Workflow and tool management

Johannes Köster

AEBC 2017

https://koesterlab.github.io

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Data analysis

"Let me do that by hand..."

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"Let me do that by hand..."

Data analysis

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From raw data to final figures:

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

Reproducible data analysis

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Handle parallelization:

• execute for tens to thousands of datasets
• efficiently use any computing platform

Reproducible data analysis

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Handle deployment:

be able to easily execute analyses on a different machine

scalability

Reproducible data analysis

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scalability

Workflow management

Nextflow

Bpipe

Taverna

Bcbio-nexgen

Hadoop

KNIME

Snakemake

CWL

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.

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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:
        "data/{sample}.txt"
    output:
        "result/{sample}.txt"
    conda:
        "software-envs/some-tool.yaml"
    shell:
        "some-tool {input} > {output}"

rule name

refer to input and output from shell command

how to create output from input

(shell, Python, R)

Directed acyclic graph (DAG) of jobs

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Scheduling

Paradigm:

Workflow definition shall be independent of computing platform and available resources

Rules:

define resource usage (threads, memory, ...)

Scheduler:

• solves multidimensional knapsack problem
• schedules independent jobs in parallel
• passes resource requirements to any backend

Scalable to any platform

workstation

compute server

cluster

grid computing

cloud computing

Command-line interface

# execute workflow locally with 16 CPU cores
snakemake --cores 16


# execute on cluster
snakemake --cluster qsub --jobs 100


# execute in the cloud
snakemake --kubernetes --jobs 1000 --default-remote-provider GS --default-remote-prefix mybucket

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Full reproducibility:

install required software and all dependencies in exact versions

scalability

Software installation is a pain

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")

Example:

ISMB 2016 "Wall of Shame"

Of 47 open-access publications, ...

https://github.com/bioconda/bioconda-recipes/pull/1951

Example:

ISMB 2016 "Wall of Shame"

Of 47 open-access publications, ...

https://github.com/bioconda/bioconda-recipes/pull/1951

good (26%)

bad (53%)

ugly (21%)

Package management with

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

Idea:

Normalization installation via recipes

#!/bin/bash

export C_INCLUDE_PATH=${PREFIX}/include
export LIBRARY_PATH=${PREFIX}/lib

make all
mkdir -p $PREFIX/bin
cp seqtk $PREFIX/bin

• source or binary
• recipe and build script

• package

Easy installation and management:

no admin rights needed

conda install pandas

conda update pandas

conda remove pandas

conda env create -f myenv.yaml -n myenv

Isolated environments:

channels:
  - conda-forge
  - defaults
dependencies:
  - pandas ==0.20.3
  - statsmodels ==0.8.0
  - r-dplyr ==0.7.0
  - r-base ==3.4.1
  - python ==3.6.0

Package management with

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

Integration with Snakemake

channels:
  - conda-forge
  - defaults
dependencies:
  - mycommand ==2.3.1

Over 2800 bioinformatics related packages

Over 200 contributors

Bioconda workflow

• recipe
• pull request
• automatic linting
• building
• testing
• human review
• merge
• upload

Builds and tests:

Paradigm:

• transparency
• open source build framework
• public logs

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How to publish this in a sustainable way?

Sustainable publishing

Goal:

• persistently store reproducible data analysis
• ensure it is always accessible

Minimize dependencies:

• third-party resources
• proprietary formats

Solution:

• store everything (including packages) in an archive
• upload to persistent storage like https://zenodo.org
• obtain document object identifier (DOI)

Sustainable publishing

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

Author:

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

Reader:

1. Download and unpack workflow archive from DOI.
2.  

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

Conclusion

• For reproducible data analysis, three dimensions have to be considered.
• A lightweight yet flexible approach to achieve this is to use Snakemake and Bioconda/Conda.

scalability

Acknowledgements

The Snakemake core team

• Christopher Tomkins-Tinch
• David Koppstein
• Tim Booth
• Manuel Holtgrewe
• Christian Arnold
• Wibowo Arindrarto

The Bioconda core team

• Ryan Dale
• Brad Chapman
• Chris Tomkins-Tinch
• Björn Grüning
• Andreas Sjödin
• Jillian Rowe
• Renan Valieris