• image/svg+xml
  • Snakemake
image/svg+xml A framework for reproducible data analysis

Readability and automation

With Snakemake, data analysis workflows are defined via an easy to read, adaptable, yet powerful specification language on top of Python. Steps are defined by "rules", which denote how to generate a set of output files from a set of input files (e.g. using a shell command). Wildcards (in curly braces) provide generalization. Dependencies between rules are determined automatically.

rule select_by_country: input: "data/worldcitiespop.csv" output: "by-country/{country}.csv" shell: "xsv search -s Country '{wildcards.country}' " "{input} > {output}"

Portability

By integration with the Conda package manager and containers, all software dependencies of each workflow step are automatically deployed upon execution.

rule select_by_country: input: "data/worldcitiespop.csv" output: "by-country/{country}.csv" conda: "envs/xsv.yaml" shell: "xsv search -s Country '{wildcards.country}' " "{input} > {output}"

Scripting integration

Rapidly implement analysis steps via direct script and jupyter notebook integration supporting Python, R, Julia, Rust, Bash, without requiring any boilerplate code.

rule select_by_country: input: "data/worldcitiespop.csv" output: "by-country/{country}.csv" script: "scripts/select_by_country.R"

Modularization

Easily create and employ re-usable tool or library wrappers, split your data analysis into well-separated modules, and compose multi-modal analyses by easily combining entire workflows various sources.

rule convert_to_pdf: input: "{prefix}.svg" output: "{prefix}.pdf" wrapper: "0.47.0/utils/cairosvg"

"Turing completeness"

Being a syntactical extension of Python, you can implement arbitrary logic beyond the plain definition of rules. Rules can be generated conditionally, arbitrary Python logic can be used to perform aggregations, configuration and metadata can be obtained and postprocessed in any required way.

def get_data(wildcards): # use arbitrary Python logic to # aggregate over the required input files return ... rule plot_histogram: input: get_data output: "plots/hist.svg" script: "scripts/plot-hist.py"

Human Readability

The logic of production workflows can become complex by involving lots of lookups and dynamic decisions. Snakemake offers semantic helper functions for lookups, branching and aggregation that avoid the need for plain Python code as shown above, and allow to express complex logic in a human-readable and self-contained way.

rule plot_histogram: input: branch( lookup(dpath="histogram/somedata", within=config), then="data/somedata.txt", otherwise="data/someotherdata.txt" ) output: "plots/hist.svg" script: "scripts/plot-hist.py"

Dynamic workflows

Snakemake allows to define workflows that are dynamically updated at runtime. By defining so-called checkpoints, the workflow can be dynamically adapted at runtime. Further, input can be provided as Python queues, thereby enabling a workflow to continuously receive new input data (e.g. while a certain measurement is conducted).

rule all: input: from_queue(all_results, finish_sentinel=...) checkpoint somestep: input: "samples/{sample}.txt" output: "somestep/{sample}.txt" shell: "somecommand {input} > {output}"

Transparency and data provenance

Automatic, interactive, self-contained reports ensure full transparency from results down to used steps, parameters, code, and software. The reports can moreover contain embedded results (from images, to PDFs and even interactive HTML) enabling a comprehensive reporting that combines analysis results with data provenance information.

Scalability

Workflows scale seamlessly from single to multicore, clusters or the cloud, without modification of the workflow definition and automatic avoidance of redundant computations.

Configurability

Snakemake is extremely flexible and configurable. Numerous options allow adapt the behavior to the needs of the data analysis at hand and the underlying infrastructure. Options can be provided via the command line interface or persisted via system-wide, user-specific, and workflow specific profiles.

executor: slurm software-deployment-method: - conda latency-wait: 60 default-storage-provider: fs shared-fs-usage: - persistence - software-deployment - sources - source-cache local-storage-prefix: /local/work/$USER/snakemake-scratch

Extensibility

Snakemake has a powerful plugin system that allows to extend various functionalities with alternative implementations. Via stable and well-defined interfaces, plugins can evolve independently of Snakemake, and mutual update requirements are minimized. Currently, execution backends and remote storage support is implemented via plugins. In the future, we will extend this to other areas, such as workflow scheduling, reporting, software deployment, and more.

