Introduction
Snakemake integrates with the package manager conda and the container engine Signlarity
Snakemake is a general purpose workflow management system not only for Bioinformatics but also for any discipline.
mamba is a package manager, which is similar to conda but faster than miniconda. It’s recommended to use mamba to install snakemake ,but it’s not necessary. Alternatively, you can install snakemake just using conda.
A Snakemake workflow is defined by specifying rules in a Snakefile , in which rules decompose the workflow into small steps.
Here, we created an example workflow of genome analysis, which includes several steps from reads mapping to a reference genome to variants calling.
Step 1: Mapping reads
Firstly, we create a new file called Snakefile with an editor of your choice (I used vim here). In the Snakefile, define the following rule:
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rule bwa_map:
input:
"data/genome.fa",
"data/samples/{sample}.fastq"
output:
"mapped_reads/{sample}.bam"
shell:
"bwa mem {input} | samtools view -Sb - > {output}"
A Snakemake rule has a name (here bwa_map) and a number of directives, here input, output and shell. The input and output are just explicit Python string.
Notably, it’s compulsory to specify the maximum number of CPU cores to use at the same time. If you want to use N cores, say --cores N or -cN. For all cores on your system (be sure that this is appropriate) use --cores all. For no parallelization use --cores 1 or -c1.
Execute the snakefile:
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snakemake --cores 1 mapped_reads/{A,B}.bam
Step 2: Sorting read alignments
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rule samtools_sort:
input:
"mapped_reads/{sample}.bam"
output:
"sorted_reads/{sample}.bam"
shell:
"samtools sort -T sorted_reads/{wildcards.sample} "
"-O bam {input} > {output}"
Note that snakemake automatically creates missing directories before jobs are executed.
