GeneMarkES-Sapelo2: Difference between revisions

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'''Version 4.57'''
'''Version 4.57'''


Version 4.57 is at /usr/local/apps/gb/genemarkes/4.57 It can be loaded with:
Version 4.57 is at /apps/eb/GeneMark-ET/4.57-GCCcore-8.3.0 It can be loaded with:
module load GeneMark-ET/4.57-GCCcore-8.3.0
module load GeneMark-ET/4.57-GCCcore-8.3.0


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<pre  class="gcommand">
<pre  class="gcommand">
module load genemarkes/4.33
module load GeneMark-ET/4.57-GCCcore-8.3.0
perl /usr/local/apps/gb/genemarkes/4.33/gmes_petap.pl  
gmes_petap.pl  
# -------------------
# -------------------
# -------------------
Usage:  /usr/local/apps/gb/genemarkes/4.33/gmes_petap.pl  [options]  --sequence [filename]
Usage:  /apps/eb/GeneMark-ET/4.57-GCCcore-8.3.0/gmes_petap.pl  [options]  --sequence [filename]
 
GeneMark-ES Suite version 4.57_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 algorithm
 
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


GeneMark-ES Suite version 4.35
To run GeneMark.hmm predictions using previously derived model
  includes transcript (GeneMark-ET) and protein (GeneMark-EP) based training and prediction
  --predict_with [filename]; file with species specific gene prediction parameters


Input sequence/s should be in FASTA format
To run ES, ET or EP with branch point model. This option is most useful for fungal genomes
  --fungus


Algorithm options
To run hmm, ES, ET or EP in PLUS mode (prediction with hints)
  --ES           to run self-training
   --evidence    [filename]; file with hints in GFF format
  --fungus      to run algorithm with branch point model (most useful for fungal genomes)
  --ET           [filename]; to run training with introns coordinates from RNA-Seq read alignments (GFF format)
  --EP           [filename]; to run training with introns coordinates from protein splice alighnmnet (GFF format)
  --et_score    [number]; 10 (default) minimum score of intron in initiation of the ET algorithm
  --ep_score    [number]; 4 (default) minimum score of intron in initiation of the EP algorithm
   --evidence    [filename]; to use in prediction external evidence (RNA or protein) mapped to genome
  --training    to run only training step
  --prediction  to run only prediction step
  --predict_with [filename]; predict genes using this file species specific parameters (bypass regular training and prediction steps)


Sequence pre-processing options
Masking option
   --max_contig  [number]; 5000000 (default) will split input genomic sequence into contigs shorter then max_contig
   --soft_mask    [number] or [auto]; default auto; to indicate that lowercase letters stand for repeats;
  --min_contig  [number]; 50000 (default); will ignore contigs shorter then min_contig in training
                masks only lowercase repeats longer than specified length
  --max_gap      [number]; 5000 (default); will split sequence at gaps longer than max_gap
                In 'auto' mode length is adjusted based on the size of the input genome
                Letters 'n' and 'N' are interpreted as standing within gaps
  --max_mask    [number]; 5000 (default); will split sequence at repeats longer then max_mask
                Letters 'x' and 'X' are interpreted as results of hard masking of repeats
  --soft_mask    [number] to indicate that lowercase letters stand for repeats; utilize only lowercase repeats longer than specified length


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


Customizing parameters:
Optional sequence pre-processing parameters
   --max_intron          [number]; default 10000 (3000 fungi), maximum length of intron
   --max_contig  [number]; default 5000000; will split input genomic sequence into contigs shorter then max_contig
   --max_intergenic     [number]; default 10000, maximum length of intergenic regions
  --min_contig  [number]; default 50000; will ignore contigs shorter then min_contig in training
   --min_gene_prediction [number]; default 300 (120 fungi) minimum allowed gene length in prediction step
   --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


Developer options:
Optinal algorithm parameters
   --usr_cfg      [filename]; to customize configuration file
  --max_intron            [number]; default 10000 (3000 fungi); maximum length of intron
  --max_intergenic        [number]; default 50000; 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; 'auto' mode detects probability from training; 'off' switches GC donor model OFF
 
Developer options
  --gc3          [number]; GC3 cutoff in training for grasses
  --training    to run only training step of algorithms; 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
   --ini_mod      [filename]; use this file with parameters for algorithm initiation
   --test_set    [filename]; to evaluate prediction accuracy on the given test set
   --test_set    [filename]; to evaluate prediction accuracy on the given test set
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   --debug
   --debug
# -------------------
# -------------------
</pre>
</pre>
[[#top|Back to Top]]
[[#top|Back to Top]]

Revision as of 14:22, 8 December 2020

Category

Bioinformatics

Program On

Sapelo2

Version

4.57

Author / Distributor

GeneMarkES

Description

"Gene Prediction in Eukaryotes. Novel genomes can be analyzed by the program GeneMark-ES utilizing unsupervised training." More details are at GeneMarkES

Running Program

Also refer to Running Jobs on Sapelo2 Also refer to Run X window Jobs and Run interactive Jobs


In order to use geneMarker you will need to download a key and put it into your home directory. Instructions to download the key can be found here: https://github.com/ablab/quast/issues/97

From the above link:

1.)Go to http://exon.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 v.4.33 and LINUX64.

2.)After pressing "I agree ..." button you will be redirected to a download page. You will need to download the key only (either 32bit or 64bit depending on your platform, I think now everyone has 64bit), the software is in Quast package already.

3.)The key is in gzip format, you should unpack it (with gunzip) and move to ~/.gm_key (the name should be exactly like this, with a dot in the beginning).


~/ is your home directory. i.e /home/ugamyid . Once the .gm_key file has been placed there you should be able to run GeneMarker.


Version 4.57

Version 4.57 is at /apps/eb/GeneMark-ET/4.57-GCCcore-8.3.0 It can be loaded with: module load GeneMark-ET/4.57-GCCcore-8.3.0


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

#!/bin/bash
#SBATCH --job-name=geneMarkJob
#SBATCH --partition=batch
#SBATCH --mail-type=ALL
#SBATCH --mail-user=username@uga.edu
#SBATCH --ntasks=1
#SBATCH --mem10gb
#SBATCH --time=08:00:00
#SBATCH --output=RAxML.%j.out
#SBATCH --error=RAxML.%j.err


cd $SLURM_SUBMIT_DIR

module load GeneMark-ET/4.57-GCCcore-8.3.0

gmes_petap.pl [options]


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.


Documentation

module load GeneMark-ET/4.57-GCCcore-8.3.0
gmes_petap.pl 
# -------------------
# -------------------
Usage:  /apps/eb/GeneMark-ET/4.57-GCCcore-8.3.0/gmes_petap.pl  [options]  --sequence [filename]

GeneMark-ES Suite version 4.57_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 algorithm

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

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

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; will ignore contigs shorter then 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

Optinal algorithm parameters
  --max_intron            [number]; default 10000 (3000 fungi); maximum length of intron
  --max_intergenic        [number]; default 50000; 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; 'auto' mode detects probability from training; 'off' switches GC donor model OFF

Developer options
  --gc3          [number]; GC3 cutoff in training for grasses
  --training     to run only training step of algorithms; 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
  --test_set     [filename]; to evaluate prediction accuracy on the given test set
  --key_bin
  --debug
# -------------------

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Installation

source code from GeneMarkES

System

64-bit Linux