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Bioinformatics. 2014 Oct 15;30(20):2956-8. doi: 10.1093/bioinformatics/btu385. Epub 2014 Jun 30.

COSMOS: Python library for massively parallel workflows.

Author information

1
Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.
2
Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, Department of Pathology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA and Department of Biology, Mohammed V University-Agal, 4 Ibn Battouta Avenue, Rabat B.P:1014RP, Morocco.

Abstract

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.

PMID:
24982428
PMCID:
PMC4184253
DOI:
10.1093/bioinformatics/btu385
[Indexed for MEDLINE]
Free PMC Article
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