Johannes Köster

2019

https://koesterlab.github.io

Reproducible data analysis with

dataset

results

Data analysis

"Let me do that by hand..."

dataset

results

dataset

"Let me do that by hand..."

Data analysis

dataset

results

dataset

automation

From raw data to final figures:

document parameters, tools, versions
execute without manual intervention

Reproducible data analysis

dataset

results

dataset

scalability

Handle parallelization:

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

automation

Reproducible data analysis

dataset

results

dataset

Handle deployment:

be able to easily execute analyses on a different system/platform/infrastructure

portability

scalability

automation

Reproducible data analysis

214k downloads since 2015

Snakemake is popular

611 citations (+359 in 2018 and 2019)

~3 citations per week

dataset

results

dataset

scalability

automation

portability

dataset

results

dataset

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

rule name

how to create output from input

define

input
output
log files
parameters
resources

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"
    script:
        "scripts/myscript.py"

reusable

Python/R scripts

External scripts

import pandas as pd

data = pd.read_table(snakemake.input[0])
data = data.sort_values("id")
data.to_csv(snakemake.output[0], sep="\t")

Python scripts:

External scripts

data <- read.table(snakemake@input[[1]])
data <- data[order(data$id),]
write.table(data, file = snakemake@output[[1]])

R scripts:

Reusable wrappers

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

reuseable wrappers from central repository

Output handling

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

Output handling

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

Output handling

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

dataset

results

dataset

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

# perfom dry-run
snakemake -n

# 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

Between workflow caching

dataset

results

dataset

shared data

Between workflow caching

dataset

results

dataset

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/some-tool.yaml"
    shell:
        "some-tool {input} > {output}"

Integration with Snakemake

channels:
 - conda-forge
dependencies:
  - some-tool =2.3.1
  - some-lib =1.1.2

Over 6000 bioinformatics related packages

Over 600 contributors

Containers

rule mytask:
    input:
        "path/to/{dataset}.txt"
    output:
        "result/{dataset}.txt"
    container:
        "docker://biocontainers/some-tool#2.3.1"
    shell:
        "some-tool {input} > {output}"

Containers + Conda

container:
    "docker://continuumio/miniconda3:4.4.1"


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

define OS

define tools/libs

Self-contained HTML reports

Sustainable publishing

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

Author:

Upload to Zenodo and acquire DOI.
Cite DOI in paper.

Reader:

Download and unpack workflow archive from DOI.

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

More features

Today:

conditional DAG updates based on job output
semi-automatic graph partitioning
resource-constrained scheduling
various ways to constrain or enforce job execution
data provenance and log file handling
CWL export and integration
...

Future:

ML-based inference of resource requirements
more backends (TES, GCP)

Conclusion

With

the human readable specification language
reusable modularization capabilities
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

Acknowledgements

Contributors:

Andreas Wilm

Anthony Underwood

Ryan Dale

David Alexander

Elias Kuthe

Elmar Pruesse

Hyeshik Chang

Jay Hesselberth

Jesper Foldager

John Huddleston

all users and supporters

Joona Lehtomäki

Justin Fear

Karel Brinda

Karl Gutwin

Kemal Eren

Kostis Anagnostopoulos

Kyle A. Beauchamp

Simon Ye

Tobias Marschall

Willem Ligtenberg

Development team:

Christopher Tomkins-Tinch

David Koppstein

Tim Booth

Manuel Holtgrewe

Christian Arnold

Wibowo Arindrarto

Rasmus Ågren

Kyle Meyer

Lance Parsons

Manuel Holtgrewe

Marcel Martin

Matthew Shirley

Mattias Franberg

Matt Shirley

Paul Moore

percyfal

Per Unneberg

Ryan C. Thompson

Ryan Dale

Sean Davis

Resources

Documentation and change log:

https://snakemake.readthedocs.io

Questions:

http://stackoverflow.com/questions/tagged/snakemake

Gold standard workflows:

https://github.com/snakemake-workflows/docs

Configuration profiles:

https://github.com/snakemake-profiles/doc

Command line help:

snakemake --help

Reproducible data analysis with Snakemake

By Johannes Köster

Reproducible data analysis with Snakemake

Data analyses usually entail the application of many command line tools or scripts to transform, filter, aggregate or plot data and results. With ever increasing amounts of data being collected in science, reproducible and scalable automatic workflow management becomes increasingly important. Snakemake is a workflow management system, consisting of a text-based workflow specification language and a scalable execution environment, that allows the parallelized execution of workflows on workstations, compute servers, clusters and the cloud without modification of the workflow definition. Since its publication, Snakemake has been widely adopted and was used to build analysis workflows for a variety of high impact publications. With about thousands of homepage visits per month, it has a large and stable user community. This talk will show how Snakemake can be used to easily document, execute, and reproduce data analyses.

2,637

Data analysis

Data analysis

Reproducible data analysis

Reproducible data analysis

Reproducible data analysis

Snakemake is popular

Define workflows

in terms of rules

Define workflows

in terms of rules

Define workflows

in terms of rules

Define workflows

in terms of rules

Define workflows

in terms of rules

Define workflows

in terms of rules

External scripts

External scripts

Reusable wrappers

Output handling

Output handling

Output handling

Scheduling

Scalable to any platform

Command-line interface

Between workflow caching

Between workflow caching

Software installation is a pain

Package management with

Integration with Snakemake

Containers

Containers + Conda

Self-contained HTML reports

Sustainable publishing

More features

Conclusion

Acknowledgements

Resources

Reproducible data analysis with Snakemake

More from Johannes Köster