at

* /apps/eb/BRAKER/3.0.3-foss-2022a-long_reads/
* /apps/eb/BRAKER/3.0.3-foss-2022a/
* /apps/eb/BRAKER/3.0.3-long_reads/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

'''Version 3.0.8'''

Version 3.0.8 installed at

* /apps/eb/BRAKER/3.0.8-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.8-foss-2022a
</div>

'''Notes about Dependencies, Etc.'''

BRAKER is a gene prediction pipeline that predicts the genes in an input genome using RNAseq data, protein data, or a combination of RNAseq and protein data to support and solidify gene predictions. If it detects the input of both kinds of supporting data, BRAKER will implement GeneMark-ETP rather than the other iterations of GeneMark; one of the scripts that GeneMark-ETP uses to filter results calls DIAMOND (unless BLAST is explicitly specified by the user) without passing the value of the user-specified "--threads" option (see full documentation below) to it, which will cause DIAMOND to auto-detect the number of CPUs present on the node and attempt to run this many threads regardless of how many CPUs have been allocated for the job. Therefore, it is strongly recommended that if you run BRAKER with both RNA and protein data, you request a full 128 CPUs for your BRAKER job; while this may cause your job to wait in the queue for longer, numerous BRAKER jobs have been observed to get "stuck" or outright fail because of DIAMOND attempting to run 128 threads on only a few CPUs. Regardless of the type(s) of input data you use, BRAKER will use one iteration or another of the GeneMark gene prediction software, and thus in order to use BRAKER, you need to have downloaded the key file for GeneMarker and put it into your home directory. Please follow instructions found at [[GeneMarkES-Sapelo2]].

Furthermore, BRAKER makes use of numerous external tools (as can be seen in the "CONFIGURATION OPTIONS" section of the documentation below); while working versions of these are all included with the modules listed above, you also have the option to use your own versions of these (see the example submission script below):

Here is an example of a shell script sub.sh to run BRAKER using a protein database from the batch queue along with your own version of ProtHint (in place of the default one, which does not need to be specified):

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=BRAKER_Job 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=16gb 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load BRAKER/3.0.8-foss-2022a 

braker.pl --genome=Newly_assembled_genome.fasta --prot_seq=Protein_database.faa --PROTHINT_PATH=/Path/to/MY/version/of/ProtHint 
</div>

In the real submission script, be sure to replace the above underlined values with proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load BRAKER/3.0.8-foss-2022a
braker.pl

DESCRIPTION

braker.pl Pipeline for predicting genes with GeneMark-EX and AUGUSTUS with
RNA-Seq and/or proteins

SYNOPSIS

braker.pl [OPTIONS] --genome=genome.fa {--bam=rnaseq.bam | --prot_seq=prot.fa}

INPUT FILE OPTIONS

--genome=genome.fa fasta file with DNA sequences
--bam=rnaseq.bam bam file with spliced alignments from
RNA-Seq
--prot_seq=prot.fa A protein sequence file in multi-fasta
format used to generate protein hints.
Unless otherwise specified, braker.pl will
run in "EP mode" or "ETP mode which uses
ProtHint to generate protein hints and
GeneMark-EP+ or GeneMark-ETP to
train AUGUSTUS.
--hints=hints.gff Alternatively to calling braker.pl with a
bam or protein fasta file, it is possible to
call it with a .gff file that contains
introns extracted from RNA-Seq and/or
protein hints (most frequently coming
from ProtHint). If you wish to use the
ProtHint hints, use its
"prothint_augustus.gff" output file.
This flag also allows the usage of hints
from additional extrinsic sources for gene
prediction with AUGUSTUS. To consider such
additional extrinsic information, you need
to use the flag --extrinsicCfgFiles to
specify parameters for all sources in the
hints file (including the source "E" for
intron hints from RNA-Seq).
In ETP mode, this option can be used together
with --geneMarkGtf and --traingenes to provide
BRAKER with results of a previous GeneMark-ETP
run, so that the GeneMark-ETP step can be
skipped. In this case, specify the hintsfile of
a previous BRAKER run here, or generate a
hintsfile from the GeneMark-ETP working
directory with the script get_etp_hints.py.
--rnaseq_sets_ids=SRR1111,SRR1115 IDs of RNA-Seq sets that are either in
one of the directories specified with
--rnaseq_sets_dir, or that can be downloaded
from SRA. If you want to use local files, you
can use unaligned reads in FASTQ format
(they have to be named ID.fastq if unpaired or
ID_1.fastq, ID_2.fastq if paired), or aligned reads
as a BAM file (named ID.bam).
--rnaseq_sets_dir=/path/to/rna_dir1 Locations where the local files of RNA-Seq data
reside that were specified with --rnaseq_sets_ids.

FREQUENTLY USED OPTIONS

--species=sname Species name. Existing species will not be
overwritten. Uses Sp_1 etc., if no species
is assigned
--AUGUSTUS_ab_initio output ab initio predictions by AUGUSTUS
in addition to predictions with hints by
AUGUSTUS
--softmasking_off Turn off softmasking option (enables by
default, discouraged to disable!)
--esmode Run GeneMark-ES (genome sequence only) and
train AUGUSTUS on long genes predicted by
GeneMark-ES. Final predictions are ab initio
--gff3 Output in GFF3 format (default is gtf
format)
--threads Specifies the maximum number of threads that
can be used during computation. Be aware:
optimize_augustus.pl will use max. 8
threads; augustus will use max. nContigs in
--genome=file threads.
--workingdir=/path/to/wd/ Set path to working directory. In the
working directory results and temporary
files are stored
--nice Execute all system calls within braker.pl
and its submodules with bash "nice"
(default nice value)
--alternatives-from-evidence=true Output alternative transcripts based on
explicit evidence from hints (default is
true).
--fungus GeneMark-EX option: run algorithm with
branch point model (most useful for fungal
genomes)
--crf Execute CRF training for AUGUSTUS;
resulting parameters are only kept for
final predictions if they show higher
accuracy than HMM parameters.
--keepCrf keep CRF parameters even if they are not
better than HMM parameters
--makehub Create track data hub with make_hub.py
for visualizing BRAKER results with the
UCSC GenomeBrowser
--busco_lineage=lineage If you provide a BUSCO lineage, BRAKER will
run compleasm on genome level to generate hints
from BUSCO to enhance BUSCO discovery in the
protein set. Also, if you provide a BUSCO
lineage, BRAKER will run compleasm to assess
the protein sets that go into TSEBRA combination,
and will determine the TSEBRA mode to maximize
BUSCO. Do not provide a busco_lineage if you
want to determina natural BUSCO sensivity of
BRAKER!
--email E-mail address for creating track data hub
--version Print version number of braker.pl
--help Print this help message

CONFIGURATION OPTIONS (TOOLS CALLED BY BRAKER)

--AUGUSTUS_CONFIG_PATH=/path/ Set path to config directory of AUGUSTUS
(if not specified as environment
variable). BRAKER1 will assume that the
directories ../bin and ../scripts of
AUGUSTUS are located relative to the
AUGUSTUS_CONFIG_PATH. If this is not the
case, please specify AUGUSTUS_BIN_PATH
(and AUGUSTUS_SCRIPTS_PATH if required).
The braker.pl commandline argument
--AUGUSTUS_CONFIG_PATH has higher priority
than the environment variable with the
same name.
--AUGUSTUS_BIN_PATH=/path/ Set path to the AUGUSTUS directory that
contains binaries, i.e. augustus and
etraining. This variable must only be set
if AUGUSTUS_CONFIG_PATH does not have
../bin and ../scripts of AUGUSTUS relative
to its location i.e. for global AUGUSTUS
installations. BRAKER1 will assume that
the directory ../scripts of AUGUSTUS is
located relative to the AUGUSTUS_BIN_PATH.
If this is not the case, please specify
--AUGUSTUS_SCRIPTS_PATH.
--AUGUSTUS_SCRIPTS_PATH=/path/ Set path to AUGUSTUS directory that
contains scripts, i.e. splitMfasta.pl.
This variable must only be set if
AUGUSTUS_CONFIG_PATH or AUGUSTUS_BIN_PATH
do not contains the ../scripts directory
of AUGUSTUS relative to their location,
i.e. for special cases of a global
AUGUSTUS installation.
--BAMTOOLS_PATH=/path/to/ Set path to bamtools (if not specified as
environment BAMTOOLS_PATH variable). Has
higher priority than the environment
variable.
--GENEMARK_PATH=/path/to/ Set path to GeneMark-ET (if not specified
as environment GENEMARK_PATH variable).
Has higher priority than environment
variable.
--SAMTOOLS_PATH=/path/to/ Optionally set path to samtools (if not
specified as environment SAMTOOLS_PATH
variable) to fix BAM files automatically,
if necessary. Has higher priority than
environment variable.
--PROTHINT_PATH=/path/to/ Set path to the directory with prothint.py.
(if not specified as PROTHINT_PATH
environment variable). Has higher priority
than environment variable.
--DIAMOND_PATH=/path/to/diamond Set path to diamond, this is an alternative
to NCIB blast; you only need to specify one
out of DIAMOND_PATH or BLAST_PATH, not both.
DIAMOND is a lot faster that BLAST and yields
highly similar results for BRAKER.
--BLAST_PATH=/path/to/blastall Set path to NCBI blastall and formatdb
executables if not specified as
environment variable. Has higher priority
than environment variable.
--COMPLEASM_PATH=/path/to/compleasm Set path to compleasm (if not specified as
environment variable). Has higher priority
than environment variable.
--PYTHON3_PATH=/path/to Set path to python3 executable (if not
specified as envirnonment variable and if
executable is not in your $PATH).
--JAVA_PATH=/path/to Set path to java executable (if not
specified as environment variable and if
executable is not in your $PATH), only
required with flags --UTR=on and --addUTR=on
--GUSHR_PATH=/path/to Set path to gushr.py exectuable (if not
specified as an environment variable and if
executable is not in your $PATH), only required
with the flags --UTR=on and --addUTR=on
--MAKEHUB_PATH=/path/to Set path to make_hub.py (if option --makehub
is used).
--CDBTOOLS_PATH=/path/to cdbfasta/cdbyank are required for running
fix_in_frame_stop_codon_genes.py. Usage of
that script can be skipped with option
'--skip_fixing_broken_genes'.

EXPERT OPTIONS

--augustus_args="--some_arg=bla" One or several command line arguments to
be passed to AUGUSTUS, if several
arguments are given, separate them by
whitespace, i.e.
"--first_arg=sth --second_arg=sth".
--skipGeneMark-ES Skip GeneMark-ES and use provided
GeneMark-ES output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ET Skip GeneMark-ET and use provided
GeneMark-ET output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-EP Skip GeneMark-EP and use provided
GeneMark-EP output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ETP Skip GeneMark-ETP and use provided
GeneMark-ETP output (e.g. provided with
--gmetp_results_dir=GeneMark-ETP/)
--geneMarkGtf=file.gtf If skipGeneMark-ET is used, braker will by
default look in the working directory in
folder GeneMarkET for an already existing
gtf file. Instead, you may provide such a
file from another location. If geneMarkGtf
option is set, skipGeneMark-ES/ET/EP/ETP is
automatically also set. Note that gene and
transcript ids in the final output may not
match the ids in the input genemark.gtf
because BRAKER internally re-assigns these
ids.
In ETP mode, this option hast to be used together
with --traingenes and --hints to provide BRAKER
with results of a previous GeneMark-ETP run.
--gmetp_results_dir Location of results from a previous
GeneMark-ETP run, which will be used to
skip the GeneMark-ETP step. This option
can be used instead of --geneMarkGtf,
--traingenes, and --hints to skip GeneMark.
--rounds The number of optimization rounds used in
optimize_augustus.pl (default 5)
--skipAllTraining Skip GeneMark-EX (training and
prediction), skip AUGUSTUS training, only
runs AUGUSTUS with pre-trained and already
existing parameters (not recommended).
Hints from input are still generated.
This option automatically sets
--useexisting to true.
--useexisting Use the present config and parameter files
if they exist for 'species'; will overwrite
original parameters if BRAKER performs
an AUGUSTUS training.
--filterOutShort It may happen that a "good" training gene,
i.e. one that has intron support from
RNA-Seq in all introns predicted by
GeneMark-EX, is in fact too short. This flag
will discard such genes that have
supported introns and a neighboring
RNA-Seq supported intron upstream of the
start codon within the range of the
maximum CDS size of that gene and with a
multiplicity that is at least as high as
20% of the average intron multiplicity of
that gene.
--skipOptimize Skip optimize parameter step (not
recommended).
--skipIterativePrediction Skip iterative prediction in --epmode (does
not affect other modes, saves a bit of runtime)
--skipGetAnnoFromFasta Skip calling the python3 script
getAnnoFastaFromJoingenes.py from the
AUGUSTUS tool suite. This script requires
python3, biopython and re (regular
expressions) to be installed. It produces
coding sequence and protein FASTA files
from AUGUSTUS gene predictions and provides
information about genes with in-frame stop
codons. If you enable this flag, these files
will not be produced and python3 and
the required modules will not be necessary
for running brkaker.pl.
--skip_fixing_broken_genes If you do not have python3, you can choose
to skip the fixing of stop codon including
genes (not recommended).
--eval=reference.gtf Reference set to evaluate predictions
against (using evaluation scripts from GaTech)
--eval_pseudo=pseudo.gff3 File with pseudogenes that will be excluded
from accuracy evaluation (may be empty file)
--AUGUSTUS_hints_preds=s File with AUGUSTUS hints predictions; will
use this file as basis for UTR training;
only UTR training and prediction is
performed if this option is given.
--flanking_DNA=n Size of flanking region, must only be
specified if --AUGUSTUS_hints_preds is given
(for UTR training in a separate braker.pl
run that builds on top of an existing run)
--verbosity=n 0 -> run braker.pl quiet (no log)
1 -> only log warnings
2 -> also log configuration
3 -> log all major steps
4 -> very verbose, log also small steps
--downsampling_lambda=d The distribution of introns in training
gene structures generated by GeneMark-EX
has a huge weight on single-exon and
few-exon genes. Specifying the lambda
parameter of a poisson distribution will
make braker call a script for downsampling
of training gene structures according to
their number of introns distribution, i.e.
genes with none or few exons will be
downsampled, genes with many exons will be
kept. Default value is 2.
If you want to avoid downsampling, you have
to specify 0.
--checkSoftware Only check whether all required software
is installed, no execution of BRAKER
--nocleanup Skip deletion of all files that are typically not
used in an annotation project after
running braker.pl. (For tracking any
problems with a braker.pl run, you
might want to keep these files, therefore
nocleanup can be activated.)

DEVELOPMENT OPTIONS (PROBABLY STILL DYSFUNCTIONAL)

--splice_sites=patterns list of splice site patterns for UTR
prediction; default: GTAG, extend like this:
--splice_sites=GTAG,ATAC,...
this option only affects UTR training
example generation, not gene prediction
by AUGUSTUS
--overwrite Overwrite existing files (except for
species parameter files) Beware, currently
not implemented properly!
--extrinsicCfgFiles=file1,file2,... Depending on the mode in which braker.pl
is executed, it may require one ore several
extrinsicCfgFiles. Don't use this option
unless you know what you are doing!
--stranded=+,-,+,-,... If UTRs are trained, i.e.~strand-specific
bam-files are supplied and coverage
information is extracted for gene prediction,
create stranded ep hints. The order of
strand specifications must correspond to the
order of bam files. Possible values are
+, -, .
If stranded data is provided, ONLY coverage
data from the stranded data is used to
generate UTR examples! Coverage data from
unstranded data is used in the prediction
step, only.
The stranded label is applied to coverage
data, only. Intron hints are generated
from all libraries treated as "unstranded"
(because splice site filtering eliminates
intron hints from the wrong strand, anyway).
--optCfgFile=ppx.cfg Optional custom config file for AUGUSTUS
for running PPX (currently not
implemented)
--grass Switch this flag on if you are using braker.pl
for predicting genes in grasses with
GeneMark-EX. The flag will enable
GeneMark-EX to handle GC-heterogenicity
within genes more properly.
NOTHING IMPLEMENTED FOR GRASS YET!
--transmasked_fasta=file.fa Transmasked genome FASTA file for GeneMark-EX
(to be used instead of the regular genome
FASTA file).
--min_contig=INT Minimal contig length for GeneMark-EX, could
for example be set to 10000 if transmasked_fasta
option is used because transmasking might
introduce many very short contigs.
--translation_table=INT Change translation table from non-standard
to something else.
DOES NOT WORK YET BECAUSE BRAKER DOESNT
SWITCH TRANSLATION TABLE FOR GENEMARK-EX, YET!
--gc_probability=DECIMAL Probablity for donor splice site pattern GC
for gene prediction with GeneMark-EX,
default value is 0.001
--gm_max_intergenic=INT Adjust maximum allowed size of intergenic
regions in GeneMark-EX. If not used, the value
is automatically determined by GeneMark-EX.
--traingenes=file.gtf Training genes that are used instead of training
genes generated with GeneMark.
In ETP mode, this option can be used together
with --geneMarkGtf and --hints to provide BRAKER
with results of a previous GeneMark-ETP run, so
that the GeneMark-ETP step can be skipped.
In this case, use training.gtf from that run as
argument.
--UTR=on create UTR training examples from RNA-Seq
coverage data; requires options
--bam=rnaseq.bam.
Alternatively, if UTR parameters already
exist, training step will be skipped and
those pre-existing parameters are used.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!
--addUTR=on Adds UTRs from RNA-Seq coverage data to
augustus.hints.gtf file. Does not perform
training of AUGUSTUS or gene prediction with
AUGUSTUS and UTR parameters.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!

