Evidence map›Paper›PMID 31185495›Full record

ArticleGigaScience2019

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

Mathieu Bourgey, Rola Dali, Robert Eveleigh, Kuang Chung Chen, Louis Letourneau, Joel Fillon, Marc Michaud, Maxime Caron, Johanna Sandoval, Francois Lefebvre and 13 more

Abstract read
In one paragraph

Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.

0numbers the graph read from it
0cells of the map it votes in
115citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

115 citing papers in PubMed.

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55 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Mathieu BourgeyCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Rola DaliCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Robert EveleighCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Kuang Chung ChenMcGill HPC Centre, McGill University, Montréal, QC, Canada.
Louis LetourneauCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Joel FillonDepartment of Human Genetics, McGill University, Montréal, QC, Canada.
Marc MichaudMcGill University and Genome Québec Innovation Center, Montréal, QC, Canada.
Maxime CaronCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Johanna SandovalBeaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada.
Francois LefebvreCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Gary LevequeCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Eloi MercierCanadian Centre for Computational Genomics, Montréal, QC, Canada.
David BujoldCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Pascale MarquisCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Patrick Tran VanDepartment of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland.
David Anderson de Lima MoraisCentre de Calcul Scientifique (CCS), Université de Sherbrooke, Sherbrooke, QC, Canada.
Julien TremblayEnergy, Mining and Environment, National Research Council Canada, Montréal, QC, Canada.
Xiaojian ShaoCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Edouard HenrionCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Emmanuel GonzalezCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Pierre-Olivier QuirionCanadian Centre for Computational Genomics, Montréal, QC, Canada.
Bryan CaronMcGill HPC Centre, McGill University, Montréal, QC, Canada.
Guillaume BourqueCanadian Centre for Computational Genomics, Montréal, QC, Canada.

Funding

CIHR MOP-115090
6 · The paper itself

Abstract

backgroundWith 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.

findingsHere 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 sequencing, chromatin immunoprecipitation sequencing, DNA sequencing, methylation sequencing, Hi-C, capture Hi-C, metagenomics, and Pacific Biosciences 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 already been configured on several servers, and a Docker image is also available to facilitate additional installations.

conclusionsGenPipes offers genomics 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.

Indexed as

SoftwareDNA MethylationEpigenomicsGenomicsHumansMetagenomicsSequence Analysis, DNASequence Analysis, RNAbioinformaticsframeworksgenomicspipelineworkflowworkflow management systems

Identifiers

PMID31185495
PMCPMC6559338

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.