Authors and Contributors ⓘ

Groups, Institutes, Companies, and Organizations ⓘ

  • University of Duisburg-Essen
  • Science for Life Laboratory
  • Broad Institute of MIT and Harvard
  • @bihealth
  • Edinburgh Genomics
  • CERN
  • CUBI Core Unit Bioinformatics, Berlin Institute of Health
  • The GLOBE Institute – University of Copenhagen
  • University of Melbourne | Doherty Institute
  • Nationalt Genome Center
  • Icahn School of Medicine at Mount Sinai
  • @AnyBody
  • Karolinska Institutet
  • 上海交通大学
  • National Institutes of Health (NIH), National Institute of Child Health and Human Development (NICHD)
  • Netherlands Institute of Ecology (NIOO-KNAW)
  • Pairwise
  • Solynta
  • Max Planck Institute for Biology Tübingen
  • LHCb
  • EMBL
  • University of Colorado School of Medicine
  • Data Science @google
  • Seoul National University
  • Princeton University
  • Oyat Consulting
  • @neherlab @nextstrain
  • University of Mainz
  • UC Santa Cruz
  • Spotify
  • @fulcrumgenomics
  • Clinical Genomics Uppsala / Uppsala University
  • GNS Science
  • Helmholtz Centre for Environmental Research
  • AWS
  • BIH
  • @cid-harvard
  • @novartis
  • NTNU
  • Regeneron Pharmaceuticals
  • Sentieon
  • University of California, Davis
  • UZH Zurich
  • DKRZ
  • Washington University School of Medicine
  • @populationgenomics
  • Exact Sciences
  • @cnio-bu
  • University of Vienna
  • Technical University of Munich
  • ImmunoScape
  • Gymrek Lab, UCSD
  • @ENCCS
  • Barcelona Supercomputing Center
  • @insilicoconsulting
  • University of Duisburg Essen
  • ETH
  • UiT The Arctic University of Norway
  • Princess Margaret Cancer Centre, University Health Network
  • @TileDB-Inc
  • DKTK/DKFZ
  • Data Science Centre, EMBL
  • @txbiomed
  • University of Helsinki
  • @BiomeSense
  • Max Delbrück center for molecular medicine
  • Alva Genomics
  • University College London
  • Sorbonne Université, Paris
  • IMS Nanofabrication GmbH
  • @common-workflow-language
  • @RWTH-EBC
  • Bioinformatics Software Engineer at Novartis Institutes for BioMedical Research (NIBR)
  • Genedata AG
  • @GenomicsUA
  • Brabant Water
  • Alexandria University
  • Center for eResearch, University of Auckland
  • Université de Montréal
  • @Syngenta
  • University of Chicago
  • Anthropic
  • WEHI
  • @BlueBrain
  • @nanoporetech
  • @idiap
  • University of Colorado Anschutz School of Medicine
  • @tonttu-ai
  • Kahnemnan-Treisman Center
  • TU Delft
  • Institute for Health Metrics and Evaluation
  • Ascend Analytics
  • University of Washington
  • Verily
  • Medical College of Wisconsin
  • University of Pittsburgh / Center for Craniofacial and Dental Genetics
  • Freie Universität Berlin
  • VantAI
  • @USF-HII
  • NIAID
  • www.hzdr.de
  • HHU Düsseldorf
  • Seqoia
  • A. C. Camargo Cancer Center
  • @mpinb
  • Duke-NUS
  • @BlueRiverTechnology
  • Institut Gustave Roussy
  • German Aerospace Center (DLR)
  • IOB
  • Arcadia Science
  • ETH Zürich
  • Novartis
  • LPC Caen - IN2P3 - CNRS
  • Sunagawa Lab @ ETH Zürich
  • University of Tartu
  • Dartmouth College, @dandi, @Debian, @DataLad, @neurodebian, @PyMVPA, @fail2ban
  • University of Michigan, Ann Arbor
  • Red Hat
  • @tempuslabs
  • WCIP | University of Glasgow
  • Helmholtz-Zentrum Dresden-Rossendorf e.V.
  • @sib-swiss
  • @Functional-Genomics-Lab
  • National Institutes of Health
  • IBIS (Institut de Biologie Intégrative et des Systèmes)
  • TU Berlin
  • Roche
  • Friedrich Schiller University Jena
  • University of Jyväskylä
  • Swiss Ornithological Institute
  • 4Catalyzer
  • IMB, UQ
  • ETHZ
  • Wellcome Sanger Institute
  • SPD
  • @Adobe
  • Sarepta Therapeutics
  • @manaakiwhenua
  • Mount Sinai Hospital (@marcoralab)
  • CNRS
  • Wesleyan University
  • Norges Miljø- & Biovitenskapelige Universitet (NMBU)
  • Freelancer for hire. Maybe.
  • @CVUA-RRW
  • Morgridge Instute for Research
  • 𝐈𝐍𝐑𝐈𝐀🇫🇷 Nat. Inst. for DigitSci & Tech
  • Predictive NeuroImaging Lab, Institute of Artificial Intelligence in Medicine, Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen
  • Oslo University Hospital
  • @JRC-STU
  • Treelogic
  • Pasqal
  • Stockholm Universitetet
  • Hochschule Darmstadt
  • City, University of London
  • Harvard, USA
  • @Quantco
  • @fulcrumgenomics
  • DTU biosustain
  • VBCF
  • Stony Brook Medicine
  • La Jolla Institute for Allergy and Immunology @LJI-Bioinformatics @IEDB
  • Fred Hutchinson Cancer Research Center; Howard Hughes Medical Institute
  • University of Pennsylvania
  • CSIRO
  • Erlangen Centre for Astroparticle Physics
  • @blab @nextstrain
  • Soon To Be Revealed
  • Vertex Pharmaceuticals