COSMOS: Python library for massively parallel workflows
Lancaster, Alex K.
Wall, Dennis P.
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CitationGafni, Erik, Lovelace J. Luquette, Alex K. Lancaster, Jared B. Hawkins, Jae-Yoon Jung, Yassine Souilmi, Dennis P. Wall, and Peter J. Tonellato. 2014. “COSMOS: Python library for massively parallel workflows.” Bioinformatics 30 (20): 2956-2958. doi:10.1093/bioinformatics/btu385. http://dx.doi.org/10.1093/bioinformatics/btu385.
AbstractSummary: 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: firstname.lastname@example.org or email@example.com. Supplementary information: Supplementary data are available at Bioinformatics online.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:13347394