EXAMPLE

To run with RNA-Seq

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--bam=accepted_hits.bam
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=rnaseq.gff

To run with protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=prothint_augustus.gff

To run with RNA-Seq and protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --rnaseq_sets_ids=id_rnaseq1,id_rnaseq2 \
--rnaseq_sets_dir=/path/to/local/rnaseq/files
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --bam=id_rnaseq1.bam,id_rnaseq2.bam
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== System ===
64-bit Linux

BLAST Databases-Sapelo2

2025-05-02T13:16:46Z

Jordan: Updated the list to reflect how the "human_genome" and "mouse_genome" databases have to be referenced since the NCBI couldn't just leave well enough alone.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]][[Category:Bioinformatics Database]]

'''Blast databases'''

Blast databases are pre-formatted to work with blast commands from BLAST and BLAST+. These databases are similar to what you could build using the makeblastdb command with FASTA files. Each database is located at a directory with the name pattern /db/ncbiblast/YYYYMMDD. For example, databases downloaded on February 01, 2022 will be available in /db/ncbiblast/20220201. Starting with the download on August 2nd, 2024 (i.e., /db/ncbiblast/20240802), the following databases are currently available and stored together with databases downloaded at the same time:
<pre class="gscript">
nr
nt
taxdb
refseq_rna
refseq_protein
mouse_genome (which is referenced as "GCF_000001635.27_top_level" when used with BLAST+)
human_genome (which is referenced as "GCF_000001405.39_top_level" when used with BLAST+)
swissprot
cdd_delta
env_nr
env_nt
tsa_nr
tsa_nt
taxdb
ref_prok_rep_genomes
ref_euk_rep_genomes
ref_viroids_rep_genomes
ref_viruses_rep_genomes
16S_ribosomal_RNA
18S_fungal_sequences
28S_fungal_sequences
LSU_eukaryote_rRNA
LSU_prokaryote_rRNA
SSU_eukaryote_rRNA
Betacoronavirus
ITS_RefSeq_Fungi
ITS_eukaryote_sequences
mito
</pre>

Note that the database refseq_genomic is no longer available to download as a pre-formatted database.

NCBI BLAST Datasets can be loaded just like software modules. This allows users to replicate results by always being able to use a time stamped version of a database. The time stamp corresponds to the download date of the dataset. To search for available NCBI BLAST dataset modules run the command:

<code>ml spider ncbiblastdb</code>

You can then load a database by running the command module load. For example to load the databases timestamped on 11/02/2024:

<code>module load ncbiblastdb/20241102</code>

Loading this module sets the environment variable BLASTDB to /db/ncbiblast/20241102. This allows BLAST+ to search that directory for databases. You can then use the name of the database you would like to use.(nt, nr, etc.) Here is an example of a shell script to run BLAST+ on the batch queue using the database nt:
<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_BLAST+ 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=BLAST+.%j.out 
<nowiki>#</nowiki>SBATCH --error=BLAST+.%j.err 

cd $SLURM_SUBMIT_DIR 
module load BLAST+/2.14.1-gompi-2023a 
module load ncbiblastdb/20241102 
blastn -query example.fasta -out results.out -db nt 
</div>

In your actual submission script, use your own discretion for the Slurm header values.

NCBI's nr and nt datasets are the most commonly used at the GACRC. GACRC staff will update these datasets '''every 3 months''' along with other potentially helpful NCBI BLAST datasets.

'''Blast database - old version 5'''

According to https://ncbiinsights.ncbi.nlm.nih.gov/tag/blastdbv5/, around Feb. 04, 2020 NCBI dropped the _v5 suffix for the v5 Blast databases.

An old version of the v5 database (download in June 2019) is located at a directory with name pattern as /db/ncbiblast.v5/''dbname'', where ''dbname'' is the name of the database, such as nr, nt, taxdb, refseq_rna, swissprot, etc.

These old versions of the nr and nt databases still have the _v5 suffix, i.e. they are called nr_v5 and nt_v5, respectively.

[[#top|Back to Top]]

Bioinformatics Databases

2025-04-25T19:10:59Z

Jordan: /* Installed Bioinformatics Databases */

As part of our services, the GACRC builds and hosts local copies of frequently cited application data, and provides assistance for sharing data among GACRC members. Datasets are located in the commonly shared "/db" filesystem. [[BLAST Databases-Sapelo2 | NCBI BLAST datasets]] are pre-formatted to work with BLAST and BLAST+ and are located in "/db/ncbiblast/" and are organized by date.

NCBI BLAST Datasets can be loaded in the same way as software modules. This allows users to replicate results by always being able to use a time stamped version of a database. The time stamp corresponds to the download date of the dataset. To search for available NCBI BLAST dataset modules run the command:

<code>ml spider ncbiblastdb</code>

You can then load a database by running the command module load. For example to load the databases timestamped at 02/02/2024:

<code>module load ncbiblastdb/20240202</code>

Loading this module sets the environment variable BLASTDB to /db/ncbiblast/20240202. This allows BLAST+ to search that directory for databases. You can then use the name of the database you would like to use.(nt, nr, etc.) Here is an example of a shell script to run BLAST+ on the batch queue using the database nt:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_BLAST+ 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=BLAST+.%j.out 
<nowiki>#</nowiki>SBATCH --error=BLAST+.%j.err 

cd $SLURM_SUBMIT_DIR 
module load BLAST+/2.13.0-gompi-2022a 
module load ncbiblastdb/20240202 
blastn -query example.fasta -out results.out -db nt 
</div>

In your actual submission script, use your own discretion for the Slurm header values.

NCBI's nr and nt datasets are the most commonly used at the GACRC. GACRC staff will update these datasets '''every 3 months''' as well as other NCBI BLAST datasets: cdd_delta, human_genome, mouse_genome, nrte, refseq_protein, refseq_rna, swissprot, and taxdb datasets.

Various subject datasets, e.g. pfam, bowtie indexes of human and mouse, and NCBI bacterial datasets, are hosted as well. However, these datasets are not accessed with a module. These datasets either don’t need updating, the source does not update frequently, or the datasets are not used frequently by our users. These datasets will only be updated by user request.
Instead of using a module, to use these databases, use the path to the databases in your script. Below is an example using BLAST+ with a dataset not available as a module.

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_BLAST+ 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=BLAST+.%j.out 
<nowiki>#</nowiki>SBATCH --error=BLAST+.%j.err 

cd $SLURM_SUBMIT_DIR 
module load BLAST+/2.13.0-gompi-2022a 
blastn -query example.fasta -out results.out -db /db/ncbiblast/refseq_microbial/fasta/09162020/ 
</div>

For datasets requested by individual lab groups, GACRC encourages users to maintain their own copies of databases, whose files can be installed in their group's shared "/work" area.
Similarly for datasets frequently updated by their source, the GACRC encourages users to maintain their own copies of these public databases.

The set of data which are regularly updated is open to review, and can be expanded based on available GACRC resources.

==Installed Bioinformatics Databases ==
{| border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" | Name
! scope="col" class="unsortable" | Version
! scope="col" | Module Available
|-
| akblab || 01/28/2018 || no
|-
| [[Bacteria NCBI-sapelo2|Bacteria NCBI]] || 12/21/2017 || no
|-
| [[bowtie2|hg19,mm10 & mm9]] || 07/29/2016 || no
|-
|bowtie2
|09/12/2024
|yes
|-
| cellranger (GRCh38 and MM10) || 12/19/2021 || yes
|-
|CheckV
|01/10/2023 (v1.5)
|yes
|-
| conspred_ressources || 02/02/2017 || yes
|-
| dammit || 03/17/2021 (v1.2) || yes
|-
| deconseq || 12/19/2021 ("04242013") || yes
|-
| decontaMiner-Sapelo2|decontaMiner || 08/29/2019 || no
|-
| eggnog-mapper || 04/16/2019 || no
|-
|foldseek
|04/11/2023
|yes
|-
| funannotate || 12/19/2021 || yes
|-
| DTDB-Tk || 01/15/2021 || no
|-
| hg || 09/20/2016 || no
|-
| kegg || 09/09/2016 || no
|-
|Kraken2 (PlusPF, PlusPFP, PlusPFP-16, core_nt, EuPathDB)
|04/06/2025
|no
|-
| maker || 05/22/2018 (v2.31.9) || yes
|-
| MetaCLADE || 08/29/2019 || no
|-
| [[BLAST Databases-Sapelo2 |NCBI BLAST Databases]] || beginning of every 3 months || yes
|-
|NCBI-FCS
|12/01/2023 (v0.5.0)
|yes
|-
| [[NCBI Fasta-Sapelo2|NCBI Fasta]] || 04/06/2022 || yes
|-
| ngs (canine and mouse) || 11/03/2016 || yes
|-
| nndb || 05/03/2008 || yes
|-
| PB || 04/24/2019 || no
|-
| [[PfamDB-Sapelo2|pfam]] || 10/07/2021 || no
|-
| phylosift || 07/20/2020 || no
|-
| [[Refseq-Sapelo2|Refseq_genomic]] || || no
|-
| repbase || 01/09/2017 || no
|-
| rfam || 02/01/2017 (v12.2) || yes
|-
| seqdb || 05/22/2012 || yes
|-
| sortmerna || 03/04/2016 || no
|-
| [[TaxDB-Sapelo2|taxdb]] || 02/23/2019 || yes
|-
| topcons2 || 02/18/2021 || no
|-
| [[Uniprot-Sapelo2|Uniprot]] || 06/15/2023 || yes
|-
| [[Uniref-Sapelo2|Uniref]] || 02/25/2022 || yes
|-
|virulencefinder
|05/06/2024
|yes
|-
| [[wublast-Sapelo2|wublast]] || 04/01/2020 || no
|-
|}

BRAKER-Sapelo2

2025-04-24T20:07:22Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
2.1.6, 3.0.3, 3.0.8

=== Author / Distributor ===
[https://github.com/Gaius-Augustus/BRAKER Gaius-Augustus]

=== Description ===
"BRAKER is a pipeline for fully automated prediction of protein coding genes with GeneMark-ES/ET and AUGUSTUS in novel eukaryotic genomes."
More details are at [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

'''Version 2.1.6'''

Version 2.1.6, installed at

* /apps/eb/BRAKER/2.1.6-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/2.1.6-foss-2022a
</div>

'''Version 3.0.3'''

Version 3.0.3 installed at

* /apps/eb/BRAKER/3.0.3-foss-2022a-long_reads/
* /apps/eb/BRAKER/3.0.3-foss-2022a/
* /apps/eb/BRAKER/3.0.3-long_reads/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a
</div>

or

<div class="gscript2">
module load BRAKER/3.0.3-foss-2022a-long_reads
</div>

'''Version 3.0.8'''

Version 3.0.8 installed at

* /apps/eb/BRAKER/3.0.8-foss-2022a/

To use it, please load the module with:
<div class="gscript2">
module load BRAKER/3.0.8-foss-2022a
</div>

'''Notes about Dependencies, Etc.'''

BRAKER is a gene prediction pipeline that predicts the genes in an input genome using RNAseq data, protein data, or a combination of RNAseq and protein data to support and solidify gene predictions. If it detects the input of both kinds of supporting data, BRAKER will implement GeneMark-ETP rather than the other iterations of GeneMark; one of the scripts that GeneMark-ETP uses to filter results calls DIAMOND (unless BLAST is explicitly specified by the user) without passing the value of the user-specified "--threads" option (see full documentation below) to it, which will cause DIAMOND to auto-detect the number of CPUs present on the node and attempt to run this many threads regardless of how many CPUs have been allocated for the job. Therefore, it is strongly recommended that if you run BRAKER with both RNA and protein data, you request a full 128 CPUs for your BRAKER job; while this may cause your job to wait in the queue for longer, numerous BRAKER jobs have been observed to get "stuck" or outright fail because of DIAMOND attempting to run 128 threads on only a few CPUs. Regardless of the type(s) of input data you use, BRAKER will use one iteration or another of the GeneMark gene prediction software, and thus in order to use BRAKER, you need to have downloaded the key file for GeneMarker and put it into your home directory. Please follow instructions found at [[GeneMarkES-Sapelo2]].

Furthermore, BRAKER makes use of numerous external tools (as can be seen in the "CONFIGURATION OPTIONS" section of the documentation below); while working versions of these are all included with the modules listed above, you also have the option to use your own versions of these (see the example submission script below):

Here is an example of a shell script sub.sh to run BRAKER using a protein database from the batch queue along with your own version of ProtHint (in place of the default one, which does not need to be specified):

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=BRAKER_Job 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=16gb 
<nowiki>#</nowiki>SBATCH --time=48:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load BRAKER/3.0.8-foss-2022a 

braker.pl --genome=Newly_assembled_genome.fasta --prot_seq=Protein_database.faa --PROTHINT_PATH=/Path/to/MY/version/of/ProtHint 
</div>

In the real submission script, be sure to replace the above underlined values with proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load BRAKER/3.0.8-foss-2022a
braker.pl

DESCRIPTION

braker.pl Pipeline for predicting genes with GeneMark-EX and AUGUSTUS with
RNA-Seq and/or proteins

SYNOPSIS

braker.pl [OPTIONS] --genome=genome.fa {--bam=rnaseq.bam | --prot_seq=prot.fa}

INPUT FILE OPTIONS

--genome=genome.fa fasta file with DNA sequences
--bam=rnaseq.bam bam file with spliced alignments from
RNA-Seq
--prot_seq=prot.fa A protein sequence file in multi-fasta
format used to generate protein hints.
Unless otherwise specified, braker.pl will
run in "EP mode" or "ETP mode which uses
ProtHint to generate protein hints and
GeneMark-EP+ or GeneMark-ETP to
train AUGUSTUS.
--hints=hints.gff Alternatively to calling braker.pl with a
bam or protein fasta file, it is possible to
call it with a .gff file that contains
introns extracted from RNA-Seq and/or
protein hints (most frequently coming
from ProtHint). If you wish to use the
ProtHint hints, use its
"prothint_augustus.gff" output file.
This flag also allows the usage of hints
from additional extrinsic sources for gene
prediction with AUGUSTUS. To consider such
additional extrinsic information, you need
to use the flag --extrinsicCfgFiles to
specify parameters for all sources in the
hints file (including the source "E" for
intron hints from RNA-Seq).
In ETP mode, this option can be used together
with --geneMarkGtf and --traingenes to provide
BRAKER with results of a previous GeneMark-ETP
run, so that the GeneMark-ETP step can be
skipped. In this case, specify the hintsfile of
a previous BRAKER run here, or generate a
hintsfile from the GeneMark-ETP working
directory with the script get_etp_hints.py.
--rnaseq_sets_ids=SRR1111,SRR1115 IDs of RNA-Seq sets that are either in
one of the directories specified with
--rnaseq_sets_dir, or that can be downloaded
from SRA. If you want to use local files, you
can use unaligned reads in FASTQ format
(they have to be named ID.fastq if unpaired or
ID_1.fastq, ID_2.fastq if paired), or aligned reads
as a BAM file (named ID.bam).
--rnaseq_sets_dir=/path/to/rna_dir1 Locations where the local files of RNA-Seq data
reside that were specified with --rnaseq_sets_ids.

FREQUENTLY USED OPTIONS

--species=sname Species name. Existing species will not be
overwritten. Uses Sp_1 etc., if no species
is assigned
--AUGUSTUS_ab_initio output ab initio predictions by AUGUSTUS
in addition to predictions with hints by
AUGUSTUS
--softmasking_off Turn off softmasking option (enables by
default, discouraged to disable!)
--esmode Run GeneMark-ES (genome sequence only) and
train AUGUSTUS on long genes predicted by
GeneMark-ES. Final predictions are ab initio
--gff3 Output in GFF3 format (default is gtf
format)
--threads Specifies the maximum number of threads that
can be used during computation. Be aware:
optimize_augustus.pl will use max. 8
threads; augustus will use max. nContigs in
--genome=file threads.
--workingdir=/path/to/wd/ Set path to working directory. In the
working directory results and temporary
files are stored
--nice Execute all system calls within braker.pl
and its submodules with bash "nice"
(default nice value)
--alternatives-from-evidence=true Output alternative transcripts based on
explicit evidence from hints (default is
true).
--fungus GeneMark-EX option: run algorithm with
branch point model (most useful for fungal
genomes)
--crf Execute CRF training for AUGUSTUS;
resulting parameters are only kept for
final predictions if they show higher
accuracy than HMM parameters.
--keepCrf keep CRF parameters even if they are not
better than HMM parameters
--makehub Create track data hub with make_hub.py
for visualizing BRAKER results with the
UCSC GenomeBrowser
--busco_lineage=lineage If you provide a BUSCO lineage, BRAKER will
run compleasm on genome level to generate hints
from BUSCO to enhance BUSCO discovery in the
protein set. Also, if you provide a BUSCO
lineage, BRAKER will run compleasm to assess
the protein sets that go into TSEBRA combination,
and will determine the TSEBRA mode to maximize
BUSCO. Do not provide a busco_lineage if you
want to determina natural BUSCO sensivity of
BRAKER!
--email E-mail address for creating track data hub
--version Print version number of braker.pl
--help Print this help message

CONFIGURATION OPTIONS (TOOLS CALLED BY BRAKER)

--AUGUSTUS_CONFIG_PATH=/path/ Set path to config directory of AUGUSTUS
(if not specified as environment
variable). BRAKER1 will assume that the
directories ../bin and ../scripts of
AUGUSTUS are located relative to the
AUGUSTUS_CONFIG_PATH. If this is not the
case, please specify AUGUSTUS_BIN_PATH
(and AUGUSTUS_SCRIPTS_PATH if required).
The braker.pl commandline argument
--AUGUSTUS_CONFIG_PATH has higher priority
than the environment variable with the
same name.
--AUGUSTUS_BIN_PATH=/path/ Set path to the AUGUSTUS directory that
contains binaries, i.e. augustus and
etraining. This variable must only be set
if AUGUSTUS_CONFIG_PATH does not have
../bin and ../scripts of AUGUSTUS relative
to its location i.e. for global AUGUSTUS
installations. BRAKER1 will assume that
the directory ../scripts of AUGUSTUS is
located relative to the AUGUSTUS_BIN_PATH.
If this is not the case, please specify
--AUGUSTUS_SCRIPTS_PATH.
--AUGUSTUS_SCRIPTS_PATH=/path/ Set path to AUGUSTUS directory that
contains scripts, i.e. splitMfasta.pl.
This variable must only be set if
AUGUSTUS_CONFIG_PATH or AUGUSTUS_BIN_PATH
do not contains the ../scripts directory
of AUGUSTUS relative to their location,
i.e. for special cases of a global
AUGUSTUS installation.
--BAMTOOLS_PATH=/path/to/ Set path to bamtools (if not specified as
environment BAMTOOLS_PATH variable). Has
higher priority than the environment
variable.
--GENEMARK_PATH=/path/to/ Set path to GeneMark-ET (if not specified
as environment GENEMARK_PATH variable).
Has higher priority than environment
variable.
--SAMTOOLS_PATH=/path/to/ Optionally set path to samtools (if not
specified as environment SAMTOOLS_PATH
variable) to fix BAM files automatically,
if necessary. Has higher priority than
environment variable.
--PROTHINT_PATH=/path/to/ Set path to the directory with prothint.py.
(if not specified as PROTHINT_PATH
environment variable). Has higher priority
than environment variable.
--DIAMOND_PATH=/path/to/diamond Set path to diamond, this is an alternative
to NCIB blast; you only need to specify one
out of DIAMOND_PATH or BLAST_PATH, not both.
DIAMOND is a lot faster that BLAST and yields
highly similar results for BRAKER.
--BLAST_PATH=/path/to/blastall Set path to NCBI blastall and formatdb
executables if not specified as
environment variable. Has higher priority
than environment variable.
--COMPLEASM_PATH=/path/to/compleasm Set path to compleasm (if not specified as
environment variable). Has higher priority
than environment variable.
--PYTHON3_PATH=/path/to Set path to python3 executable (if not
specified as envirnonment variable and if
executable is not in your $PATH).
--JAVA_PATH=/path/to Set path to java executable (if not
specified as environment variable and if
executable is not in your $PATH), only
required with flags --UTR=on and --addUTR=on
--GUSHR_PATH=/path/to Set path to gushr.py exectuable (if not
specified as an environment variable and if
executable is not in your $PATH), only required
with the flags --UTR=on and --addUTR=on
--MAKEHUB_PATH=/path/to Set path to make_hub.py (if option --makehub
is used).
--CDBTOOLS_PATH=/path/to cdbfasta/cdbyank are required for running
fix_in_frame_stop_codon_genes.py. Usage of
that script can be skipped with option
'--skip_fixing_broken_genes'.

