Difference between revisions of "RNASeq: Obtaining read counts"
(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 [[ | + | 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: | ||
| + | <div style="line-height:90%; background-color: LightGray; border-style: solid; border-width:1px; font-family:courier new,courier,monospace;"> | ||
| + | #!/bin/bash | ||
| − | + | #SBATCH --job-name=my_R_script_name | |
| − | SBATCH -- | + | #SBATCH --account=myAccountName |
| − | SBATCH -- | + | #SBATCH --time=48:00:00 |
| − | SBATCH -- | + | #SBATCH --mem-per-cpu=3500M |
| − | SBATCH -- | + | #SBATCH --nodes=1 |
| + | |||
| + | #SBATCH --ntasks-per-node=1 | ||
| − | |||
| − | |||
source /cluster/bin/jobsetup | source /cluster/bin/jobsetup | ||
| − | + | ||
| + | |||
| + | module load R | ||
| + | |||
| + | R CMD BATCH /path/to/Rscript.R | ||
| + | </div> | ||
Revision as of 13:39, 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:
- !/bin/bash
- SBATCH --job-name=my_R_script_name
- SBATCH --account=myAccountName
- SBATCH --time=48:00:00
- SBATCH --mem-per-cpu=3500M
- SBATCH --nodes=1
- SBATCH --ntasks-per-node=1
source /cluster/bin/jobsetup
module load R
R CMD BATCH /path/to/Rscript.R
