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COSMOS: Python library for massively parallel workflows
Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal descript...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184253/ https://www.ncbi.nlm.nih.gov/pubmed/24982428 http://dx.doi.org/10.1093/bioinformatics/btu385 |
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author | Gafni, Erik Luquette, Lovelace J. Lancaster, Alex K. Hawkins, Jared B. Jung, Jae-Yoon Souilmi, Yassine Wall, Dennis P. Tonellato, Peter J. |
author_facet | Gafni, Erik Luquette, Lovelace J. Lancaster, Alex K. Hawkins, Jared B. Jung, Jae-Yoon Souilmi, Yassine Wall, Dennis P. Tonellato, Peter J. |
author_sort | Gafni, Erik |
collection | PubMed |
description | Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://wall-lab.stanford.edu. Contact: dpwall@stanford.edu or peter_tonellato@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-4184253 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-41842532014-10-07 COSMOS: Python library for massively parallel workflows Gafni, Erik Luquette, Lovelace J. Lancaster, Alex K. Hawkins, Jared B. Jung, Jae-Yoon Souilmi, Yassine Wall, Dennis P. Tonellato, Peter J. Bioinformatics Applications Notes Summary: Efficient workflows to shepherd clinically generated genomic data through the multiple stages of a next-generation sequencing pipeline are of critical importance in translational biomedical science. Here we present COSMOS, a Python library for workflow management that allows formal description of pipelines and partitioning of jobs. In addition, it includes a user interface for tracking the progress of jobs, abstraction of the queuing system and fine-grained control over the workflow. Workflows can be created on traditional computing clusters as well as cloud-based services. Availability and implementation: Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at http://lpm.hms.harvard.edu and http://wall-lab.stanford.edu. Contact: dpwall@stanford.edu or peter_tonellato@hms.harvard.edu. Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2014-10-15 2014-06-30 /pmc/articles/PMC4184253/ /pubmed/24982428 http://dx.doi.org/10.1093/bioinformatics/btu385 Text en © The Author 2014. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Gafni, Erik Luquette, Lovelace J. Lancaster, Alex K. Hawkins, Jared B. Jung, Jae-Yoon Souilmi, Yassine Wall, Dennis P. Tonellato, Peter J. COSMOS: Python library for massively parallel workflows |
title | COSMOS: Python library for massively parallel workflows |
title_full | COSMOS: Python library for massively parallel workflows |
title_fullStr | COSMOS: Python library for massively parallel workflows |
title_full_unstemmed | COSMOS: Python library for massively parallel workflows |
title_short | COSMOS: Python library for massively parallel workflows |
title_sort | cosmos: python library for massively parallel workflows |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184253/ https://www.ncbi.nlm.nih.gov/pubmed/24982428 http://dx.doi.org/10.1093/bioinformatics/btu385 |
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