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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.
SUBMITTER: Gafni E
PROVIDER: S-EPMC4184253 | biostudies-literature | 2014 Oct
REPOSITORIES: biostudies-literature

Gafni Erik E Luquette Lovelace J LJ Lancaster Alex K AK Hawkins Jared B JB Jung Jae-Yoon JY Souilmi Yassine Y Wall Dennis P DP Tonellato Peter J PJ
Bioinformatics (Oxford, England) 20140630 20
<h4>Summary</h4>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 workf ...[more]