Gafni, ErikLuquette, JoeLancaster, Alex K.Hawkins, JaredJung, Jae-YoonSouilmi, YassineWall, Dennis P.Tonellato, Peter2014-11-032014Gafni, 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/btu385.1367-4803http://nrs.harvard.edu/urn-3:HUL.InstRepos:13347394Summary: 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.en-USJournal Article2014-11-0310.1093/bioinformatics/btu385