Difference between revisions of "RNASeq: Obtaining read counts"

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(Created page with "Read counting implies counting the number of reads that map inside a specific annotation feature. The tutorials listed [[RNASeq_and_differential_gene_expression_analysis|here]...")
 
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Read counting implies counting the number of reads that map inside a specific annotation feature. The tutorials listed [[RNASeq_and_differential_gene_expression_analysis|here]] demonstrate read counting as part of differential gene expression using the R library DESeq/DESeq2. Alternatively, reads may be counted with the python program HTSeq-count, see the manual for instructions ([http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html]).
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Read counting implies counting the number of reads that map inside a specific annotation feature. The tutorials listed [[RNASeq and differential gene expression analysis|here]] demonstrate read counting as part of differential gene expression using the R library DESeq/DESeq2. Alternatively, reads may be counted with the python program HTSeq-count, see the manual for instructions ([http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html]).
  
 
Read counting may be CPU-intensive, depending on the size of the BAM file(s) used. It is thus recommended to run this process as a job script on Abel. Such a job script must first load the R module on Abel, subsequently executing an R script containing the read-counting R code. Such a job script may look like:
 
Read counting may be CPU-intensive, depending on the size of the BAM file(s) used. It is thus recommended to run this process as a job script on Abel. Such a job script must first load the R module on Abel, subsequently executing an R script containing the read-counting R code. Such a job script may look like:
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<div style="line-height:90%; background-color: LightGray; border-style: solid; border-width:1px; font-family:courier new,courier,monospace;">
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#!/bin/bash
  
!/bin/bash
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#SBATCH --job-name=my_R_script_name
  
SBATCH --job-name=my_R_script_name
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#SBATCH --account=myAccountName
  
SBATCH --account=myAccountName
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#SBATCH --time=48:00:00
  
SBATCH --time=48:00:00
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#SBATCH --mem-per-cpu=3500M
  
SBATCH --mem-per-cpu=3500M
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#SBATCH --nodes=1
 +
 
 +
#SBATCH --ntasks-per-node=1
  
SBATCH --nodes=1
 
  
SBATCH --ntasks-per-node=1
 
  
 
source /cluster/bin/jobsetup
 
source /cluster/bin/jobsetup
  
module purge module load R R CMD BATCH /path/to/Rscript.R
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 +
 
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module load R  
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 +
R CMD BATCH /path/to/Rscript.R
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</div>

Revision as of 12:39, 28 May 2015

Read counting implies counting the number of reads that map inside a specific annotation feature. The tutorials listed here demonstrate read counting as part of differential gene expression using the R library DESeq/DESeq2. Alternatively, reads may be counted with the python program HTSeq-count, see the manual for instructions (http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html).

Read counting may be CPU-intensive, depending on the size of the BAM file(s) used. It is thus recommended to run this process as a job script on Abel. Such a job script must first load the R module on Abel, subsequently executing an R script containing the read-counting R code. Such a job script may look like:

  1. !/bin/bash
  1. SBATCH --job-name=my_R_script_name
  1. SBATCH --account=myAccountName
  1. SBATCH --time=48:00:00
  1. SBATCH --mem-per-cpu=3500M
  1. SBATCH --nodes=1
  1. SBATCH --ntasks-per-node=1


source /cluster/bin/jobsetup


module load R

R CMD BATCH /path/to/Rscript.R