ohsu-cedar-comp-hub / Bulk-RNA-seq-pipeline-SE

This is a pipeline to run basic RNA-seq analysis for single-end data.

Bulk-RNA-seq-pipeline-SE

Pipeline to run basic RNA-seq analysis on single-end data.

This is a package of Python and R scripts that enable reading, processing and analysis of Omics' datasets. This package implements the Snakemake management workflow system and is currently implemented to work with the cluster management and job scheduling system SLURM. This snakemake workflow utilizes conda installations to download and use packages for further analysis, so please ensure that you have installed miniconda prior to use.

Questions/issues

Please add an issue to the Omics-QC-pipeline repository. We would appreciate if your issue included sample code/files (as appropriate) so that we can reproduce your bug/issue.

Contributing

We welcome contributors! For your pull requests, please include the following:

• Sample code/file that reproducibly causes the bug/issue
• Documented code providing fix
• Unit tests evaluating added/modified methods.

Use

Locate raw files:

• After sequencing, your raw fastq files are placed in /path/to/sequencing/files.

$ cd /path/to/raw/data
$ ls -alh

Check md5sum.

$ md5sum –c md5sum.txt > md5sum_out.txt

Move your files into the archive to be stored.

$ mv /path/to/raw/data /path/to/archive

Check md5sum again to ensure your sequencing files are not corrupted.

$ md5sum –c md5sum.txt > md5sum_out.txt

Clone this Pipeline into your working directory.

$ git clone https://github.com/ohsu-cedar-comp-hub/Bulk-RNA-seq-pipeline-SE.git

Create a samples/raw directory, and a logs directory in your wdir().

$ mkdir logs
$ mkdir samples
$ cd samples
$ mkdir raw

Symbollically link the fastq files of your samples to the wdir/samples/raw directory using a bash script loop in your terminal.

ls -1 /path/to/data/LIB*gz |     while read gz; do
  R=$( basename $gz | cut -d '_' -f 3 | awk '{print $1".fastq.gz"}' )
  echo $R
  ln -s ${gz} ./${R}
done

Upload your metadata file to the data directory, with the correct formatting:

• Columns should read: StudyID Column2 Column3 ...
• Each row should be a sample, with subsequent desired information provided (RNA extraction date, etc.)
• Edit omic_config.yaml to include only columns included in this metadata file:
  • This includes meta_columns_to_plot and pca labels
• All values in this file should be tab-separated

Edit the omic_config.yaml in your wdir():

• Change the project_id to a unique project identifier
• Add appropriate contrasts based on your samples under the [diffexp][contrasts] section
• Add the path to your metadata file for the omic_meta_data and samples parameters
• Change base_dir to your current working directory
• Ensure you have the correct assembly specified
  • Current options for this are: hg19, hg38.89 (ensembl v89) and hg38.90 (ensembl v90)

Do a dry-run of snakemake to ensure proper execution before submitting it to the cluster (in your wdir).

$ snakemake -np --verbose

Once your files are symbolically linked, you can submit the job to exacloud via your terminal window.

$ sbatch submit_snakemake.sh

To see how the job is running, look at your queue.

$ squeue -u your_username

Directed Acyclic Graph (DAG) of an example workflow including two samples