EXPERT OPTIONS

--augustus_args="--some_arg=bla" One or several command line arguments to
be passed to AUGUSTUS, if several
arguments are given, separate them by
whitespace, i.e.
"--first_arg=sth --second_arg=sth".
--skipGeneMark-ES Skip GeneMark-ES and use provided
GeneMark-ES output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ET Skip GeneMark-ET and use provided
GeneMark-ET output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-EP Skip GeneMark-EP and use provided
GeneMark-EP output (e.g. provided with
--geneMarkGtf=genemark.gtf)
--skipGeneMark-ETP Skip GeneMark-ETP and use provided
GeneMark-ETP output (e.g. provided with
--gmetp_results_dir=GeneMark-ETP/)
--geneMarkGtf=file.gtf If skipGeneMark-ET is used, braker will by
default look in the working directory in
folder GeneMarkET for an already existing
gtf file. Instead, you may provide such a
file from another location. If geneMarkGtf
option is set, skipGeneMark-ES/ET/EP/ETP is
automatically also set. Note that gene and
transcript ids in the final output may not
match the ids in the input genemark.gtf
because BRAKER internally re-assigns these
ids.
In ETP mode, this option hast to be used together
with --traingenes and --hints to provide BRAKER
with results of a previous GeneMark-ETP run.
--gmetp_results_dir Location of results from a previous
GeneMark-ETP run, which will be used to
skip the GeneMark-ETP step. This option
can be used instead of --geneMarkGtf,
--traingenes, and --hints to skip GeneMark.
--rounds The number of optimization rounds used in
optimize_augustus.pl (default 5)
--skipAllTraining Skip GeneMark-EX (training and
prediction), skip AUGUSTUS training, only
runs AUGUSTUS with pre-trained and already
existing parameters (not recommended).
Hints from input are still generated.
This option automatically sets
--useexisting to true.
--useexisting Use the present config and parameter files
if they exist for 'species'; will overwrite
original parameters if BRAKER performs
an AUGUSTUS training.
--filterOutShort It may happen that a "good" training gene,
i.e. one that has intron support from
RNA-Seq in all introns predicted by
GeneMark-EX, is in fact too short. This flag
will discard such genes that have
supported introns and a neighboring
RNA-Seq supported intron upstream of the
start codon within the range of the
maximum CDS size of that gene and with a
multiplicity that is at least as high as
20% of the average intron multiplicity of
that gene.
--skipOptimize Skip optimize parameter step (not
recommended).
--skipIterativePrediction Skip iterative prediction in --epmode (does
not affect other modes, saves a bit of runtime)
--skipGetAnnoFromFasta Skip calling the python3 script
getAnnoFastaFromJoingenes.py from the
AUGUSTUS tool suite. This script requires
python3, biopython and re (regular
expressions) to be installed. It produces
coding sequence and protein FASTA files
from AUGUSTUS gene predictions and provides
information about genes with in-frame stop
codons. If you enable this flag, these files
will not be produced and python3 and
the required modules will not be necessary
for running brkaker.pl.
--skip_fixing_broken_genes If you do not have python3, you can choose
to skip the fixing of stop codon including
genes (not recommended).
--eval=reference.gtf Reference set to evaluate predictions
against (using evaluation scripts from GaTech)
--eval_pseudo=pseudo.gff3 File with pseudogenes that will be excluded
from accuracy evaluation (may be empty file)
--AUGUSTUS_hints_preds=s File with AUGUSTUS hints predictions; will
use this file as basis for UTR training;
only UTR training and prediction is
performed if this option is given.
--flanking_DNA=n Size of flanking region, must only be
specified if --AUGUSTUS_hints_preds is given
(for UTR training in a separate braker.pl
run that builds on top of an existing run)
--verbosity=n 0 -> run braker.pl quiet (no log)
1 -> only log warnings
2 -> also log configuration
3 -> log all major steps
4 -> very verbose, log also small steps
--downsampling_lambda=d The distribution of introns in training
gene structures generated by GeneMark-EX
has a huge weight on single-exon and
few-exon genes. Specifying the lambda
parameter of a poisson distribution will
make braker call a script for downsampling
of training gene structures according to
their number of introns distribution, i.e.
genes with none or few exons will be
downsampled, genes with many exons will be
kept. Default value is 2.
If you want to avoid downsampling, you have
to specify 0.
--checkSoftware Only check whether all required software
is installed, no execution of BRAKER
--nocleanup Skip deletion of all files that are typically not
used in an annotation project after
running braker.pl. (For tracking any
problems with a braker.pl run, you
might want to keep these files, therefore
nocleanup can be activated.)

DEVELOPMENT OPTIONS (PROBABLY STILL DYSFUNCTIONAL)

--splice_sites=patterns list of splice site patterns for UTR
prediction; default: GTAG, extend like this:
--splice_sites=GTAG,ATAC,...
this option only affects UTR training
example generation, not gene prediction
by AUGUSTUS
--overwrite Overwrite existing files (except for
species parameter files) Beware, currently
not implemented properly!
--extrinsicCfgFiles=file1,file2,... Depending on the mode in which braker.pl
is executed, it may require one ore several
extrinsicCfgFiles. Don't use this option
unless you know what you are doing!
--stranded=+,-,+,-,... If UTRs are trained, i.e.~strand-specific
bam-files are supplied and coverage
information is extracted for gene prediction,
create stranded ep hints. The order of
strand specifications must correspond to the
order of bam files. Possible values are
+, -, .
If stranded data is provided, ONLY coverage
data from the stranded data is used to
generate UTR examples! Coverage data from
unstranded data is used in the prediction
step, only.
The stranded label is applied to coverage
data, only. Intron hints are generated
from all libraries treated as "unstranded"
(because splice site filtering eliminates
intron hints from the wrong strand, anyway).
--optCfgFile=ppx.cfg Optional custom config file for AUGUSTUS
for running PPX (currently not
implemented)
--grass Switch this flag on if you are using braker.pl
for predicting genes in grasses with
GeneMark-EX. The flag will enable
GeneMark-EX to handle GC-heterogenicity
within genes more properly.
NOTHING IMPLEMENTED FOR GRASS YET!
--transmasked_fasta=file.fa Transmasked genome FASTA file for GeneMark-EX
(to be used instead of the regular genome
FASTA file).
--min_contig=INT Minimal contig length for GeneMark-EX, could
for example be set to 10000 if transmasked_fasta
option is used because transmasking might
introduce many very short contigs.
--translation_table=INT Change translation table from non-standard
to something else.
DOES NOT WORK YET BECAUSE BRAKER DOESNT
SWITCH TRANSLATION TABLE FOR GENEMARK-EX, YET!
--gc_probability=DECIMAL Probablity for donor splice site pattern GC
for gene prediction with GeneMark-EX,
default value is 0.001
--gm_max_intergenic=INT Adjust maximum allowed size of intergenic
regions in GeneMark-EX. If not used, the value
is automatically determined by GeneMark-EX.
--traingenes=file.gtf Training genes that are used instead of training
genes generated with GeneMark.
In ETP mode, this option can be used together
with --geneMarkGtf and --hints to provide BRAKER
with results of a previous GeneMark-ETP run, so
that the GeneMark-ETP step can be skipped.
In this case, use training.gtf from that run as
argument.
--UTR=on create UTR training examples from RNA-Seq
coverage data; requires options
--bam=rnaseq.bam.
Alternatively, if UTR parameters already
exist, training step will be skipped and
those pre-existing parameters are used.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!
--addUTR=on Adds UTRs from RNA-Seq coverage data to
augustus.hints.gtf file. Does not perform
training of AUGUSTUS or gene prediction with
AUGUSTUS and UTR parameters.
DO NOT USE IN CONTAINER!
TRY NOT TO USE AT ALL!

EXAMPLE

To run with RNA-Seq

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--bam=accepted_hits.bam
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=rnaseq.gff

To run with protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--hints=prothint_augustus.gff

To run with RNA-Seq and protein sequences

braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --rnaseq_sets_ids=id_rnaseq1,id_rnaseq2 \
--rnaseq_sets_dir=/path/to/local/rnaseq/files
braker.pl [OPTIONS] --genome=genome.fa --species=speciesname \
--prot_seq=proteins.fa --bam=id_rnaseq1.bam,id_rnaseq2.bam
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [https://github.com/Gaius-Augustus/BRAKER BRAKER]

=== System ===
64-bit Linux

Canu-Sapelo2

2025-04-17T16:50:07Z

Jordan:

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
2.2, 2.3

=== Author / Distributor ===

[http://www.repeatmasker.org Canu]

=== Description ===
"Canu is a fork of the Celera Assembler, designed for high-noise single-molecule sequencing (such as the PacBio RS II/Sequel or Oxford Nanopore MinION). "
More details are at Canu's [https://canu.readthedocs.io/en/latest/index.html documentation].

=== Running Program ===

'''Version 2.2'''

To use this version, please load the module with
<pre class="gscript">
ml canu/2.2-GCCcore-11.2.0
</pre>
or with
<pre class="gscript">
ml canu/2.2-GCCcore-11.3.0
</pre>

'''Version 2.3'''

To use this version, please load the module with
<pre class="gscript">
ml canu/2.3-GCC-12.3.0
</pre>
or with
<pre class="gscript">
ml canu/2.3-GCCcore-12.3.0
</pre>

When you invoke canu, please use the gridOptions to pass queueing system options for the jobs the canu pipeline submits. At a minimum, please specify a partition, the number of tasks and the walltime. For example, use '''gridOptions = --partition=batch --ntasks=1 --cpus-per-task=4 --time=168:00:00 '''. The --mem-per-cpu option will be added automatically by the pipeline scripts, but you can also add it if the pipeline is not able to estimate the memory needed correctly.

Here is an example of a shell script, sub.sh, to run Canu on the batch queue:
<pre class="gscript">
#!/bin/bash
#SBATCH --partition=batch
#SBATCH --job-name=canujobname
#SBATCH --ntasks=1
#SBATCH --time=1:00:00
#SBATCH --mem=10G

cd $SLURM_SUBMIT_DIR

ml canu/2.2-GCCcore-11.2.0

canu gridOptions=" --partition=batch --ntasks=1 --cpus-per-task=4 --time=168:00:00 " [options]

</pre>
where [options] need to be replaced by the options (command and arguments) you want to use. Other parameters of the job, such as the maximum wall clock time, maximum memory, the number of cores per node, and the job name need to be modified appropriately as well. Please note that the Slurm headers (#SBATCH lines) are only for Canu's initial job. The resource limits of all of the jobs that Canu spawns will be determined by what is defined in the gridOptions.

To submit the job submission use the command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===
<pre class="gcommand">
[cft07037@d2-13 canu]$ ml canu/2.2-GCCcore-11.2.0
[cft07037@d2-13 canu]$ canu --help

usage: canu [-version] [-citation] \
[-haplotype | -correct | -trim | -assemble | -trim-assemble] \
[-s <assembly-specifications-file>] \
-p <assembly-prefix> \
-d <assembly-directory> \
genomeSize=<number>[g|m|k] \
[other-options] \
[-haplotype{NAME} illumina.fastq.gz] \
[-corrected] \
[-trimmed] \
[-pacbio |
-nanopore |
-pacbio-hifi] file1 file2 ...

example: canu -d run1 -p godzilla genomeSize=1g -nanopore-raw reads/*.fasta.gz

To restrict canu to only a specific stage, use:
-haplotype - generate haplotype-specific reads
-correct - generate corrected reads
-trim - generate trimmed reads
-assemble - generate an assembly
-trim-assemble - generate trimmed reads and then assemble them

The assembly is computed in the -d <assembly-directory>, with output files named
using the -p <assembly-prefix>. This directory is created if needed. It is not
possible to run multiple assemblies in the same directory.

The genome size should be your best guess of the haploid genome size of what is being
assembled. It is used primarily to estimate coverage in reads, NOT as the desired
assembly size. Fractional values are allowed: '4.7m' equals '4700k' equals '4700000'

Some common options:
useGrid=string
- Run under grid control (true), locally (false), or set up for grid control
but don't submit any jobs (remote)
rawErrorRate=fraction-error
- The allowed difference in an overlap between two raw uncorrected reads. For lower
quality reads, use a higher number. The defaults are 0.300 for PacBio reads and
0.500 for Nanopore reads.
correctedErrorRate=fraction-error
- The allowed difference in an overlap between two corrected reads. Assemblies of
low coverage or data with biological differences will benefit from a slight increase
in this. Defaults are 0.045 for PacBio reads and 0.144 for Nanopore reads.
gridOptions=string
- Pass string to the command used to submit jobs to the grid. Can be used to set
maximum run time limits. Should NOT be used to set memory limits; Canu will do
that for you.
minReadLength=number
- Ignore reads shorter than 'number' bases long. Default: 1000.
minOverlapLength=number
- Ignore read-to-read overlaps shorter than 'number' bases long. Default: 500.
A full list of options can be printed with '-options'. All options can be supplied in
an optional sepc file with the -s option.

For TrioCanu, haplotypes are specified with the -haplotype{NAME} option, with any
number of haplotype-specific Illumina read files after. The {NAME} of each haplotype
is free text (but only letters and numbers, please). For example:
-haplotypeNANNY nanny/*gz
-haplotypeBILLY billy1.fasta.gz billy2.fasta.gz

Reads can be either FASTA or FASTQ format, uncompressed, or compressed with gz, bz2 or xz.

Reads are specified by the technology they were generated with, and any processing performed.

