Reproducible and scalable data analysis with Snakemake

Johannes Köster

2017

Data analysis

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Data analysis

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Data analysis

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From raw data to final figures:

• document parameters, tools, versions
• execute without manual intervention

Data analysis

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scalability

Handle parallelization:

execute for tens to thousands of datasets

Avoid redundancy:

• when adding datasets
• when resuming from failures

Data analysis

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scalability

Workflow management:

formalize, document and execute data analyses

Large constantly growing community

Reproducibility with Snakemake

Genome of the Netherlands:

GoNL consortium. Nature Genetics 2014.

Cancer:

Townsend et al. Cancer Cell 2016.

Schramm et al. Nature Genetics 2015.

Martin et al. Nature Genetics 2013.

Ebola:

Park et al. Cell 2015

iPSC:

Burrows et al. PLOS Genetics 2016.

Computational methods:

Ziller et al. Nature Methods 2015.

Schmied et al. Bioinformatics 2015.

Břinda et al. Bioinformatics 2015

Chang et al. Molecular Cell 2014.

Marschall et al. Bioinformatics 2012.

dataset

results

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Define workflows

in terms of rules

Define workflows

in terms of rules

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"

Define workflows

in terms of rules

Define workflows

in terms of rules

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

rule name

refer to input and output from shell command

how to create output from input

Define workflows

in terms of rules

generalize rules with

named wildcards

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

Define workflows

in terms of rules

refer to Python script

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

Define workflows

in terms of rules

refer to R script

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

Define workflows

in terms of rules

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

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

rule mytask:
    input:
        "path/to/dataset2.txt"
    output:
        "result/dataset2.txt"
    script:
        "scripts/myscript.R"

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

rule mytask:
    input:
        "path/to/dataset1.txt"
    output:
        "result/dataset1.txt"
    script:
        "scripts/myscript.R"

Dependencies are determined automatically

Directed acyclic graph (DAG) of jobs

Command line interface

# execute the workflow with target D1.sorted.txt
snakemake D1.sorted.txt

# execute the workflow without target: first rule defines target
snakemake

# dry-run
snakemake -n

# dry-run, print shell commands
snakemake -n -p

# dry-run, print execution reason for each job
snakemake -n -r

# visualize the DAG of jobs using the Graphviz dot command
snakemake --dag | dot -Tsvg > dag.svg

Parallelization

Disjoint paths in the DAG of jobs can be executed in parallel.

# execute the workflow with 8 cores
snakemake --cores 8

Parallelization

schedule according to given resources

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    threads: 4
    resources:
        mem_gb=2
    shell:
        "mycommand {input} > {output}"

Command line interface

# execute the workflow with 8 cores
snakemake --cores 8

# execute the workflow with 8 cores and 100MB memory
snakemake --cores 8 --resources mem_gb=3

Scheduling

s.t.

available jobs

resource usage

free resource (e.g. CPU cores)

Many additional features

• scaling from workstation to cluster without workflow modification
• modularization
• handling of temporary and protected files
• record logging information
• HTML reports
• tracking of tool versions and code changes
• remote file support (S3/Google, Dropbox, HTTPS, FTP, ...)

Reproducible software installation

dataset

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Full reproducibility:

install required software and all dependencies in exact versions

Software installation is heterogeneous

source("https://bioconductor.org/biocLite.R")
biocLite("DESeq2")

easy_install snakemake

./configure --prefix=/usr/local
make
make install

cp lib/amd64/jli/*.so lib
cp lib/amd64/*.so lib
cp * $PREFIX

cpan -i bioperl

cmake ../../my_project \
    -DCMAKE_MODULE_PATH=~/devel/seqan/util/cmake \
    -DSEQAN_INCLUDE_PATH=~/devel/seqan/include
make
make install

apt-get install bwa

yum install python-h5py

install.packages("matrixpls")

Conda package manager

source or binary

package:
  name: seqtk
  version: 1.2

source:
  fn: v1.2.tar.gz
  url: https://github.com/lh3/seqtk/archive/v1.2.tar.gz

requirements:
  build:
    - gcc
    - zlib
  run:
    - zlib

about:
  home: https://github.com/lh3/seqtk
  license: MIT License
  summary: Seqtk is a fast and lightweight tool for processing sequences

test:
  commands:
    - seqtk seq

package

Normalization of installation routines via recipes:

Easy installation and management:

conda install --channel bioconda bwa=0.7.15

conda update bwa

conda remove bwa

Isolated environments:

channels:
  - r
  - bioconda
dependencies:
  - picard ==2.3.0
  - samtools ==1.3.0

Conda package manager

Already over 2000 bioinformatics related conda packages

(C, C++, Python, R, Perl, ...)

Over 130 contributors

CONDA-FORGE

Partner project for general purpose software:

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

Integration with Snakemake

channels:
  - r
  - bioconda
dependencies:
  - mycommand=2.3.1

# automatic deployment of dependencies
snakemake --use-conda

Integrated with 3 popular workflow management systems

Conda Integration with WMS

Sustainable publishing

# archive workflow (including Conda packages)
snakemake --archive myworkflow.tar.gz

Author:

1.  
2. Upload to Zenodo and acquire DOI
3. Cite DOI

Reviewer/reader:

1. Download and unpack workflow archive
2.  

# execute workflow (Conda packages are deployed automatically)
snakemake --use-conda --cores 16

Conclusion

• formalization
• documentation
• parallelization

of data analyses.

Snakemake ensures reproducibility and scalability via

Bioconda is a distribution of Bioinformatics software that

• standardizes
• simplifies
• automates

the installation.

Combined, they enable fully reproducible data analysis.

https://snakemake.bitbucket.org

Köster, Johannes and Rahmann, Sven. "Snakemake - A scalable bioinformatics workflow engine". Bioinformatics 2012.

Köster, Johannes. "Parallelization, Scalability, and Reproducibility in Next-Generation Sequencing Analysis", PhD thesis, TU Dortmund 2014.

Resources

https://bioconda.github.io