SPHN Workflow Interoperability Workshop

 

Johannes Köster

2018

 

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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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 system/platform/infrastructure

portability

scalability

automation

Reproducible data analysis

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scalability

automation

portability

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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

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"
    shell:
        "some-tool {input} > {output}"

rule name

how to create output from input

define

  • input
  • output
  • log files
  • parameters
  • resources

Define workflows

in terms of rules

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

reusable

Python/R scripts

Define workflows

in terms of rules

rule map_reads:
    input:
        "{sample}.bam"
    output:
        "{sample}.sorted.bam"
    wrapper:
        "0.22.0/bio/samtools/sort"

reuseable wrappers from central repository

Define workflows

in terms of rules

use CWL tool

definitions

rule map_reads:
    input:
        "{sample}.bam"
    output:
        "{sample}.sorted.bam"
    cwl:
        "https://github.com/common-workflow-language/"
        "workflows/blob/fb406c95/tools/samtools-sort.cwl"

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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

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

Configuration profiles

snakemake --profile slurm --jobs 1000
$HOME/.config/snakemake/slurm
├── config.yaml
├── slurm-jobscript.sh
├── slurm-status.py
└── slurm-submit.py

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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
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

Singularity

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    singularity:
        "docker://biocontainers/mycommand#2.3.1"
    shell:
        "mycommand {input} > {output}"

Singularity + Conda

singularity:
    "docker://continuumio/miniconda3:4.4.1"


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

define OS

define tools/libs

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

With

  • the rule-based DSL
  • modularization via wrappers and CWL integration
  • seamless execution on all platforms without adaptation of the workflow definition
  • integrated package management and containerization

Snakemake covers all three dimensions of fully reproducible data analysis.

portability

scalability

automation

Snakemake

By Johannes Köster

Snakemake

SPHN Workflow Interoperability Workshop

  • 1,798

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