snakePipes: facilitating flexible, scalable and integrative epigenomic analysis.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
01 11 2019
Historique:
received: 28 01 2019
revised: 29 04 2019
accepted: 23 05 2019
pubmed: 28 5 2019
medline: 2 7 2020
entrez: 29 5 2019
Statut: ppublish

Résumé

Due to the rapidly increasing scale and diversity of epigenomic data, modular and scalable analysis workflows are of wide interest. Here we present snakePipes, a workflow package for processing and downstream analysis of data from common epigenomic assays: ChIP-seq, RNA-seq, Bisulfite-seq, ATAC-seq, Hi-C and single-cell RNA-seq. snakePipes enables users to assemble variants of each workflow and to easily install and upgrade the underlying tools, via its simple command-line wrappers and yaml files. snakePipes can be installed via conda: `conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'. Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online. Supplementary data are available at Bioinformatics online.

Identifiants

pubmed: 31134269
pii: 5499080
doi: 10.1093/bioinformatics/btz436
pmc: PMC6853707
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

4757-4759

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press.

Références

Nat Commun. 2018 Jan 15;9(1):189
pubmed: 29335486
Bioinformatics. 2012 Oct 1;28(19):2520-2
pubmed: 22908215
Genome Biol. 2010;11(8):R86
pubmed: 20738864
Cell. 2018 Jul 12;174(2):406-421.e25
pubmed: 29887375
Bioinformatics. 2016 Oct 1;32(19):3047-8
pubmed: 27312411
Bioinformatics. 2011 Aug 1;27(15):2156-8
pubmed: 21653522
F1000Res. 2016 Jun 23;5:1479
pubmed: 27429743
Nat Biotechnol. 2017 Apr 11;35(4):316-319
pubmed: 28398311
Nat Methods. 2018 Jul;15(7):475-476
pubmed: 29967506
Elife. 2018 Dec 21;7:
pubmed: 30575519
Nature. 2016 Jul 28;535(7613):575-9
pubmed: 27437574
Nature. 2015 Jan 15;517(7534):321-6
pubmed: 25592537

Auteurs

Vivek Bhardwaj (V)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.
Faculty of Biology, University of Freiburg, 79104 Freiburg, Germany.

Steffen Heyne (S)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

Katarzyna Sikora (K)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

Leily Rabbani (L)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

Michael Rauer (M)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

Fabian Kilpert (F)

Institutes of Neurogenetics & Cardiogenetics, University of Lübeck, 23562 Lübeck, Germany.

Andreas S Richter (AS)

Genedata AG, 4053 Basel, Switzerland.

Devon P Ryan (DP)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

Thomas Manke (T)

Max Planck Institute of Immunobiology and Epigenetics, 79108 Freiburg, Germany.

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Classifications MeSH