Snakemake working group

2024-10-15

Nadia GOUE, Sébastien RAVEL, Chloé QUIGNOT

Source: adapted from FAIRbioinfo 2021 training material of the IFB and Snakemake introduction tutorial from BIOI2

Material under CC-BY-SA licence

How this working group is organised

• Divided into 2 sessions of 1h30 each
• A single continuous but progressive exercise over these 2 sessions
• Presentation of a use-case by Pauline @2pm (~30min)

What the exercise is about

• You’ll be building a Snakemake pipeline from scratch
• The exercise will be divided into several parts and you’ll be guided along the way
  • Part A: create a first simple pipeline with FastQC & MultiQC tools
    • Inputs: NGS data (*.fastq.gz)
    • Processing: individual QC analyses with FastQC and aggregation of results with MultiQC
    • Output: Quality Control report
  • Part B: improve the previous pipeline
  • Part C: adapt the previous pipeline to the HPC environment

Don’t worry about understanding what QC analysis does

Snakemake’s FAQ: https://snakemake.readthedocs.io/en/latest/project_info/faq.html

It’s time to get your hands dirty!

Go to the Moodle web page and open up instructions for exercise A.

https://moodle.france-bioinformatique.fr/course/view.php?id=29

A few reminders

• Snakemake workflow = set of rules
• Snakefile = where snakemake code is written
• Rules are defined by their name and contain directives (of which input and output to specify input & output files):

rule myRuleName
    input: "myInputFile"
    output: "myOutputFile"
    shell: "echo {input} > {output}"

• Snakemake only executes the target rule and only rules that will help in generating its files
• Rules can be generalised using wildcards written within braces (e.g. {wildcardname})
• A Snakefile is run with the snakemake --cores 1 command (+ other options available)
• Debugging options: --dag, --rulegraph, --filegraph and --dry-run + -p to print shell commands