[processing]
-corrected
-trimmed

[technology]
-pacbio <files>
-nanopore <files>
-pacbio-hifi <files>

Complete documentation at http://canu.readthedocs.org/en/latest/
</pre>

<pre class="gcommand">
[cft07037@d2-13 canu]$ ml canu/2.2-GCCcore-11.2.0
[cft07037@d2-13 canu]$ canu -options
batConcurrency Unused, only one process supported
batMemory Approximate maximum memory usage, in gigabytes, default is the maxMemory limit
batOptions Advanced options to bogart
batStageSpace Amount of local disk space needed to stage data for unitig construction jobs
batThreads Number of threads to use; default is the maxThreads limit
cnsConcurrency If grid not enabled, number of unitig consensus jobs to run at the same time; default is n_proc / n_threads
cnsConsensus Which consensus algorithm to use; 'pbdagcon' (fast, reliable); 'utgcns' (multialignment output); 'quick' (single read mosaic); default 'pbdagcon'
cnsErrorRate Consensus expects alignments at about this error rate
cnsMaxCoverage Limit unitig consensus to at most this coverage; default '40' = unlimited
cnsMemory Amount of memory, in gigabytes, to use for unitig consensus jobs
cnsPartitions Attempt to create this many consensus jobs; default '0' = based on the largest tig
cnsStageSpace Amount of local disk space needed to stage data for unitig consensus jobs
cnsThreads Number of threads to use for unitig consensus jobs
contigFilter Parameters to filter out 'unassembled' unitigs. Five values: minReads minLength singleReadSpan lowCovFraction lowCovDepth
corConcurrency If grid not enabled, number of read correction jobs to run at the same time; default is n_proc / n_threads
corConsensus Which consensus algorithm to use; only 'falcon' is supported; default 'falcon'
corErrorRate Only use raw alignments below this error rate to construct corrected reads
corFilter Method to filter short reads from correction; 'quick' or 'expensive'; default 'expensive'
corMaxEvidenceCoverageGlobal Limit reads used for correction to supporting at most this coverage; default: '1.0x' = 1.0 * estimated coverage
corMaxEvidenceCoverageLocal Limit reads being corrected to at most this much evidence coverage; default: '2.0x' = 2.0 * estimated coverage
corMaxEvidenceErate Limit read correction to only overlaps at or below this fraction error; default: unlimited
corMemory Amount of memory, in gigabytes, to use for read correction jobs
corMhapBlockSize Number of reads per GB of memory allowed (mhapMemory)
cormhapConcurrency If grid not enabled, number of mhap overlaps for correction jobs to run at the same time; default is n_proc / n_threads
corMhapFilterThreshold Value between 0 and 1. kmers which comprise more than this percentage of the input are downweighted
corMhapFilterUnique Expert option: True or false, supress the low-frequency k-mer distribution based on them being likely noise and not true overlaps. Threshold auto-computed based on error rate and coverage.
cormhapMemory Amount of memory, in gigabytes, to use for mhap overlaps for correction jobs
corMhapMerSize K-mer size for seeds in mhap
corMhapNoTf Expert option: True or false, do not use tf weighting, only idf of tf-idf.
corMhapOptions Expert option: free-form parameters to pass to MHAP.
corMhapOrderedMerSize K-mer size for second-stage filter in mhap
corMhapPipe Report results to a pipe instead of *large* files.
corMhapSensitivity Coarse sensitivity level: 'low', 'normal' or 'high'. Set automatically based on coverage; 'high' <= 30x < 'normal' < 60x <= 'low'
cormhapStageSpace Amount of local disk space needed to stage data for mhap overlaps for correction jobs
cormhapThreads Number of threads to use for mhap overlaps for correction jobs
corMhapVersion Version of the MHAP jar file to use
corMinCoverage Minimum number of bases supporting each corrected base, if less than this sequences are split; default based on input read coverage: 0 <= 30x < 4 < 60x <= 4
corMinEvidenceLength Limit read correction to only overlaps longer than this; default: unlimited
corMMapBlockSize Number of reads per 1GB; memory * blockSize = the size of block loaded into memory per job
cormmapConcurrency If grid not enabled, number of mmap overlaps for correction jobs to run at the same time; default is n_proc / n_threads
cormmapMemory Amount of memory, in gigabytes, to use for mmap overlaps for correction jobs
corMMapMerSize K-mer size for seeds in minmap
cormmapStageSpace Amount of local disk space needed to stage data for mmap overlaps for correction jobs
cormmapThreads Number of threads to use for mmap overlaps for correction jobs
corOutCoverage Only correct the longest reads up to this coverage; default 40
corOverlapper Which overlap algorithm to use for correction
corovlConcurrency If grid not enabled, number of overlaps for correction jobs to run at the same time; default is n_proc / n_threads
corOvlErrorRate Overlaps above this error rate are not computed
corOvlFilter Filter overlaps based on expected kmers vs observed kmers
corOvlFrequentMers Do not seed overlaps with these kmers
corOvlHashBits Width of the kmer hash. Width 22=1gb, 23=2gb, 24=4gb, 25=8gb. Plus 10b per corOvlHashBlockLength
corOvlHashBlockLength Amount of sequence (bp) to load into the overlap hash table
corOvlHashLoad Maximum hash table load. If set too high, table lookups are inefficent; if too low, search overhead dominates run time; default 0.75
corovlMemory Amount of memory, in gigabytes, to use for overlaps for correction jobs
corOvlMerDistinct K-mer frequency threshold; the least frequent fraction of distinct mers can seed overlaps
corOvlMerSize K-mer size for seeds in overlaps
corOvlMerThreshold K-mer frequency threshold; mers more frequent than this count are ignored
corOvlRefBlockLength Amount of sequence (bp) to search against the hash table per batch
corovlStageSpace Amount of local disk space needed to stage data for overlaps for correction jobs
corovlThreads Number of threads to use for overlaps for correction jobs
corPartitionMin Don't make a read correction partition with fewer than N reads
corPartitions Partition read correction into N jobs
corReAlign Refine overlaps by computing the actual alignment: 'true' or 'false'. Not useful for overlapper=ovl. Uses corOvlErrorRate
correctedErrorRate Expected fraction error in an alignment of two corrected reads
corStageSpace Amount of local disk space needed to stage data for read correction jobs
corThreads Number of threads to use for read correction jobs
enableOEA Do overlap error adjustment - comprises two steps: read error detection (RED) and overlap error adjustment (OEA); default 'true'
executiveMemory Amount of memory, in GB, to reserve for the Canu exective process
executiveThreads Number of threads to reserve for the Canu exective process
genomeSize An estimate of the size of the genome
gnuplot Path to the gnuplot executable
gnuplotImageFormat Image format that gnuplot will generate. Default: based on gnuplot, 'png', 'svg' or 'gif'
gridEngine Grid engine configuration, not documented
gridEngineArrayMaxJobs Grid engine configuration, not documented
gridEngineArrayName Grid engine configuration, not documented
gridEngineArrayOption Grid engine configuration, not documented
gridEngineArraySubmitID Grid engine configuration, not documented
gridEngineJobID Grid engine configuration, not documented
gridEngineMemoryOption Grid engine configuration, not documented
gridEngineMemoryPerJob Grid engine configuration, not documented
gridEngineMemoryUnits Grid engine configuration, not documented
gridEngineNameOption Grid engine configuration, not documented
gridEngineNameToJobIDCommand Grid engine configuration, not documented
gridEngineNameToJobIDCommandNoArrayGrid engine configuration, not documented
gridEngineOutputOption Grid engine configuration, not documented
gridEngineResourceOption Grid engine configuration, not documented
gridEngineStageOption Grid engine configuration, not documented
gridEngineSubmitCommand Grid engine configuration, not documented
gridEngineTaskID Grid engine configuration, not documented
gridEngineThreadsOption Grid engine configuration, not documented
gridOptions Grid engine options applied to all jobs
gridOptionsbat Grid engine options applied to unitig construction jobs
gridOptionscns Grid engine options applied to unitig consensus jobs
gridOptionscor Grid engine options applied to read correction jobs
gridOptionscormhap Grid engine options applied to mhap overlaps for correction jobs
gridOptionscormmap Grid engine options applied to mmap overlaps for correction jobs
gridOptionscorovl Grid engine options applied to overlaps for correction jobs
gridOptionsExecutive Grid engine options applied to the canu executive script
gridOptionshap Grid engine options applied to haplotype assignment jobs
gridOptionsJobName Grid jobs job-name suffix
gridOptionsmeryl Grid engine options applied to mer counting jobs
gridOptionsobtmhap Grid engine options applied to mhap overlaps for trimming jobs
gridOptionsobtmmap Grid engine options applied to mmap overlaps for trimming jobs
gridOptionsobtovl Grid engine options applied to overlaps for trimming jobs
gridOptionsoea Grid engine options applied to overlap error adjustment jobs
gridOptionsovb Grid engine options applied to overlap store bucketizing jobs
gridOptionsovs Grid engine options applied to overlap store sorting jobs
gridOptionsred Grid engine options applied to read error detection jobs
gridOptionsutgmhap Grid engine options applied to mhap overlaps for unitig construction jobs
gridOptionsutgmmap Grid engine options applied to mmap overlaps for unitig construction jobs
gridOptionsutgovl Grid engine options applied to overlaps for unitig construction jobs
hapConcurrency Unused, there is only one process
hapMemory Amount of memory, in gigabytes, to use for haplotype assignment
hapStageSpace Amount of local disk space needed to stage data for haplotype assignment jobs
hapThreads Number of threads to use for haplotype assignment
hapUnknownFraction Fraction of allowed unknown bases before they are included in the assembly, between 0-1; default 0.05
homoPolyCompress Compute everything but consensus sequences using homopolymer compressed reads
java Java interpreter to use; at least version 1.8; default 'java'
javaUse64Bit Java interpreter supports the -d64 or -d32 flags; default auto
maxInputCoverage If input coverage is high, downsample to something reasonable; default 200
maxMemory Maximum memory to use by any component of the assembler
maxThreads Maximum number of compute threads to use by any component of the assembler
merylConcurrency Unused, there is only one process
merylMemory Amount of memory, in gigabytes, to use for mer counting
merylStageSpace Amount of local disk space needed to stage data for mer counting jobs
merylThreads Number of threads to use for mer counting
minimap Path to minimap2; default 'minimap2'
minInputCoverage Stop if input coverage is too low; default 10
minMemory Minimum amount of memory needed to compute the assembly (do not set unless prompted!)
minOverlapLength Overlaps shorter than this length are not computed; default 500
minReadLength Reads shorter than this length are not loaded into the assembler; default 1000
minThreads Minimum number of compute threads suggested to compute the assembly
objectStore Type of object storage used; not ready for production yet
objectStoreClient Path to the command line client used to access the object storage
objectStoreClientDA Path to the command line client used to download files from object storage
objectStoreClientUA Path to the command line client used to upload files to object storage
objectStoreNameSpace Object store parameters; specific to the type of objectStore used
objectStoreProject Object store project; specific to the type of objectStore used
obtErrorRate Stringency of overlaps to use for trimming
obtMhapBlockSize Number of reads per GB of memory allowed (mhapMemory)
obtmhapConcurrency If grid not enabled, number of mhap overlaps for trimming jobs to run at the same time; default is n_proc / n_threads
obtMhapFilterThreshold Value between 0 and 1. kmers which comprise more than this percentage of the input are downweighted
obtMhapFilterUnique Expert option: True or false, supress the low-frequency k-mer distribution based on them being likely noise and not true overlaps. Threshold auto-computed based on error rate and coverage.
obtmhapMemory Amount of memory, in gigabytes, to use for mhap overlaps for trimming jobs
obtMhapMerSize K-mer size for seeds in mhap
obtMhapNoTf Expert option: True or false, do not use tf weighting, only idf of tf-idf.
obtMhapOptions Expert option: free-form parameters to pass to MHAP.
obtMhapOrderedMerSize K-mer size for second-stage filter in mhap
obtMhapPipe Report results to a pipe instead of *large* files.
obtMhapSensitivity Coarse sensitivity level: 'low', 'normal' or 'high'. Set automatically based on coverage; 'high' <= 30x < 'normal' < 60x <= 'low'
obtmhapStageSpace Amount of local disk space needed to stage data for mhap overlaps for trimming jobs
obtmhapThreads Number of threads to use for mhap overlaps for trimming jobs
obtMhapVersion Version of the MHAP jar file to use
obtMMapBlockSize Number of reads per 1GB; memory * blockSize = the size of block loaded into memory per job
obtmmapConcurrency If grid not enabled, number of mmap overlaps for trimming jobs to run at the same time; default is n_proc / n_threads
obtmmapMemory Amount of memory, in gigabytes, to use for mmap overlaps for trimming jobs
obtMMapMerSize K-mer size for seeds in minmap
obtmmapStageSpace Amount of local disk space needed to stage data for mmap overlaps for trimming jobs
obtmmapThreads Number of threads to use for mmap overlaps for trimming jobs
obtOverlapper Which overlap algorithm to use for overlap based trimming
obtovlConcurrency If grid not enabled, number of overlaps for trimming jobs to run at the same time; default is n_proc / n_threads
obtOvlErrorRate Overlaps at or below this error rate are used to trim reads
obtOvlFilter Filter overlaps based on expected kmers vs observed kmers
obtOvlFrequentMers Do not seed overlaps with these kmers
obtOvlHashBits Width of the kmer hash. Width 22=1gb, 23=2gb, 24=4gb, 25=8gb. Plus 10b per obtOvlHashBlockLength
obtOvlHashBlockLength Amount of sequence (bp) to load into the overlap hash table
obtOvlHashLoad Maximum hash table load. If set too high, table lookups are inefficent; if too low, search overhead dominates run time; default 0.75
obtovlMemory Amount of memory, in gigabytes, to use for overlaps for trimming jobs
obtOvlMerDistinct K-mer frequency threshold; the least frequent fraction of distinct mers can seed overlaps
obtOvlMerSize K-mer size for seeds in overlaps
obtOvlMerThreshold K-mer frequency threshold; mers more frequent than this count are ignored
obtOvlRefBlockLength Amount of sequence (bp) to search against the hash table per batch
obtovlStageSpace Amount of local disk space needed to stage data for overlaps for trimming jobs
obtovlThreads Number of threads to use for overlaps for trimming jobs
obtReAlign Refine overlaps by computing the actual alignment: 'true' or 'false'. Not useful for overlapper=ovl. Uses obtOvlErrorRate
oeaBatchLength Number of bases per overlap error correction batch
oeaBatchSize Number of reads per overlap error correction batch
oeaConcurrency If grid not enabled, number of overlap error adjustment jobs to run at the same time; default is n_proc / n_threads
oeaErrorRate Only use overlaps with at most this much fraction error to find errors in reads; default utgOvlErrorRate, 0.003 for HiFi reads
oeaHaploConfirm This many or more reads will confirm a true haplotype difference; default 5
oeaMaskTrivial Mask trivial DNA in Overlap Error Adjustment; default off; on for HiFi reads
oeaMemory Amount of memory, in gigabytes, to use for overlap error adjustment jobs
oeaStageSpace Amount of local disk space needed to stage data for overlap error adjustment jobs
oeaThreads Number of threads to use for overlap error adjustment jobs
onFailure Full path to command to run on failure
onSuccess Full path to command to run on successful completion
ovbConcurrency If grid not enabled, number of overlap store bucketizing jobs to run at the same time; default is n_proc / n_threads
ovbMemory Amount of memory, in gigabytes, to use for overlap store bucketizing jobs
ovbStageSpace Amount of local disk space needed to stage data for overlap store bucketizing jobs
ovbThreads Number of threads to use for overlap store bucketizing jobs
ovsConcurrency If grid not enabled, number of overlap store sorting jobs to run at the same time; default is n_proc / n_threads
ovsMemory Amount of memory, in gigabytes, to use for overlap store sorting jobs
ovsStageSpace Amount of local disk space needed to stage data for overlap store sorting jobs
ovsThreads Number of threads to use for overlap store sorting jobs
preExec A command line to run at the start of Canu execution scripts
purgeOverlaps When to delete intermediate overlap files: never, normal (default), aggressive, dangerous
rawErrorRate Expected fraction error in an alignment of two uncorrected reads
readSamplingBias Score reads as 'random * length^bias', keep the highest scoring reads
redBatchLength Number of bases per fragment error detection batch
redBatchSize Number of reads per fragment error detection batch
redConcurrency If grid not enabled, number of read error detection jobs to run at the same time; default is n_proc / n_threads
redMemory Amount of memory, in gigabytes, to use for read error detection jobs
redStageSpace Amount of local disk space needed to stage data for read error detection jobs
redThreads Number of threads to use for read error detection jobs
saveMerCounts Save full mer counting results, sometimes useful
saveOverlaps Do not remove the overlap stores. Default: false = remove overlap stores when they're no longer needed
saveReadCorrections Save intermediate read correction files, almost never a good idea
saveReadHaplotypes Save intermediate read haplotype files, almost never a good idea
saveReads Save intermediate corrected and trimmed reads to asm.correctedReads.fasta.gz and asm.trimmedReads.fasta.gz
shell Command interpreter to use; sh-compatible (e.g., bash), NOT C-shell (csh or tcsh); default '/bin/sh'
showNext Don't run any commands, just report what would run
stageDirectory If set, copy heavily used data to this node-local location
stopAfter Stop after a specific algorithm step is completed
stopOnLowCoverage Stop if raw, corrected or trimmed read coverage is low
trimReadsCoverage Minimum depth of evidence to retain bases; default '2
trimReadsOverlap Minimum overlap between evidence to make contiguous trim; default '500'
unitigger Which unitig algorithm to use; only 'bogart' supported; default 'bogart'
useGrid If 'true', enable grid-based execution; if 'false', run all jobs on the local machine; if 'remote', create jobs for grid execution but do not submit; default 'true'
useGridbat If 'true', run module unitig construction under grid control; if 'false' run locally.
useGridcns If 'true', run module unitig consensus under grid control; if 'false' run locally.
useGridcor If 'true', run module read correction under grid control; if 'false' run locally.
useGridcormhap If 'true', run module mhap overlaps for correction under grid control; if 'false' run locally.
useGridcormmap If 'true', run module mmap overlaps for correction under grid control; if 'false' run locally.
useGridcorovl If 'true', run module overlaps for correction under grid control; if 'false' run locally.
useGridhap If 'true', run module haplotype assignment under grid control; if 'false' run locally.
useGridmeryl If 'true', run module mer counting under grid control; if 'false' run locally.
useGridobtmhap If 'true', run module mhap overlaps for trimming under grid control; if 'false' run locally.
useGridobtmmap If 'true', run module mmap overlaps for trimming under grid control; if 'false' run locally.
useGridobtovl If 'true', run module overlaps for trimming under grid control; if 'false' run locally.
useGridoea If 'true', run module overlap error adjustment under grid control; if 'false' run locally.
useGridovb If 'true', run module overlap store bucketizing under grid control; if 'false' run locally.
useGridovs If 'true', run module overlap store sorting under grid control; if 'false' run locally.
useGridred If 'true', run module read error detection under grid control; if 'false' run locally.
useGridutgmhap If 'true', run module mhap overlaps for unitig construction under grid control; if 'false' run locally.
useGridutgmmap If 'true', run module mmap overlaps for unitig construction under grid control; if 'false' run locally.
useGridutgovl If 'true', run module overlaps for unitig construction under grid control; if 'false' run locally.
utgBubbleDeviation Overlaps this much above mean of contig will be used to identify bubbles
utgChimeraType When to filter reads for contig construction: none, chimera (missing middle), uncovered (missing middle or ends), deadend (missing middle or end or no neighbor) (default)
utgErrorRate Overlaps at or below this error rate are used to construct contigs
utgGraphDeviation Overlaps this much above median will not be used for initial graph construction
utgMhapBlockSize Number of reads per GB of memory allowed (mhapMemory)
utgmhapConcurrency If grid not enabled, number of mhap overlaps for unitig construction jobs to run at the same time; default is n_proc / n_threads
utgMhapFilterThreshold Value between 0 and 1. kmers which comprise more than this percentage of the input are downweighted
utgMhapFilterUnique Expert option: True or false, supress the low-frequency k-mer distribution based on them being likely noise and not true overlaps. Threshold auto-computed based on error rate and coverage.
utgmhapMemory Amount of memory, in gigabytes, to use for mhap overlaps for unitig construction jobs
utgMhapMerSize K-mer size for seeds in mhap
utgMhapNoTf Expert option: True or false, do not use tf weighting, only idf of tf-idf.
utgMhapOptions Expert option: free-form parameters to pass to MHAP.
utgMhapOrderedMerSize K-mer size for second-stage filter in mhap
utgMhapPipe Report results to a pipe instead of *large* files.
utgMhapSensitivity Coarse sensitivity level: 'low', 'normal' or 'high'. Set automatically based on coverage; 'high' <= 30x < 'normal' < 60x <= 'low'
utgmhapStageSpace Amount of local disk space needed to stage data for mhap overlaps for unitig construction jobs
utgmhapThreads Number of threads to use for mhap overlaps for unitig construction jobs
utgMhapVersion Version of the MHAP jar file to use
utgMMapBlockSize Number of reads per 1GB; memory * blockSize = the size of block loaded into memory per job
utgmmapConcurrency If grid not enabled, number of mmap overlaps for unitig construction jobs to run at the same time; default is n_proc / n_threads
utgmmapMemory Amount of memory, in gigabytes, to use for mmap overlaps for unitig construction jobs
utgMMapMerSize K-mer size for seeds in minmap
utgmmapStageSpace Amount of local disk space needed to stage data for mmap overlaps for unitig construction jobs
utgmmapThreads Number of threads to use for mmap overlaps for unitig construction jobs
utgOverlapper Which overlap algorithm to use for unitig construction
utgovlConcurrency If grid not enabled, number of overlaps for unitig construction jobs to run at the same time; default is n_proc / n_threads
utgOvlErrorRate Overlaps at or below this error rate are used to trim reads
utgOvlFilter Filter overlaps based on expected kmers vs observed kmers
utgOvlFrequentMers Do not seed overlaps with these kmers
utgOvlHashBits Width of the kmer hash. Width 22=1gb, 23=2gb, 24=4gb, 25=8gb. Plus 10b per utgOvlHashBlockLength
utgOvlHashBlockLength Amount of sequence (bp) to load into the overlap hash table
utgOvlHashLoad Maximum hash table load. If set too high, table lookups are inefficent; if too low, search overhead dominates run time; default 0.75
utgovlMemory Amount of memory, in gigabytes, to use for overlaps for unitig construction jobs
utgOvlMerDistinct K-mer frequency threshold; the least frequent fraction of distinct mers can seed overlaps
utgOvlMerSize K-mer size for seeds in overlaps
utgOvlMerThreshold K-mer frequency threshold; mers more frequent than this count are ignored
utgOvlRefBlockLength Amount of sequence (bp) to search against the hash table per batch
utgovlStageSpace Amount of local disk space needed to stage data for overlaps for unitig construction jobs
utgovlThreads Number of threads to use for overlaps for unitig construction jobs
utgReAlign Refine overlaps by computing the actual alignment: 'true' or 'false'. Not useful for overlapper=ovl. Uses utgOvlErrorRate
utgRepeatConfusedBP Repeats where the next best edge is at least this many bp shorter will not be split
utgRepeatConfusedPC Repeats where the next best edge is at least this many percent shorter will not be split
utgRepeatDeviation Overlaps this much above mean unitig error rate will not be used for repeat splitting
</pre>
[[#top|Back to Top]]

