snakemake_for_qc.md

Adding onto our snakemake workflow

In our previous lesson, we use snakemake to automate the first two rules of our quality control/quality analysis. Given the things you learned during that lesson, we will now automate the rest of the quality control workflow with using snakemake. For this lesson, we would like you to work with others in your room to make the snakefile. We have provided the basic structure for each of the rules, and would like you to fill in the remaining necessary details for the full workflow to run on all samples.

We've added in the rules we created from our previous lesson as well.

Let's make sure we're in a good working directory and we have the necessary software installed. If you've already ran the installation command on your current instance, you don't need to run it again.

cd ~
conda install -y fastqc multiqc trimmomatic

SAMPLES=['ERR458493', 'ERR458494', 'ERR458495', 'ERR458500', 'ERR458501', 
'ERR458502']

rule all:
    input:
        "" # add the path to the final file 

rule fastqc_raw:
    input: "data/{sample}.fastq.gz"
    output: 
        "fastqc_raw/{sample}_fastqc.html",
        "fastqc_raw/{sample}_fastqc.zip"
    shell:'''
    fastqc -o fastqc_raw {input}
    '''

rule multiqc_raw:
    input: expand("fastqc_raw/{sample}_fastqc.html", sample = SAMPLES)
    output: "fastqc_raw/multiqc_report.html"
    shell:'''
    multiqc -o fastqc_raw fastqc_raw
    '''

# Add the trimmomatic commands from our trimming lesson. 
rule trim:
    input:
        reads = "",
        adapters = ""
    output: "" # the output should be the trimmed file
    shell: '''
    trimmomatic SE 
    '''

# Use the commands above as a template to fill in these rules, this time
running the analyses on the trimmed reads.
rule fastqc_trimmed:

rule multiqc_trimmed:

When complete, this snakemake workflow automates all of the bash steps of our quality control workflow!

You can generate a dag of your workflow using the following command:

snakemake --dag | dot -Tpng > dag.png

Remember, "dag" stands for Directed Acyclic Graph. It shows the steps of your workflow that are executed by Snakemake. This dag can be helpful to communicate your workflow to others, or to visualize the decisions snakemake is making as you are constructing your workflow.

You can open it to view using the "Files" tab in the RStudio pane and opening the dag.png file you created.