Reproducible data analysis with Snakemake

 

 

Johannes Köster

2019

 

https://koesterlab.github.io

150k downloads since 2015

497 citations (+150 in 2019)

Snakemake is popular

https://snakemake.readthedocs.io

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    shell:
        "some-tool {input} > {output}"

Concise DSL

https://snakemake.readthedocs.io

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    script:
        "scripts/mytask.py"

Python scripts

https://snakemake.readthedocs.io

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    script:
        "scripts/mytask.R"

R scripts

https://snakemake.readthedocs.io

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    script:
        "scripts/mytask.jl"

Julia scripts

https://snakemake.readthedocs.io

import matplotlib.pyplot as plt
import pandas as pd

d = pd.read_table(snakemake.input[0])

d.hist(bins=snakemake.config["hist-bins"])

plt.savefig(snakemake.output[0])

No boilerplate

rule mytask:
    input:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    script:
        "scripts/mytask.py"

https://snakemake.readthedocs.io

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    script:
        "scripts/myscript.R"


rule myfiltration:
     input:
        "result/{dataset}.txt"
     output:
        "result/{dataset}.filtered.txt"
     shell:
        "mycommand {input} > {output}"


rule aggregate:
    input:
        "results/dataset1.filtered.txt",
        "results/dataset2.filtered.txt"
    output:
        "plots/myplot.pdf"
    script:
        "scripts/myplot.R"

Implicit dependencies

https://snakemake.readthedocs.io

workstation

compute server

cluster

grid computing

cloud computing

Scalability

https://snakemake.readthedocs.io

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    conda:
        "envs/mycommand.yaml"
    shell:
        "mycommand {input} > {output}"
channels:
  - bioconda
  - conda-forge
dependencies:
  -mycommand =2.3.1

Conda integration

https://snakemake.readthedocs.io

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    singularity:
        "docker://some/container"
    shell:
        "mycommand {input} > {output}"

Singularity integration

https://snakemake.readthedocs.io

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    conda:
        "envs/mycommand.yaml"
    singularity:
        "docker://some/os"
    shell:
        "mycommand {input} > {output}"

Singularity + Conda

https://snakemake.readthedocs.io

Snakemake

https://snakemake.readthedocs.io

Snakemake

https://snakemake.readthedocs.io

dataset

results

dataset

dataset

dataset

dataset

dataset

portability

scalability

automation/ documentation

https://snakemake.readthedocs.io

Snakemake DoDSC

By Johannes Köster

Snakemake DoDSC

DoDSC 2019

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