=== Installation ===

Source code obtained from https://github.com/marbl/canu/releases/download/v2.2

=== System ===
64-bit Linux

Software

2025-04-02T14:07:14Z

Jordan: /* Partial List of Installed Software */

On Sapelo2 and on the Teaching cluster users have the option to install their own software or use software installed centrally by the GACRC staff. Detailed information on how to find and access software packages installed on Sapelo2 are available at the [[Software on Sapelo2]] page and on the teaching cluster at the [[Software on Teaching Cluster]] page.

Below is a very limited list of software packages installed on Sapelo2 and on the Teaching cluster. We only intend to make an application page for software packages that requires extra documentation not covered in [[Software on Sapelo2]] or [[Software on Teaching Cluster]].

To view a list of all modules installed on Sapelo2/Teaching, along with a short description of each module, please run the following command on Sapelo2/Teaching:
<pre class="gcommand">
module spider
</pre>
You can also use the command above to search for a specific application. For more information, please see [[Lmod]].

The '''file transfer''' nodes ('''xfer''' nodes) do not have environment modules installed. File transfer utilities are installed centrally on the xfer nodes and available on the users' default PATH. Such utilities include bs (basespace-cli), fpsync, rclone, rsync, etc.
https://wiki.gacrc.uga.edu/wiki/Main_Page

'''Note:''' Although we have not made individual software pages for the applications installed on Sapelo2, there are currently over 1300 software packages installed there. For more information, please see [[Software on Sapelo2]].

==Partial List of Installed Software==
Last updated '''{{REVISIONYEAR}}-{{REVISIONMONTH}}-{{REVISIONDAY2}}'''. This is a partial list of installed software so there may be software or versions of software available on Sapelo2 which are not included in this table.
'''Note:''' Click on the icons to the right of the column headings to sort the table.
[[#0|0-9]] [[#A|A]] [[#B|B]] [[#C|C]] [[#D|D]] [[#E|E]] [[#F|F]] [[#G|G]] [[#H|H]] [[#I|I]] [[#J|J]] [[#K|K]] [[#L|L]] [[#M|M]] [[#N|N]] [[#O|O]] [[#P|P]] [[#Q|Q]] [[#R|R]] [[#S|S]] [[#T|T]] [[#U|U]] [[#V|V]] [[#W|W]] [[#X|X]] [[#Y|Y]] [[#Z|Z]]
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" width="200px"| Name
! scope="col" class="unsortable" width="240px"| Version
! scope="col" width="160px"| Category
! scope="col" width="60px"| Cluster
! scope="col" width="20px"| Top
|-













| [[AlphaFold-Sapelo2|AlphaFold 2]] || 2.2.4, 2.3.1, 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->
| [[AlphaFold3-Sapelo2|AlphaFold 3]] || 3.0.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[AMBER-Sapelo2|AMBER]] || 22 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[anvi'o-Sapelo2|anvi'o]] || 4, 5.1, 5.3, 5.4, 6.1, 7, 7.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
















| [[basespace-cli-Sapelo2|basespace-cli]] || 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










|| [[Beast-Sapelo2|Beast]] || 1.10.4, 2.5.1, 2.6.3, 2.7.3, 2.7.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










| [[BioPython-Sapelo2|BioPython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[Biopython-Teaching|Biopython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-




| [[BLAST+-Sapelo2|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] ||[[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST+-Teaching|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[BLAST-Sapelo2|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST-Teaching|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Bowtie2-Sapelo2|Bowtie2]] || 2.2.3, 2.3.3, 2.3.4.1, 2.3.4.2, 2.3.5.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Bowtie2-Teaching|Bowtie2]] || 2.4.1, 2.4.4, 2.4.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


| [[BRAKER-Sapelo2|BRAKER]] || 2.1.6, 3.0.3, 3.0.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



| [[BUSCO-Sapelo2|BUSCO]] || 2.0, 4.0.5, 5.4.7, 5.5.0, 5.8.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[Cactus-Sapelo2|Cactus]] || 1.2.3, 2.0.3, 2.4.3, 2.4.4, 2.5.0, 2.6.0, 2.6.7, 2.6.9, 2.7.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Canu-Sapelo2|Canu]] || 2.2||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[CellRanger-ATAC-Sapelo2|CellRanger-ATAC]] || 2.1.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[Chimera-Teaching|Chimera]] || 1.16 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-























| [[cryoSPARC-Sapelo2|CryoSPARC]] || 3.3.1 || [[:Category:Engineering|Engineering]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Cromwell-Sapelo2|Cromwell]] || 56 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|[[CUDA-Sapelo2|CUDA]] || 11.3.1, 11.4.1, 11.7.0, 12.0.0, 12.1.1, 12.2.0, 12.4.0, 12.6.0 ||[[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Cytoscape-Sapelo2|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Cytoscape-Teaching|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-



























|-->
| [[dMRIharmonization-Sapelo2|dMRIharmonization]] || 20240227 || [[:Category:Engineering|Engineering]]|| [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-->







| [[EIGENSOFT-Sapelo2|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[EIGENSOFT-Teaching|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Flankophile-Sapelo2|Flankophile]] || 0.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-






































|-
| [[Funannotate-Sapelo2|Funannotate]] || 1.8.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[GAUSSIAN-Sapelo2|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[GAUSSIAN-Teaching|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[GaussView-Sapelo2|GaussView]] || 5, 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[GaussView-Teaching|GaussView]] || 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[GEM-Sapelo2|GEM]] || 1.5.1 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[GeneMarkES-Sapelo2|GeneMarkET]] || 4.71 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





























|[[gocryptfs-Sapelo2|gocryptfs]] || 2.5.2 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[GROMACS-Sapelo2 |GROMACS]] || 2021.5, 2023.1, 2023.4 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


















| [[HISAT2-Sapelo2|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[HISAT2-Teaching|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[HpcGridRunner-Sapelo2|HPCGridRunner]] || 1.0.2 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










|-
| [[iCommands-Sapelo2|iCommands]] || 4.2.8 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[InterProScan-Sapelo2|InterProScan]] || 5.63-95.0, 5.64-96.0, 5.66-98.0 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[IQ-Tree-Sapelo2|IQ-Tree]] || 1.6.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[IQ-Tree-Teaching|IQ-Tree]] || 1.6.12|| [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-








| [[Java-Teaching|Java]] || 1.8.0_241, 11.0.16, 13.0.2, 15.0.1, 17.0.6 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Jupyter-Sapelo2|Jupyter]] || 6.4.12, 6.5.2, 6.5.4, 6.5.6, 7.0.3, 7.2.0 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Jupyter-Teaching|Jupyter]] || 6.4.12 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[LAMMPS-Sapelo2|LAMMPS]] || 23Jun2022 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



































| [[Magma-Sapelo2|Magma]] || 2.27-1 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-


| [[Maker-Sapelo2|Maker]] || 3.01.04 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[MATLAB-Sapelo2|MATLAB]] || R2023a, R2023b || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[Medusa-Sapelo2 |Medusa]] || 1.6 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-













| [[MetaPhlAn4-Sapelo2|MetaPhlAn4]] || 4.0.3.1, 4.0.6, 4.1.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
























| [[MOTECC_qmdcp-Teaching|MOTECC_qmdcp]] || 90 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Mothur-Sapelo2|Mothur]] || 1.45.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->




















































| [[Ont-Guppy-Sapelo2|Ont-Guppy]] || 6.5.7 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[ORCA-Sapelo2 |ORCA]] || 4.2.1, 5.0.4 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




|[[ORP-Sapelo2|ORP]] || 2.2.6, 2.2.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[OrthoFinder-Sapelo2|OrthoFinder]] || 2.5.4, 2.5.5 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[PAML-Teaching|PAML]] || 4.9j, 4.10.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->























| [[Perl-Teaching|Perl]] || 5.30.0, 5.30.2, 5.32.0, 5.32.1, 5.34.0, 5.34.1, 5.36.0, 5.36.1 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[Phenix-Sapelo2|Phenix]] || 1.21.2-5419 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

























































|-
| [[Python-Sapelo2 |Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Python-Teaching|Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->






| [[QIIME2-Sapelo2|QIIME2]] || 2020.6, 2023.2, 2023.5.1, 2023.7, 2023.9, 2024.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[R-Sapelo2 |R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 ||[[:Category:Statistics|Statistics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[R-Teaching|R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 || [[:Category:Statistics|Statistics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[racon-Sapelo2 |Racon]] || 1.5.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[RAxML-NG-Sapelo2 |RAxML-NG]] || 1.2.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Sapelo2 |RAxML]] || 8.2.11, 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Teaching|RAxML]] || 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

















| [[RevBayes-Teaching|RevBayes]] || 1.1.0, 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Rosetta-Sapelo2 |Rosetta]] || 2021.16.61629, 2022.46.334 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[Rstudio-Sapelo2|Rstudio]] || 2023.06.1-524 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[SAMtools-Teaching|SAMtools]] || 0.1.20, 1.6, 1.14, 1.16.1, 1.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

| [[SequenceTubeMap-Sapelo2|sequenceTubeMap]] || 20230906 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->









































| [[snpEff-Sapelo2|snpEff]] || 5.0e || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





















| [[SRAToolKit-Sapelo2 |SRAToolKit]] || 3.0.1, 3.0.3 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[StringTie-Teaching|StringTie]] || 2.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Structure-Sapelo2|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Structure-Teaching|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


































| [[Trinity-Sapelo2|Trinity]] || 2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[UBCG-Sapelo2|UBCG]] || 3.0
|[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-














| [[Velvet-Sapelo2|Velvet]] || 1.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->






















|}
[[#top|Back To Top]] [[File:Up20.png|20px|link=#top]]

[[Category:Sapelo2]][[Category:Teaching]][[Category:Software]]

Software

2025-04-02T13:54:45Z

Jordan: /* Partial List of Installed Software */

On Sapelo2 and on the Teaching cluster users have the option to install their own software or use software installed centrally by the GACRC staff. Detailed information on how to find and access software packages installed on Sapelo2 are available at the [[Software on Sapelo2]] page and on the teaching cluster at the [[Software on Teaching Cluster]] page.

Below is a very limited list of software packages installed on Sapelo2 and on the Teaching cluster. We only intend to make an application page for software packages that requires extra documentation not covered in [[Software on Sapelo2]] or [[Software on Teaching Cluster]].

To view a list of all modules installed on Sapelo2/Teaching, along with a short description of each module, please run the following command on Sapelo2/Teaching:
<pre class="gcommand">
module spider
</pre>
You can also use the command above to search for a specific application. For more information, please see [[Lmod]].

The '''file transfer''' nodes ('''xfer''' nodes) do not have environment modules installed. File transfer utilities are installed centrally on the xfer nodes and available on the users' default PATH. Such utilities include bs (basespace-cli), fpsync, rclone, rsync, etc.
https://wiki.gacrc.uga.edu/wiki/Main_Page

'''Note:''' Although we have not made individual software pages for the applications installed on Sapelo2, there are currently over 1300 software packages installed there. For more information, please see [[Software on Sapelo2]].

==Partial List of Installed Software==
Last updated '''{{REVISIONYEAR}}-{{REVISIONMONTH}}-{{REVISIONDAY2}}'''. This is a partial list of installed software so there may be software or versions of software available on Sapelo2 which are not included in this table.
'''Note:''' Click on the icons to the right of the column headings to sort the table.
[[#0|0-9]] [[#A|A]] [[#B|B]] [[#C|C]] [[#D|D]] [[#E|E]] [[#F|F]] [[#G|G]] [[#H|H]] [[#I|I]] [[#J|J]] [[#K|K]] [[#L|L]] [[#M|M]] [[#N|N]] [[#O|O]] [[#P|P]] [[#Q|Q]] [[#R|R]] [[#S|S]] [[#T|T]] [[#U|U]] [[#V|V]] [[#W|W]] [[#X|X]] [[#Y|Y]] [[#Z|Z]]
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" width="200px"| Name
! scope="col" class="unsortable" width="240px"| Version
! scope="col" width="160px"| Category
! scope="col" width="60px"| Cluster
! scope="col" width="20px"| Top
|-













| [[AlphaFold-Sapelo2|AlphaFold 2]] || 2.2.4, 2.3.1, 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->
| [[AlphaFold3-Sapelo2|AlphaFold 3]] || 3.0.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[AMBER-Sapelo2|AMBER]] || 22 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[anvi'o-Sapelo2|anvi'o]] || 4, 5.1, 5.3, 5.4, 6.1, 7, 7.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
















| [[basespace-cli-Sapelo2|basespace-cli]] || 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










|| [[Beast-Sapelo2|Beast]] || 1.10.4, 2.5.1, 2.6.3, 2.7.3, 2.7.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










| [[BioPython-Sapelo2|BioPython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[Biopython-Teaching|Biopython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-




| [[BLAST+-Sapelo2|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] ||[[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST+-Teaching|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[BLAST-Sapelo2|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST-Teaching|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Bowtie2-Sapelo2|Bowtie2]] || 2.2.3, 2.3.3, 2.3.4.1, 2.3.4.2, 2.3.5.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Bowtie2-Teaching|Bowtie2]] || 2.4.1, 2.4.4, 2.4.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


| [[BRAKER-Sapelo2|BRAKER]] || 2.1.6, 3.0.3, 3.0.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



| [[BUSCO-Sapelo2|BUSCO]] || 2.0, 4.0.5, 5.4.7, 5.5.0, 5.8.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[Cactus-Sapelo2|Cactus]] || 1.2.3, 2.0.3, 2.4.3, 2.4.4, 2.5.0, 2.6.0, 2.6.7, 2.6.9, 2.7.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Canu-Sapelo2|Canu]] || 2.2||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[CellRanger-ATAC-Sapelo2|CellRanger-ATAC]] || 2.1.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[Chimera-Teaching|Chimera]] || 1.16 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-























| [[cryoSPARC-Sapelo2|CryoSPARC]] || 3.3.1 || [[:Category:Engineering|Engineering]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Cromwell-Sapelo2|Cromwell]] || 56 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|[[CUDA-Sapelo2|CUDA]] || 11.3.1, 11.4.1, 11.7.0, 12.0.0, 12.1.1, 12.2.0, 12.4.0, 12.6.0 ||[[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Cytoscape-Sapelo2|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Cytoscape-Teaching|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-



























|-->
| [[dMRIharmonization-Sapelo2|dMRIharmonization]] || 20240227 || [[:Category:Engineering|Engineering]]|| [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-->







