GenPipes: an open-source framework for distributed and scalable genomic analyses
NRC publication: Yes
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| Published in: | Gigascience Vol. 8; no. 6 |
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Oxford University Press
01.06.2019
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| Subjects: | |
| ISSN: | 2047-217X, 2047-217X |
| Online Access: | Get full text |
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| Abstract | NRC publication: Yes Background: 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. Findings: 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 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. Conclusions: GenPipes 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. |
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| AbstractList | 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 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.
GenPipes 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. Background 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. Findings 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 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. Conclusions GenPipes 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. Abstract Background 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. Findings 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 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. Conclusions GenPipes 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. 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.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.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 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.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.GenPipes 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.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. NRC publication: Yes |
| Author | Leveque, Gary Bujold, David Bourque, Guillaume Caron, Bryan Fillon, Joel Caron, Maxime Marquis, Pascale Sandoval, Johanna Mercier, Eloi Tremblay, Julien Bourgey, Mathieu Lefebvre, Francois Henrion, Edouard Van, Patrick Tran Shao, Xiaojian Anderson de Lima Morais, David Eveleigh, Robert Michaud, Marc Letourneau, Louis Chen, Kuang Chung Dali, Rola Gonzalez, Emmanuel Quirion, Pierre-Olivier |
| AuthorAffiliation | 7 Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland 2 McGill University and Genome Québec Innovation Center, Montréal, QC, Canada 6 Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada 4 Calcul Québec, QC, Canada 9 Energy, Mining and Environment, National Research Council Canada, Montréal, QC, Canada 5 Department of Human Genetics, McGill University, Montréal, QC, Canada 1 Canadian Centre for Computational Genomics, Montréal, QC, Canada 3 McGill HPC Centre, McGill University, Montréal, QC, Canada 8 Centre de Calcul Scientifique (CCS), Université de Sherbrooke, Sherbrooke, QC, Canada |
| AuthorAffiliation_xml | – name: 3 McGill HPC Centre, McGill University, Montréal, QC, Canada – name: 6 Beaulieu-Saucier Université de Montréal Pharmacogenomics Centre, Montréal, QC, Canada – name: 2 McGill University and Genome Québec Innovation Center, Montréal, QC, Canada – name: 7 Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland – name: 1 Canadian Centre for Computational Genomics, Montréal, QC, Canada – name: 8 Centre de Calcul Scientifique (CCS), Université de Sherbrooke, Sherbrooke, QC, Canada – name: 5 Department of Human Genetics, McGill University, Montréal, QC, Canada – name: 4 Calcul Québec, QC, Canada – name: 9 Energy, Mining and Environment, National Research Council Canada, Montréal, QC, Canada |
| Author_xml | – sequence: 1 fullname: Bourgey, Mathieu – sequence: 2 fullname: Dali, Rola – sequence: 3 fullname: Eveleigh, Robert – sequence: 4 fullname: Chen, Kuang Chung – sequence: 5 fullname: Letourneau, Louis – sequence: 6 fullname: Fillon, Joel – sequence: 7 fullname: Michaud, Marc – sequence: 8 fullname: Caron, Maxime – sequence: 9 fullname: Sandoval, Johanna – sequence: 10 fullname: Lefebvre, Francois – sequence: 11 fullname: Leveque, Gary – sequence: 12 fullname: Mercier, Eloi – sequence: 13 fullname: Bujold, David – sequence: 14 fullname: Marquis, Pascale – sequence: 15 fullname: Van, Patrick Tran – sequence: 16 fullname: Anderson de Lima Morais, David – sequence: 17 fullname: Tremblay, Julien – sequence: 18 fullname: Shao, Xiaojian – sequence: 19 fullname: Henrion, Edouard – sequence: 20 fullname: Gonzalez, Emmanuel – sequence: 21 fullname: Quirion, Pierre-Olivier – sequence: 22 fullname: Caron, Bryan – sequence: 23 fullname: Bourque, Guillaume |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31185495$$D View this record in MEDLINE/PubMed |
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| Title | GenPipes: an open-source framework for distributed and scalable genomic analyses |
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