ChIPseq Workflow

This workflow performs ChIPseq analysis using BWA-MEM, MACS2, and deepTools

Authors

• Dean Pettinga (@deanpettinga), https://github.com/deanpettinga

Usage

NOTE this workflow is optimized for HPC3 @ Van Andel Institute.

Step 1: Installation

make sure you are running

The following recipe provides established best practices for running and extending this workflow in a reproducible way.

1. Fork the repo to a project directory on /secondary
2. Clone the fork to the desired working directory for your project.
3. Create a new branch (the project-branch) within the clone and switch to it. The branch will contain any project-specific modifications (e.g. to configuration, but also to code).

Step 2: Configure the workflow

• Modify the config, and any necessary sheets, e.g.:
  • src/samples.txt
  • src/config.yaml
  • src/cluster.yaml
• Move your sequencing reads to raw_reads/

Step 3: Test the workflow

Test your configuration by performing a dry-run via

snakemake --use-conda -np

Execute as from within your project directory as a PBS job using BBC nodes via

qsub -q bbc /src/run_snake.sh

This job script will produce DAG (.txt & .png) and .html with run stats for the workflow to be executed in runs/chipseq_workflow_(TIME)

Step 4: Investigate Results

Review your results including the run stats:

• runs/chipseq_workflow_(TIME).html

Step 5:

Now that you've successfully run your analysis, it's time to do some housekeeping.

• Commit any changes you've made to the repo and push the project-branch to your fork on github.
  • Optional: Merge back any valuable and generalizable changes to the [upstream repo] via a pull request. This would be greatly appreciated.
  • Optional: Push results (plots/tables) to the remote branch on your fork.
  • Optional: Create a self-contained workflow archive for publication along with the paper (snakemake --archive).
  • Optional: Delete the local clone/workdir to free space.