| [[EIGENSOFT-Sapelo2|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[EIGENSOFT-Teaching|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Flankophile-Sapelo2|Flankophile]] || 0.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-






































|-
| [[Funannotate-Sapelo2|Funannotate]] || 1.8.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[GAUSSIAN-Sapelo2|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[GAUSSIAN-Teaching|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[GaussView-Sapelo2|GaussView]] || 5, 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[GaussView-Teaching|GaussView]] || 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[GEM-Sapelo2|GEM]] || 1.5.1 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[GeneMarkET-Sapelo2|GeneMarkET]] || 4.71 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





























|[[gocryptfs-Sapelo2|gocryptfs]] || 2.5.2 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[GROMACS-Sapelo2 |GROMACS]] || 2021.5, 2023.1, 2023.4 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


















| [[HISAT2-Sapelo2|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[HISAT2-Teaching|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[HpcGridRunner-Sapelo2|HPCGridRunner]] || 1.0.2 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










|-
| [[iCommands-Sapelo2|iCommands]] || 4.2.8 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[InterProScan-Sapelo2|InterProScan]] || 5.63-95.0, 5.64-96.0, 5.66-98.0 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[IQ-Tree-Sapelo2|IQ-Tree]] || 1.6.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[IQ-Tree-Teaching|IQ-Tree]] || 1.6.12|| [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-








| [[Java-Teaching|Java]] || 1.8.0_241, 11.0.16, 13.0.2, 15.0.1, 17.0.6 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Jupyter-Sapelo2|Jupyter]] || 6.4.12, 6.5.2, 6.5.4, 6.5.6, 7.0.3, 7.2.0 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Jupyter-Teaching|Jupyter]] || 6.4.12 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[LAMMPS-Sapelo2|LAMMPS]] || 23Jun2022 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



































| [[Magma-Sapelo2|Magma]] || 2.27-1 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-


| [[Maker-Sapelo2|Maker]] || 3.01.04 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[MATLAB-Sapelo2|MATLAB]] || R2023a, R2023b || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[Medusa-Sapelo2 |Medusa]] || 1.6 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-













| [[MetaPhlAn4-Sapelo2|MetaPhlAn4]] || 4.0.3.1, 4.0.6, 4.1.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
























| [[MOTECC_qmdcp-Teaching|MOTECC_qmdcp]] || 90 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Mothur-Sapelo2|Mothur]] || 1.45.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->




















































| [[Ont-Guppy-Sapelo2|Ont-Guppy]] || 6.5.7 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[ORCA-Sapelo2 |ORCA]] || 4.2.1, 5.0.4 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




|[[ORP-Sapelo2|ORP]] || 2.2.6, 2.2.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[OrthoFinder-Sapelo2|OrthoFinder]] || 2.5.4, 2.5.5 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[PAML-Teaching|PAML]] || 4.9j, 4.10.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->























| [[Perl-Teaching|Perl]] || 5.30.0, 5.30.2, 5.32.0, 5.32.1, 5.34.0, 5.34.1, 5.36.0, 5.36.1 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[Phenix-Sapelo2|Phenix]] || 1.21.2-5419 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

























































|-
| [[Python-Sapelo2 |Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Python-Teaching|Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->






| [[QIIME2-Sapelo2|QIIME2]] || 2020.6, 2023.2, 2023.5.1, 2023.7, 2023.9, 2024.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[R-Sapelo2 |R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 ||[[:Category:Statistics|Statistics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[R-Teaching|R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 || [[:Category:Statistics|Statistics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[racon-Sapelo2 |Racon]] || 1.5.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[RAxML-NG-Sapelo2 |RAxML-NG]] || 1.2.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Sapelo2 |RAxML]] || 8.2.11, 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Teaching|RAxML]] || 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

















| [[RevBayes-Teaching|RevBayes]] || 1.1.0, 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Rosetta-Sapelo2 |Rosetta]] || 2021.16.61629, 2022.46.334 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[Rstudio-Sapelo2|Rstudio]] || 2023.06.1-524 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[SAMtools-Teaching|SAMtools]] || 0.1.20, 1.6, 1.14, 1.16.1, 1.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

| [[SequenceTubeMap-Sapelo2|sequenceTubeMap]] || 20230906 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->









































| [[snpEff-Sapelo2|snpEff]] || 5.0e || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





















| [[SRAToolKit-Sapelo2 |SRAToolKit]] || 3.0.1, 3.0.3 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[StringTie-Teaching|StringTie]] || 2.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Structure-Sapelo2|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Structure-Teaching|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


































| [[Trinity-Sapelo2|Trinity]] || 2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[UBCG-Sapelo2|UBCG]] || 3.0
|[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-














| [[Velvet-Sapelo2|Velvet]] || 1.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->






















|}
[[#top|Back To Top]] [[File:Up20.png|20px|link=#top]]

[[Category:Sapelo2]][[Category:Teaching]][[Category:Software]]

SnpEff-Sapelo2

2025-03-26T19:25:51Z

Jordan:

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
5.0e

=== Author / Distributor ===

[http://sourceforge.net/projects/snpeff/ snpEff]

=== Description ===
"SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes)."
More details are at [http://sourceforge.net/projects/snpeff/ snpEff]

=== Running Program ===

The latest version of this application is available with Java 11 and either GCCcore-11.2.0 or GCCcore-11.3.0

To use these version, please load the module with
<pre class="gscript">
ml snpEff/5.0e-GCCcore-11.2.0-Java-11
</pre>
or
<pre class="gscript">
ml snpEff/5.0e-GCCcore-11.3.0-Java-11
</pre>

Note that we do not currently have any snpEff databases available centrally; users are instead encouraged to download (or create, as described [http://pcingola.github.io/SnpEff/snpeff/build_db/ here]) whatever databases they need in their own space by first copying the snpEff config file (available at either /apps/eb/snpEff/5.0e-GCCcore-11.2.0-Java-11/snpEff.config or /apps/eb/snpEff/5.0e-GCCcore-11.3.0-Java-11/snpEff.config) to their own space and modifying it as needed, then using the '-c' option to specify the location of this custom config file when running the software as described below and at the snpEff website.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_snpEff 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=snpEff.%j.out 
<nowiki>#</nowiki>SBATCH --error=snpEff.%j.err 

cd $SLURM_SUBMIT_DIR 
ml snpEff/5.0e-GCCcore-11.3.0-Java-11 
snpEff [command ] [options] [files] 
</div>
In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Please refer to [[Running_Jobs_on_Sapelo2]].

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===
(Note, despite what the documentation states, it is NOT necessary to use "java -jar snpEff.jar" or "java -jar snpEff.jar command"...you can simply type "snpEff [command] -h")
<pre class="gcommand">
ml snpEff/5.0e-GCCcore-11.3.0-Java-11
$ snpEff -h
SnpEff version SnpEff 5.0e (build 2021-03-09 06:01), by Pablo Cingolani
Usage: snpEff [command] [options] [files]

Run 'java -jar snpEff.jar command' for help on each specific command

Available commands:
[eff|ann] : Annotate variants / calculate effects (you can use either 'ann' or 'eff', they mean the same). Default: ann (no command or 'ann').
build : Build a SnpEff database.
buildNextProt : Build a SnpEff for NextProt (using NextProt's XML files).
cds : Compare CDS sequences calculated form a SnpEff database to the one in a FASTA file. Used for checking databases correctness.
closest : Annotate the closest genomic region.
count : Count how many intervals (from a BAM, BED or VCF file) overlap with each genomic interval.
databases : Show currently available databases (from local config file).
download : Download a SnpEff database.
dump : Dump to STDOUT a SnpEff database (mostly used for debugging).
genes2bed : Create a bed file from a genes list.
len : Calculate total genomic length for each marker type.
pdb : Build interaction database (based on PDB data).
protein : Compare protein sequences calculated form a SnpEff database to the one in a FASTA file. Used for checking databases correctness.
seq : Show sequence (from command line) translation.
show : Show a text representation of genes or transcripts coordiantes, DNA sequence and protein sequence.
translocReport : Create a translocations report (from VCF file).

Generic options:
-c , -config : Specify config file
-configOption name=value : Override a config file option
-d , -debug : Debug mode (very verbose).
-dataDir <path> : Override data_dir parameter from config file.
-download : Download a SnpEff database, if not available locally. Default: true
-nodownload : Do not download a SnpEff database, if not available locally.
-h , -help : Show this help and exit
-noLog : Do not report usage statistics to server
-q , -quiet : Quiet mode (do not show any messages or errors)
-v , -verbose : Verbose mode
-version : Show version number and exit

Database options:
-canon : Only use canonical transcripts.
-canonList <file> : Only use canonical transcripts, replace some transcripts using the 'gene_id transcript_id' entries in <file>.
-interaction : Annotate using inteactions (requires interaciton database). Default: true
-interval <file> : Use a custom intervals in TXT/BED/BigBed/VCF/GFF file (you may use this option many times)
-maxTSL <TSL_number> : Only use transcripts having Transcript Support Level lower than <TSL_number>.
-motif : Annotate using motifs (requires Motif database). Default: true
-nextProt : Annotate using NextProt (requires NextProt database).
-noGenome : Do not load any genomic database (e.g. annotate using custom files).
-noExpandIUB : Disable IUB code expansion in input variants
-noInteraction : Disable inteaction annotations
-noMotif : Disable motif annotations.
-noNextProt : Disable NextProt annotations.
-onlyReg : Only use regulation tracks.
-onlyProtein : Only use protein coding transcripts. Default: false
-onlyTr <file.txt> : Only use the transcripts in this file. Format: One transcript ID per line.
-reg <name> : Regulation track to use (this option can be used add several times).
-ss , -spliceSiteSize <int> : Set size for splice sites (donor and acceptor) in bases. Default: 2
-spliceRegionExonSize <int> : Set size for splice site region within exons. Default: 3 bases
-spliceRegionIntronMin <int> : Set minimum number of bases for splice site region within intron. Default: 3 bases
-spliceRegionIntronMax <int> : Set maximum number of bases for splice site region within intron. Default: 8 bases
-strict : Only use 'validated' transcripts (i.e. sequence has been checked). Default: false
-ud , -upDownStreamLen <int> : Set upstream downstream interval length (in bases)

</pre>
[[#top|Back to Top]]

=== Installation ===

Source code is obtained from [http://sourceforge.net/projects/snpeff/ snpEff]

=== System ===
64-bit Linux

SnpEff-Sapelo2

2025-03-26T19:05:25Z

Jordan: Created 03/26/2025 to provide users documentation on using snpEff in our specific environment (in particular, to let users know where to find the required config file)

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
5.0e

=== Author / Distributor ===

[http://sourceforge.net/projects/snpeff/ snpEff]

=== Description ===
"SnpEff is a variant annotation and effect prediction tool. It annotates and predicts the effects of genetic variants (such as amino acid changes)."
More details are at [http://sourceforge.net/projects/snpeff/ snpEff]

=== Running Program ===

The latest version of this application is available with Java 11 and either GCCcore-11.2.0 or GCCcore-11.3.0

To use these version, please load the module with
<pre class="gscript">
ml snpEff/5.0e-GCCcore-11.2.0-Java-11
</pre>
or
<pre class="gscript">
ml snpEff/5.0e-GCCcore-11.3.0-Java-11
</pre>

Note that we do not currently have any snpEff databases available centrally; users are instead encourage to download (or create, as described [http://pcingola.github.io/SnpEff/snpeff/build_db/ here]) whatever databases they need in their own space by first copying the snpEff config file (available at either /apps/eb/snpEff/5.0e-GCCcore-11.2.0-Java-11/snpEff.config or /apps/eb/snpEff/5.0e-GCCcore-11.3.0-Java-11/snpEff.config) to their own space and modifying it as needed, then using the '-c' option to specify the location of this custom config file when running the software as described below and at the snpEff website.

Here is an example of a shell script, sub.sh, to run on the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_snpEff 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=snpEff.%j.out 
<nowiki>#</nowiki>SBATCH --error=snpEff.%j.err 

cd $SLURM_SUBMIT_DIR 
ml snpEff/5.0e-GCCcore-11.3.0-Java-11 
snpEff [command ] [options] [files] 
</div>
In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Please refer to [[Running_Jobs_on_Sapelo2]].

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===
(Note, despite what the documentation states, it is NOT necessary to use "java -jar snpEff.jar" or "java -jar snpEff.jar command"...you can simply type "snpEff [command] -h")
<pre class="gcommand">
ml snpEff/5.0e-GCCcore-11.3.0-Java-11
$ snpEff -h
SnpEff version SnpEff 5.0e (build 2021-03-09 06:01), by Pablo Cingolani
Usage: snpEff [command] [options] [files]

Run 'java -jar snpEff.jar command' for help on each specific command

Available commands:
[eff|ann] : Annotate variants / calculate effects (you can use either 'ann' or 'eff', they mean the same). Default: ann (no command or 'ann').
build : Build a SnpEff database.
buildNextProt : Build a SnpEff for NextProt (using NextProt's XML files).
cds : Compare CDS sequences calculated form a SnpEff database to the one in a FASTA file. Used for checking databases correctness.
closest : Annotate the closest genomic region.
count : Count how many intervals (from a BAM, BED or VCF file) overlap with each genomic interval.
databases : Show currently available databases (from local config file).
download : Download a SnpEff database.
dump : Dump to STDOUT a SnpEff database (mostly used for debugging).
genes2bed : Create a bed file from a genes list.
len : Calculate total genomic length for each marker type.
pdb : Build interaction database (based on PDB data).
protein : Compare protein sequences calculated form a SnpEff database to the one in a FASTA file. Used for checking databases correctness.
seq : Show sequence (from command line) translation.
show : Show a text representation of genes or transcripts coordiantes, DNA sequence and protein sequence.
translocReport : Create a translocations report (from VCF file).

Generic options:
-c , -config : Specify config file
-configOption name=value : Override a config file option
-d , -debug : Debug mode (very verbose).
-dataDir <path> : Override data_dir parameter from config file.
-download : Download a SnpEff database, if not available locally. Default: true
-nodownload : Do not download a SnpEff database, if not available locally.
-h , -help : Show this help and exit
-noLog : Do not report usage statistics to server
-q , -quiet : Quiet mode (do not show any messages or errors)
-v , -verbose : Verbose mode
-version : Show version number and exit

Database options:
-canon : Only use canonical transcripts.
-canonList <file> : Only use canonical transcripts, replace some transcripts using the 'gene_id transcript_id' entries in <file>.
-interaction : Annotate using inteactions (requires interaciton database). Default: true
-interval <file> : Use a custom intervals in TXT/BED/BigBed/VCF/GFF file (you may use this option many times)
-maxTSL <TSL_number> : Only use transcripts having Transcript Support Level lower than <TSL_number>.
-motif : Annotate using motifs (requires Motif database). Default: true
-nextProt : Annotate using NextProt (requires NextProt database).
-noGenome : Do not load any genomic database (e.g. annotate using custom files).
-noExpandIUB : Disable IUB code expansion in input variants
-noInteraction : Disable inteaction annotations
-noMotif : Disable motif annotations.
-noNextProt : Disable NextProt annotations.
-onlyReg : Only use regulation tracks.
-onlyProtein : Only use protein coding transcripts. Default: false
-onlyTr <file.txt> : Only use the transcripts in this file. Format: One transcript ID per line.
-reg <name> : Regulation track to use (this option can be used add several times).
-ss , -spliceSiteSize <int> : Set size for splice sites (donor and acceptor) in bases. Default: 2
-spliceRegionExonSize <int> : Set size for splice site region within exons. Default: 3 bases
-spliceRegionIntronMin <int> : Set minimum number of bases for splice site region within intron. Default: 3 bases
-spliceRegionIntronMax <int> : Set maximum number of bases for splice site region within intron. Default: 8 bases
-strict : Only use 'validated' transcripts (i.e. sequence has been checked). Default: false
-ud , -upDownStreamLen <int> : Set upstream downstream interval length (in bases)

</pre>
[[#top|Back to Top]]

=== Installation ===

Source code is obtained from [http://sourceforge.net/projects/snpeff/ snpEff]

=== System ===
64-bit Linux

Software

2025-03-26T18:46:52Z

Jordan: /* Partial List of Installed Software */

On Sapelo2 and on the Teaching cluster users have the option to install their own software or use software installed centrally by the GACRC staff. Detailed information on how to find and access software packages installed on Sapelo2 are available at the [[Software on Sapelo2]] page and on the teaching cluster at the [[Software on Teaching Cluster]] page.

Below is a very limited list of software packages installed on Sapelo2 and on the Teaching cluster. We only intend to make an application page for software packages that requires extra documentation not covered in [[Software on Sapelo2]] or [[Software on Teaching Cluster]].

To view a list of all modules installed on Sapelo2/Teaching, along with a short description of each module, please run the following command on Sapelo2/Teaching:
<pre class="gcommand">
module spider
</pre>
You can also use the command above to search for a specific application. For more information, please see [[Lmod]].

The '''file transfer''' nodes ('''xfer''' nodes) do not have environment modules installed. File transfer utilities are installed centrally on the xfer nodes and available on the users' default PATH. Such utilities include bs (basespace-cli), fpsync, rclone, rsync, etc.
https://wiki.gacrc.uga.edu/wiki/Main_Page

'''Note:''' Although we have not made individual software pages for the applications installed on Sapelo2, there are currently over 1300 software packages installed there. For more information, please see [[Software on Sapelo2]].

