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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...

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Detalles Bibliográficos
Autores principales: Gafni, Erik, Luquette, Lovelace J., Lancaster, Alex K., Hawkins, Jared B., Jung, Jae-Yoon, Souilmi, Yassine, Wall, Dennis P., Tonellato, Peter J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
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.
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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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