Reproducibility with Snakemake and Bioconda

Johannes Köster

 

https://koesterlab.github.io

dataset

results

Data analysis

"Let me do that by hand..."

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"Let me do that by hand..."

Data analysis

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automation

From raw data to final figures:

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

Reproducible data analysis

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scalability

Handle parallelization:

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

automation

Reproducible data analysis

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Handle deployment:

be able to easily execute analyses on a different machine

portability

scalability

automation

Reproducible data analysis

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results

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scalability

automation

portability

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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scalability

automation

portability

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

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Full reproducibility:

install required software and all dependencies in exact versions

portability

scalability

automation

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")

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 3000 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

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.

portability

scalability

automation

Snakemake+Bioconda (short)

By Johannes Köster

Snakemake+Bioconda (short)

Short introduction to reproducibilty with Snakemake and Bioconda

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