==Partial List of Installed Software==
Last updated '''{{REVISIONYEAR}}-{{REVISIONMONTH}}-{{REVISIONDAY2}}'''. This is a partial list of installed software so there may be software or versions of software available on Sapelo2 which are not included in this table.
'''Note:''' Click on the icons to the right of the column headings to sort the table.
[[#0|0-9]] [[#A|A]] [[#B|B]] [[#C|C]] [[#D|D]] [[#E|E]] [[#F|F]] [[#G|G]] [[#H|H]] [[#I|I]] [[#J|J]] [[#K|K]] [[#L|L]] [[#M|M]] [[#N|N]] [[#O|O]] [[#P|P]] [[#Q|Q]] [[#R|R]] [[#S|S]] [[#T|T]] [[#U|U]] [[#V|V]] [[#W|W]] [[#X|X]] [[#Y|Y]] [[#Z|Z]]
{| width="100%" border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" width="200px"| Name
! scope="col" class="unsortable" width="240px"| Version
! scope="col" width="160px"| Category
! scope="col" width="60px"| Cluster
! scope="col" width="20px"| Top
|-













| [[AlphaFold-Sapelo2|AlphaFold 2]] || 2.2.4, 2.3.1, 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->
| [[AlphaFold3-Sapelo2|AlphaFold 3]] || 3.0.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[AMBER-Sapelo2|AMBER]] || 22 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[anvi'o-Sapelo2|anvi'o]] || 4, 5.1, 5.3, 5.4, 6.1, 7, 7.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
















| [[basespace-cli-Sapelo2|basespace-cli]] || 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










|| [[Beast-Sapelo2|Beast]] || 1.10.4, 2.5.1, 2.6.3, 2.7.3, 2.7.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->










| [[BioPython-Sapelo2|BioPython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[Biopython-Teaching|Biopython]] || 1.75, 1.76, 1.78, 1.79, 1.81 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-




| [[BLAST+-Sapelo2|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] ||[[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST+-Teaching|BLAST+]] || 2.2.31, 2.10.1, 2.11.0, 2.12.0, 2.13.0, 2.14.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[BLAST-Sapelo2|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[BLAST-Teaching|BLAST]] || 2.2.26 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Bowtie2-Sapelo2|Bowtie2]] || 2.2.3, 2.3.3, 2.3.4.1, 2.3.4.2, 2.3.5.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Bowtie2-Teaching|Bowtie2]] || 2.4.1, 2.4.4, 2.4.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


| [[BRAKER-Sapelo2|BRAKER]] || 2.1.6, 3.0.3, 3.0.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



| [[BUSCO-Sapelo2|BUSCO]] || 2.0, 4.0.5, 5.4.7, 5.5.0, 5.8.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[Cactus-Sapelo2|Cactus]] || 1.2.3, 2.0.3, 2.4.3, 2.4.4, 2.5.0, 2.6.0, 2.6.7, 2.6.9, 2.7.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

| [[Canu-Sapelo2|Canu]] || 2.2||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[CellRanger-ATAC-Sapelo2|CellRanger-ATAC]] || 2.1.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[Chimera-Teaching|Chimera]] || 1.16 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-























| [[cryoSPARC-Sapelo2|CryoSPARC]] || 3.3.1 || [[:Category:Engineering|Engineering]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Cromwell-Sapelo2|Cromwell]] || 56 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|[[CUDA-Sapelo2|CUDA]] || 11.3.1, 11.4.1, 11.7.0, 12.0.0, 12.1.1, 12.2.0, 12.4.0, 12.6.0 ||[[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Cytoscape-Sapelo2|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Cytoscape-Teaching|Cytoscape]] || 3.9.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-



























|-->
| [[dMRIharmonization-Sapelo2|dMRIharmonization]] || 20240227 || [[:Category:Engineering|Engineering]]|| [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-->







| [[EIGENSOFT-Sapelo2|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[EIGENSOFT-Teaching|EIGENSOFT]] || 7.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Flankophile-Sapelo2|Flankophile]] || 0.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-






































|-
| [[Funannotate-Sapelo2|Funannotate]] || 1.8.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[GAUSSIAN-Sapelo2|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
|[[GAUSSIAN-Teaching|GAUSSIAN]] || 09, 16 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[GaussView-Sapelo2|GaussView]] || 5, 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[GaussView-Teaching|GaussView]] || 6.1 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[GEM-Sapelo2|GEM]] || 1.5.1 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[GeneMarkES-Sapelo2|GeneMarkES]] || 4.57, 4.71 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





























|[[gocryptfs-Sapelo2|gocryptfs]] || 2.5.2 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[GROMACS-Sapelo2 |GROMACS]] || 2021.5, 2023.1, 2023.4 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


















| [[HISAT2-Sapelo2|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[HISAT2-Teaching|HISAT2]] || 2.2.1, 3 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[HpcGridRunner-Sapelo2|HPCGridRunner]] || 1.0.2 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










|-
| [[iCommands-Sapelo2|iCommands]] || 4.2.8 || [[:Category:Tools|Tools]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[InterProScan-Sapelo2|InterProScan]] || 5.63-95.0, 5.64-96.0, 5.66-98.0 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[IQ-Tree-Sapelo2|IQ-Tree]] || 1.6.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[IQ-Tree-Teaching|IQ-Tree]] || 1.6.12|| [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-








| [[Java-Teaching|Java]] || 1.8.0_241, 11.0.16, 13.0.2, 15.0.1, 17.0.6 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-






| [[Jupyter-Sapelo2|Jupyter]] || 6.4.12, 6.5.2, 6.5.4, 6.5.6, 7.0.3, 7.2.0 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Jupyter-Teaching|Jupyter]] || 6.4.12 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
|- -->













| [[LAMMPS-Sapelo2|LAMMPS]] || 23Jun2022 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-



































| [[Magma-Sapelo2|Magma]] || 2.27-1 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-


| [[Maker-Sapelo2|Maker]] || 3.01.04 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[MATLAB-Sapelo2|MATLAB]] || R2023a, R2023b || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-









| [[Medusa-Sapelo2 |Medusa]] || 1.6 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-













| [[MetaPhlAn4-Sapelo2|MetaPhlAn4]] || 4.0.3.1, 4.0.6, 4.1.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
























| [[MOTECC_qmdcp-Teaching|MOTECC_qmdcp]] || 90 || [[:Category:Chemistry|Chemistry]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Mothur-Sapelo2|Mothur]] || 1.45.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->




















































| [[Ont-Guppy-Sapelo2|Ont-Guppy]] || 6.5.7 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-





| [[ORCA-Sapelo2 |ORCA]] || 4.2.1, 5.0.4 || [[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




|[[ORP-Sapelo2|ORP]] || 2.2.6, 2.2.8 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[OrthoFinder-Sapelo2|OrthoFinder]] || 2.5.4, 2.5.5 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->








| [[PAML-Teaching|PAML]] || 4.9j, 4.10.5 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->























| [[Perl-Teaching|Perl]] || 5.30.0, 5.30.2, 5.32.0, 5.32.1, 5.34.0, 5.34.1, 5.36.0, 5.36.1 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[Phenix-Sapelo2|Phenix]] || 1.21.2-5419 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-

























































|-
| [[Python-Sapelo2 |Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Python-Teaching|Python]] || 2.7.18, 3.7.4, 3.8.6, 3.9.6, 3.10.4, 3.10.8 || [[:Category:Programming|Programming]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->






| [[QIIME2-Sapelo2|QIIME2]] || 2020.6, 2023.2, 2023.5.1, 2023.7, 2023.9, 2024.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->







| [[R-Sapelo2 |R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 ||[[:Category:Statistics|Statistics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-
| [[R-Teaching|R]] || 3.6.2, 4.1.2, 4.2.1, 4.3.1, 4.3.2 || [[:Category:Statistics|Statistics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[racon-Sapelo2 |Racon]] || 1.5.0 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-




| [[RAxML-NG-Sapelo2 |RAxML-NG]] || 1.2.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Sapelo2 |RAxML]] || 8.2.11, 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[RAxML-Teaching|RAxML]] || 8.2.12 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

















| [[RevBayes-Teaching|RevBayes]] || 1.1.0, 1.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-







| [[Rosetta-Sapelo2 |Rosetta]] || 2021.16.61629, 2022.46.334 ||[[:Category:Chemistry|Chemistry]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->


| [[Rstudio-Sapelo2|Rstudio]] || 2023.06.1-524 || [[:Category:Other|Other]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-










| [[SAMtools-Teaching|SAMtools]] || 0.1.20, 1.6, 1.14, 1.16.1, 1.17 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|- -->

| [[SequenceTubeMap-Sapelo2|sequenceTubeMap]] || 20230906 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:sapelo2|sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->









































| [[snpEff-Sapelo2|snpEff]] || 5.0e || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->





















| [[SRAToolKit-Sapelo2 |SRAToolKit]] || 3.0.1, 3.0.3 ||[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->













| [[StringTie-Teaching|StringTie]] || 2.2.1 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-
| [[Structure-Sapelo2|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->
| [[Structure-Teaching|Structure]] || 2.3.4 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Teaching|Teaching]] || [[File:Up20.png|20px|link=#top]]
|-


































| [[Trinity-Sapelo2|Trinity]] || 2.5.1, 2.8.4, 2.8.5, 2.15.1, 2.15.2 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->



| [[UBCG-Sapelo2|UBCG]] || 3.0
|[[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|-














| [[Velvet-Sapelo2|Velvet]] || 1.2.10 || [[:Category:Bioinformatics|Bioinformatics]] || [[:Category:Sapelo2|Sapelo2]] || [[File:Up20.png|20px|link=#top]]
|- -->






















|}
[[#top|Back To Top]] [[File:Up20.png|20px|link=#top]]

[[Category:Sapelo2]][[Category:Teaching]][[Category:Software]]

AlphaFold3-Sapelo2

2025-03-25T14:39:58Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). More specifically, the "unifiedmem" version is for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens, and this version will only work on the A100 nodes due to architectural conflicts with the H100 nodes; to use this version, please use the following Slurm header in your submission script in place of the corresponding header seen in the sample job below (and note that you don't need the "constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-03-25T14:16:36Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). More specifically, the "unifiedmem" version is for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens, and this version will only work on the A100 nodes due to architectural conflicts with the H100 nodes; to use this version, please use the following Slurm header in your submission script in place of the corresponding header seen in the sample job below (and note that you don't need the "constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-03-25T14:14:09Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

/apps/singularity-images/alphafold-3.0.1-unifiedmem.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, in an interactive job (i.e., NOT a login/submit node, where Singularity does not work):
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). More specifically, the "unifiedmem" version is for proteins and other complexes that are large enough to exceed the standard maximum of 5,120 tokens, and this version will only work on the A100 nodes due to architectural conflicts with the H100 nodes; to use this version, please use the following Slurm header in your submission script in place of the corresponding header seen in the sample job below (and note that you don't need the "constraint" header for this):
<pre class="gscript">
...
#SBATCH --gres=gpu:A100:1
...
</pre>

AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-03-11T13:06:52Z

Jordan: /* Version */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0, 3.0.1

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-03-04T16:53:14Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

AlphaFold3-Sapelo2

2025-03-04T16:51:16Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

'''Version 3.0.1'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.1.sif

</pre>
You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.1.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>
Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

[[#top|Back to Top]]

GeneMarkES-Sapelo2

2025-02-24T17:23:26Z

Jordan: /* Running Program */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]
=== Category ===

Bioinformatics

=== Program On ===

Sapelo2

=== Version ===
4.71

=== Author / Distributor ===
[http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Description ===
"Gene Prediction in Eukaryotes. Novel genomes can be analyzed by the program GeneMark-ES utilizing unsupervised training."
More details are at [http://exon.gatech.edu/GeneMark/ GeneMarkES]

=== Running Program ===

Also refer to [[Running Jobs on Sapelo2]]

In order to use geneMarker you will need to download a key file and put it into your home directory. Please follow instructions as given below:

1) Go to http://topaz.gatech.edu/GeneMark/license_download.cgi, fill the requested fields are read the license text. Note: you can select any tool and platform actually, e.g. GeneMark-ES/ET/EP+ ver 4.72_lic and LINUX64 kernel 3.10-5.

2) After pressing "I agree ..." button you will be redirected to a download page. You will need to download the 64bit key file only. The key file gm_key_64.gz will be downloaded to your local drive. Then please transfer it to the cluster using a transfer node or Globus. Please refer to [[Transferring Files]].

3) The key file downloaded is in gzip format, on the cluster please unpack it (with gunzip) and move it to ~/.gm_key (the name should be exactly like this, with a dot in the beginning), for example:

<pre class="gcommand">
gunzip gm_key_64.gz
mv gm_key_64 ~/.gm_key
</pre>

where ~ is your home directory. i.e /home/MyID. Once the .gm_key file has been placed in your home dir you should be able to run GeneMarker.

'''Version 4.71'''

Version 4.71, installed at

* /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/
* /apps/eb/GeneMark-ET/4.71-GCCcore-11.2.0/

To use it, please load the module with:
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.3.0
</div>

or
<div class="gscript2">
module load GeneMark-ET/4.71-GCCcore-11.2.0
</div>

Here is an example of a shell script sub.sh to run it at the batch queue:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=geneMarkJob 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=log.%j.out 
<nowiki>#</nowiki>SBATCH --error=log.%j.err 

cd $SLURM_SUBMIT_DIR 

module load GeneMark-ET/4.71-GCCcore-11.3.0

gmes_petap.pl [options]
</div>

In the real submission script, at least all the above underlined values need to be reviewed or to be replaced by the proper values.

Here is an example of job submission command:
<pre class="gcommand">
sbatch ./sub.sh
</pre>

=== Documentation ===

<pre class="gcommand">
module load GeneMark-ET/4.71-GCCcore-11.3.0
gmes_petap.pl

# -------------------
Usage: /apps/eb/GeneMark-ET/4.71-GCCcore-11.3.0/gmes_petap.pl [options] --sequence [filename]

GeneMark-ES Suite version 4.71_lic
Suite includes GeneMark.hmm, GeneMark-ES, GeneMark-ET and GeneMark-EP algorithms.

Input sequence/s should be in FASTA format.

Select one of the gene prediction algorithms:

To run GeneMark-ES self-training algorithm
--ES

To run GeneMark-ET with hints from transcriptome splice alignments
--ET [filename]; file with intron coordinates from RNA-Seq read splice alignment in GFF format
--et_score [number]; default 10; minimum score of intron in initiation of the ET algorithm

To run GeneMark-EP with hints from protein splice alignments
--EP
--dbep [filename]; file with protein database in FASTA format
--ep_score [number,number]; default 4,0.25; minimum score of intron in initiation of the EP algorithm
or
--EP [filename]; file with intron coordinates from protein splice alignment in GFF format

To run GeneMark.hmm predictions using previously derived model
--predict_with [filename]; file with species specific gene prediction parameters

To run ES, ET or EP with branch point model. This option is most useful for fungal genomes
--fungus

To run hmm, ES, ET or EP in PLUS mode (prediction with hints)
--evidence [filename]; file with hints in GFF format

To run algorithms with alternative genetic codes
--gcode [number]; default 1; supported 1 and 6/26

Output formatting options:
--format [label]; default GTF; output gene prediction in GTF of GFF3 format
--work_dir [folder name]; default current working directory .;

Masking option
--soft_mask [number] or [auto]; default auto; to indicate that lowercase letters stand for repeats;
algorithm hard masks only lowercase repeats longer than specified length
In 'auto' mode hard masking threshold is selected by algorithm based on the size of the input genome
--mask_penalty [number] or [auto]; default 0.03;

Run options
--cores [number]; default 1; to run program with multiple threads
--pbs to run on cluster with PBS support
--v verbose

Optional sequence pre-processing parameters
--max_contig [number]; default 5000000; will split input genomic sequence into contigs shorter then max_contig
--min_contig [number]; default 50000 (10000 fungi);
will ignore contigs shorter than min_contig in training
--max_gap [number]; default 5000; will split sequence at gaps longer than max_gap
Letters 'n' and 'N' are interpreted as standing within gaps
--max_mask [number]; default 5000; will split sequence at repeats longer then max_mask
Letters 'x' and 'X' are interpreted as results of hard masking of repeats

Optional parameters
--max_intron [number]; default 10000 (3000 fungi); maximum length of intron
--max_intergenic [number]; default 50000 (10000 fungi); maximum length of intergenic regions
--min_contig_in_predict [number]; default 500; minimum allowed length of contig in prediction step
--min_gene_in_predict [number]; default 300 (120 fungi); minimum allowed gene length in prediction step
--gc_donor [value]; default 0.001; transition probability to GC donor in the range 0..1;
'off' switches GC donor model OFF
--gc3 [number]; GC3 cutoff in training for grasses

Developer options
--training to run only training step of algorithm; applicable to ES, ET or EP
--prediction to run only prediction step of algorithms using species parameters from previously executed training; applicable to ES, ET or EP
--usr_cfg [filename]; use custom configuration from this file
--ini_mod [filename]; use this file with parameters for algorithm initiation
--key_bin
--debug
# -------------------
</pre>
[[#top|Back to Top]]

=== Installation ===

source code from [http://exon.gatech.edu/GeneMark/license_download.cgi GeneMarkES]

=== System ===
64-bit Linux

Bioinformatics Databases

2025-01-17T20:48:19Z

Jordan: /* Installed Bioinformatics Databases */

As part of our services, the GACRC builds and hosts local copies of frequently cited application data, and provides assistance for sharing data among GACRC members. Datasets are located in the commonly shared "/db" filesystem. [[BLAST Databases-Sapelo2 | NCBI BLAST datasets]] are pre-formatted to work with BLAST and BLAST+ and are located in "/db/ncbiblast/" and are organized by date.

NCBI BLAST Datasets can be loaded in the same way as software modules. This allows users to replicate results by always being able to use a time stamped version of a database. The time stamp corresponds to the download date of the dataset. To search for available NCBI BLAST dataset modules run the command:

<code>ml spider ncbiblastdb</code>

You can then load a database by running the command module load. For example to load the databases timestamped at 02/02/2024:

<code>module load ncbiblastdb/20240202</code>

Loading this module sets the environment variable BLASTDB to /db/ncbiblast/20240202. This allows BLAST+ to search that directory for databases. You can then use the name of the database you would like to use.(nt, nr, etc.) Here is an example of a shell script to run BLAST+ on the batch queue using the database nt:

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_BLAST+ 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=BLAST+.%j.out 
<nowiki>#</nowiki>SBATCH --error=BLAST+.%j.err 

cd $SLURM_SUBMIT_DIR 
module load BLAST+/2.13.0-gompi-2022a 
module load ncbiblastdb/20240202 
blastn -query example.fasta -out results.out -db nt 
</div>

In your actual submission script, use your own discretion for the Slurm header values.

