GenPipes: an open-source framework for distributed and scalable genomic analyses

Mathieu Bourgey; Rola Dali; Robert J. M. Eveleigh; Kuang Chung Chen; Louis Létourneau; Joël Fillon; Marc Michaud; Maxime Caron; Johanna Sandoval; François Lefebvre; Gary Leveque; Eloi Mercier; David Bujold; Pascale Marquis; Patrick Tran Van; David Anderson de Lima Morais; Julien Tremblay; Xiaojian Shao; Édouard Henrion; Emmanuel Gonzalez; Pierre-Olivier Quirion; B. Caron; Guillaume Bourque

doi:10.1093/gigascience/giz037

BioinformaticsGigaScienceJun 1, 2019

Peer-reviewed journal article

GenPipes: an open-source framework for distributed and scalable genomic analyses

Mathieu Bourgey iD, Rola Dali iD, Robert J. M. Eveleigh iD, Kuang Chung Chen, Louis Létourneau iD, Joël Fillon, Marc Michaud, Maxime Caron iD, Johanna Sandoval iD, François Lefebvre iD, Gary Leveque iD, Eloi Mercier, David Bujold iD, Pascale Marquis, Patrick Tran Van iD, David Anderson de Lima Morais iD, Julien Tremblay iD, Xiaojian Shao iD, Édouard Henrion, Emmanuel Gonzalez, Pierre-Olivier Quirion iD, B. Caron iD, Guillaume Bourque iD

Abstract

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for High Performance Computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA-Seq, ChIP-Seq, DNA-Seq, Methyl-Seq, Hi-C, capture Hi-C, metagenomics and PacBio long read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has been already configured on several servers and a docker image is also available to facilitate additional installations. In summary, GenPipes offers genomic researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

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