NCBI's nr and nt datasets are the most commonly used at the GACRC. GACRC staff will update these datasets '''every 3 months''' as well as other NCBI BLAST datasets: cdd_delta, human_genome, mouse_genome, nrte, refseq_protein, refseq_rna, swissprot, and taxdb datasets.

Various subject datasets, e.g. pfam, bowtie indexes of human and mouse, and NCBI bacterial datasets, are hosted as well. However, these datasets are not accessed with a module. These datasets either don’t need updating, the source does not update frequently, or the datasets are not used frequently by our users. These datasets will only be updated by user request.
Instead of using a module, to use these databases, use the path to the databases in your script. Below is an example using BLAST+ with a dataset not available as a module.

<div class="gscript2">
<nowiki>#</nowiki>!/bin/bash 
<nowiki>#</nowiki>SBATCH --job-name=j_BLAST+ 
<nowiki>#</nowiki>SBATCH --partition=batch 
<nowiki>#</nowiki>SBATCH --mail-type=ALL 
<nowiki>#</nowiki>SBATCH --mail-user=username@uga.edu 
<nowiki>#</nowiki>SBATCH --ntasks=1 
<nowiki>#</nowiki>SBATCH --mem=10gb 
<nowiki>#</nowiki>SBATCH --time=08:00:00 
<nowiki>#</nowiki>SBATCH --output=BLAST+.%j.out 
<nowiki>#</nowiki>SBATCH --error=BLAST+.%j.err 

cd $SLURM_SUBMIT_DIR 
module load BLAST+/2.13.0-gompi-2022a 
blastn -query example.fasta -out results.out -db /db/ncbiblast/refseq_microbial/fasta/09162020/ 
</div>

For datasets requested by individual lab groups, GACRC encourages users to maintain their own copies of databases, whose files can be installed in their group's shared "/work" area.
Similarly for datasets frequently updated by their source, the GACRC encourages users to maintain their own copies of these public databases.

The set of data which are regularly updated is open to review, and can be expanded based on available GACRC resources.

==Installed Bioinformatics Databases ==
{| border="1" cellspacing="0" cellpadding="2" align="center" class="wikitable sortable"
|-
! scope="col" | Name
! scope="col" class="unsortable" | Version
! scope="col" | Module Available
|-
| akblab || 01/28/2018 || no
|-
| [[Bacteria NCBI-sapelo2|Bacteria NCBI]] || 12/21/2017 || no
|-
| [[bowtie2|hg19,mm10 & mm9]] || 07/29/2016 || no
|-
|bowtie2
|09/12/2024
|yes
|-
| cellranger (GRCh38 and MM10) || 12/19/2021 || yes
|-
|CheckV
|01/10/2023 (v1.5)
|yes
|-
| conspred_ressources || 02/02/2017 || yes
|-
| dammit || 03/17/2021 (v1.2) || yes
|-
| deconseq || 12/19/2021 ("04242013") || yes
|-
| decontaMiner-Sapelo2|decontaMiner || 08/29/2019 || no
|-
| eggnog-mapper || 04/16/2019 || no
|-
|foldseek
|04/11/2023
|yes
|-
| funannotate || 12/19/2021 || yes
|-
| DTDB-Tk || 01/15/2021 || no
|-
| hg || 09/20/2016 || no
|-
| kegg || 09/09/2016 || no
|-
|Kraken2 (PlusPF, PlusPFP, core_nt, EuPathDB)
|05/26/2024
|yes
|-
| maker || 05/22/2018 (v2.31.9) || yes
|-
| MetaCLADE || 08/29/2019 || no
|-
| [[BLAST Databases-Sapelo2 |NCBI BLAST Databases]] || beginning of every 3 months || yes
|-
|NCBI-FCS
|12/01/2023 (v0.5.0)
|yes
|-
| [[NCBI Fasta-Sapelo2|NCBI Fasta]] || 04/06/2022 || yes
|-
| ngs (canine and mouse) || 11/03/2016 || yes
|-
| nndb || 05/03/2008 || yes
|-
| PB || 04/24/2019 || no
|-
| [[PfamDB-Sapelo2|pfam]] || 10/07/2021 || no
|-
| phylosift || 07/20/2020 || no
|-
| [[Refseq-Sapelo2|Refseq_genomic]] || || no
|-
| repbase || 01/09/2017 || no
|-
| rfam || 02/01/2017 (v12.2) || yes
|-
| seqdb || 05/22/2012 || yes
|-
| sortmerna || 03/04/2016 || no
|-
| [[TaxDB-Sapelo2|taxdb]] || 02/23/2019 || yes
|-
| topcons2 || 02/18/2021 || no
|-
| [[Uniprot-Sapelo2|Uniprot]] || 06/15/2023 || yes
|-
| [[Uniref-Sapelo2|Uniref]] || 02/25/2022 || yes
|-
|virulencefinder
|05/06/2024
|yes
|-
| [[wublast-Sapelo2|wublast]] || 04/01/2020 || no
|-
|}

AlphaFold3-Sapelo2

2025-01-15T15:10:23Z

Jordan: Updated the list of available Singularity containers to reflect the "CCDpatched" version that was built and deployed on 01/13/2025 to address some problems that a user ran into while trying to work on a protein with multiple ligands.

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container, of which there are currently two versions available:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif

/apps/singularity-images/alphafold-3.0.0-CCDpatched.sif
</pre>
Both of these containers have worked for multiple users, but the second one ("CCDpatched") seems to work better for users working on proteins with one or more ligands. You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

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AlphaFold3-Sapelo2

2024-12-13T15:15:07Z

Jordan: /* Documentation */

[[Category:Sapelo2]][[Category:Software]][[Category:Bioinformatics]]

=== Category ===
Bioinformatics

=== Program On ===
Sapelo2

=== Version ===
3.0.0

=== Author / Distributor ===
Please see https://github.com/google-deepmind/alphafold3

=== Description ===
From https://github.com/google-deepmind/alphafold3: "This package provides an implementation of the inference pipeline of AlphaFold 3. "

=== Running Program ===
Also refer to [[Running Jobs on Sapelo2]]

*'''Version 3.0.0'''

This version is installed as a singularity container:
<pre class="gscript">
/apps/singularity-images/alphafold-3.0.0.sif
</pre>
You can view the documentation for this version of AlphaFold with the following command, on an interactive (i.e., NOT a login/submit) node:
<pre class="gcommand">
singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
</pre>

Note that AlphaFold 3 is GPU dependent while also being CPU and memory intensive, meaning that it will only run on the A100 and H100 nodes and needs numerous CPUs to run in a timely/efficient manner (see the example job submission script below). AlphaFold 3 also depends on a set of database files, the current versions of which can be found at /db/AlphaFold3/20241114, and a file containing the model parameters; currently this is only available directly from Google, and can be obtained by completing the form found here: https://docs.google.com/forms/d/e/1FAIpQLSfWZAgo1aYk0O4MuAXZj8xRQ8DafeFJnldNOnh_13qAx2ceZw/viewform. Once these are obtained and placed somewhere in your storage space on Sapelo2 (/home or /work is recommended), they must be included with the '-B' or "--bind" option at runtime, as must the location of the input file and output directory (see example below). Also note that although AlphaFold 3 was created for CUDA v12.6 and we currently only have CUDA v12.2, AlphaFold 3 will still work (it prints a message noting the fact that it's running in an old version of CUDA, but then proceeds to run anyway).

Sample job submission script to run the singularity container for v3.0.0 on a GPU:

<pre class="gscript">
#!/bin/bash
#SBATCH --job-name=alphafold3 #Name your job something original
#SBATCH --partition=gpu_p #Use the GPU partition
#SBATCH --ntasks=1
#SBATCH --cpus-per-task=32 #If you use the default options, AlphaFold3 will run four simutaneous Jackhmmer processes with 8 CPUs each
#SBATCH --gres=gpu:1 #If you don’t care whether your job uses an A100 node or an H100 node (and there isn’t much difference in run time)…
#SBATCH --constraint=Milan|SapphireRapids #…this is the easiest way to specify either one without accidentally using a P100 or L4, which lack sufficient device memory
#SBATCH --mem=60gb
#SBATCH --time=120:00:00
#SBATCH --output=%x.%j.out
#SBATCH --error=%x.%j.err

cd $SLURM_SUBMIT_DIR

singularity exec \
--nv \
--bind /Path/to/input/file:/root/af_input \
--bind /Path/to/output_directory:/root/af_output \
--bind /Path/to/model_parameters:/root/models \
--bind /db/AlphaFold3/20241114:/root/public_databases \
/apps/singularity-images/alphafold-3.0.0.sif \
python /app/alphafold/run_alphafold.py \
--json_path=/root/af_input/NAME_OF_INPUT_FILE.json \
--model_dir=/root/models \
--db_dir=/root/public_databases \
--output_dir=/root/af_output

</pre>

Example input JSON file:
<pre class="gcommand">
{
"name": "2PV7",
"sequences": [
{
"protein": {
"id": ["A", "B"],
"sequence": "GMRESYANENQFGFKTINSDIHKIVIVGGYGKLGGLFARYLRASGYPISILDREDWAVAESILANADVVIVSVPINLTLETIERLKPYLTENMLLADLTSVKREPLAKMLEVHTGAVLGLHPMFGADIASMAKQVVVRCDGRFPERYEWLLEQIQIWGAKIYQTNATEHDHNMTYIQALRHFSTFANGLHLSKQPINLANLLALSSPIYRLELAMIGRLFAQDAELYADIIMDKSENLAVIETLKQTYDEKQTYDEALTFFENNDRQGFIDAFHKVRDWFGDYSEQFLKESRQLLQQANDLKQG"
}
}
],
"modelSeeds": [1],
"dialect": "alphafold3",
"version": 1
}
</pre>

Note: the above homodimer protein runs on both the H100 and A100 nodes in under an hour with 32 CPUs, so if you find your job is sitting in the queue for long periods of time, you could consider requesting significantly less than 120 hours (especially if your protein is either small or is otherwise well characterized and thus likely to align to the databases relatively quickly).

=== Documentation ===

Details and references are at: https://github.com/google-deepmind/alphafold3

'''Version 3.0.0:''' Full help options
<pre class="gcommand">
$ singularity exec /apps/singularity-images/alphafold-3.0.0.sif python /app/alphafold/run_alphafold.py --helpfull
AlphaFold 3 structure prediction script.

AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0. To view a copy of
this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/

To request access to the AlphaFold 3 model parameters, follow the process set
out at https://github.com/google-deepmind/alphafold3. You may only use these
if received directly from Google. Use is subject to terms of use available at
https://github.com/google-deepmind/alphafold3/blob/main/WEIGHTS_TERMS_OF_USE.md

flags:

/app/alphafold/run_alphafold.py:
--buckets: Strictly increasing order of token sizes for which to cache compilations. For any input with more tokens than the largest bucket size, a new bucket is created for exactly that number of tokens.
(default: '256,512,768,1024,1280,1536,2048,2560,3072,3584,4096,4608,5120')
(a comma separated list)
--db_dir: Path to the directory containing the databases.
(default: '/home/MyID/public_databases')
--flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN
kernels require Ampere GPUs or later. 'xla' uses an XLA attention implementation (no flash attention) and is portable across GPU devices.
(default: 'triton')
--hmmalign_binary_path: Path to the Hmmalign binary.
(default: '/hmmer/bin/hmmalign')
--hmmbuild_binary_path: Path to the Hmmbuild binary.
(default: '/hmmer/bin/hmmbuild')
--hmmsearch_binary_path: Path to the Hmmsearch binary.
(default: '/hmmer/bin/hmmsearch')
--input_dir: Path to the directory containing input JSON files.
--jackhmmer_binary_path: Path to the Jackhmmer binary.
(default: '/hmmer/bin/jackhmmer')
--jackhmmer_n_cpu: Number of CPUs to use for Jackhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--jax_compilation_cache_dir: Path to a directory for the JAX compilation cache.
--json_path: Path to the input JSON file.
--mgnify_database_path: Mgnify database path, used for protein MSA search.
(default: '${DB_DIR}/mgy_clusters_2022_05.fa')
--model_dir: Path to the model to use for inference.
(default: '/home/MyID/models')
--nhmmer_binary_path: Path to the Nhmmer binary.
(default: '/hmmer/bin/nhmmer')
--nhmmer_n_cpu: Number of CPUs to use for Nhmmer. Default to min(cpu_count, 8). Going beyond 8 CPUs provides very little additional speedup.
(default: '8')
(an integer)
--ntrna_database_path: NT-RNA database path, used for RNA MSA search.
(default: '${DB_DIR}/nt_rna_2023_02_23_clust_seq_id_90_cov_80_rep_seq.fasta')
--output_dir: Path to a directory where the results will be saved.
--pdb_database_path: PDB database directory with mmCIF files path, used for template search.
(default: '${DB_DIR}/pdb_2022_09_28_mmcif_files.tar')
--rfam_database_path: Rfam database path, used for RNA MSA search.
(default: '${DB_DIR}/rfam_14_9_clust_seq_id_90_cov_80_rep_seq.fasta')
--rna_central_database_path: RNAcentral database path, used for RNA MSA search.
(default: '${DB_DIR}/rnacentral_active_seq_id_90_cov_80_linclust.fasta')
--[no]run_data_pipeline: Whether to run the data pipeline on the fold inputs.
(default: 'true')
--[no]run_inference: Whether to run inference on the fold inputs.
(default: 'true')
--seqres_database_path: PDB sequence database path, used for template search.
(default: '${DB_DIR}/pdb_seqres_2022_09_28.fasta')
--small_bfd_database_path: Small BFD database path, used for protein MSA search.
(default: '${DB_DIR}/bfd-first_non_consensus_sequences.fasta')
--uniprot_cluster_annot_database_path: UniProt database path, used for protein paired MSA search.
(default: '${DB_DIR}/uniprot_all_2021_04.fa')
--uniref90_database_path: UniRef90 database path, used for MSA search. The MSA obtained by searching it is used to construct the profile for template search.
(default: '${DB_DIR}/uniref90_2022_05.fa')

absl.app:
-?,--[no]help: show this help
(default: 'false')
--[no]helpfull: show full help
(default: 'false')
--[no]helpshort: show this help
(default: 'false')
--[no]helpxml: like --helpfull, but generates XML output
(default: 'false')
--[no]only_check_args: Set to true to validate args and exit.
(default: 'false')
--[no]pdb: Alias for --pdb_post_mortem.
(default: 'false')
--[no]pdb_post_mortem: Set to true to handle uncaught exceptions with PDB post mortem.
(default: 'false')
--profile_file: Dump profile information to a file (for python -m pstats). Implies --run_with_profiling.
--[no]run_with_pdb: Set to true for PDB debug mode
(default: 'false')
--[no]run_with_profiling: Set to true for profiling the script. Execution will be slower, and the output format might change over time.
(default: 'false')
--[no]use_cprofile_for_profiling: Use cProfile instead of the profile module for profiling. This has no effect unless --run_with_profiling is set.
(default: 'true')

absl.logging:
--[no]alsologtostderr: also log to stderr?
(default: 'false')
--log_dir: directory to write logfiles into
(default: '')
--logger_levels: Specify log level of loggers. The format is a CSV list of `name:level`. Where `name` is the logger name used with `logging.getLogger()`, and `level` is a level name (INFO, DEBUG, etc). e.g. `myapp.foo:INFO,other.logger:DEBUG`
(default: '')
--[no]logtostderr: Should only log to stderr?
(default: 'false')
--[no]showprefixforinfo: If False, do not prepend prefix to info messages when it's logged to stderr, --verbosity is set to INFO level, and python logging is used.
(default: 'true')
--stderrthreshold: log messages at this level, or more severe, to stderr in addition to the logfile. Possible values are 'debug', 'info', 'warning', 'error', and 'fatal'. Obsoletes --alsologtostderr. Using --alsologtostderr cancels the effect of this flag. Please also
note that this flag is subject to --verbosity and requires logfile not be stderr.
(default: 'fatal')
-v,--verbosity: Logging verbosity level. Messages logged at this level or lower will be included. Set to 1 for debug logging. If the flag was not set or supplied, the value will be changed from the default of -1 (warning) to 0 (info) after flags are parsed.
(default: '-1')
(an integer)

absl.testing.absltest:
--test_random_seed: Random seed for testing. Some test frameworks may change the default value of this flag between runs, so it is not appropriate for seeding probabilistic tests.
(default: '301')
(an integer)
--test_randomize_ordering_seed: If positive, use this as a seed to randomize the execution order for test cases. If "random", pick a random seed to use. If 0 or not set, do not randomize test case execution order. This flag also overrides the
TEST_RANDOMIZE_ORDERING_SEED environment variable.
(default: '')
--test_srcdir: Root of directory tree where source files live
(default: '')
--test_tmpdir: Directory for temporary testing files
(default: '/tmp/absl_testing')
--xml_output_file: File to store XML test results
(default: '')

chex._src.fake:
--[no]chex_assert_multiple_cpu_devices: Whether to fail if a number of CPU devices is less than 2.
(default: 'false')
--chex_n_cpu_devices: Number of CPU threads to use as devices in tests.
(default: '1')
(an integer)

chex._src.variants:
--[no]chex_skip_pmap_variant_if_single_device: Whether to skip pmap variant if only one device is available.
(default: 'true')

absl.flags:
--flagfile: Insert flag definitions from the given file into the command line.
(default: '')
--undefok: comma-separated list of flag names that it is okay to specify on the command line even if the program does not define a flag with that name. IMPORTANT: flags in this list that have arguments MUST use the --flag=value format.
(default: '')
</pre>

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