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Cite_COSMOS.bib
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Cite_COSMOS.bib
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@article{Gafni2014,
abstract = {SUMMARY: Efficient workflows to shepherd clinically-generated genomic data through the multiple stages of a next-generation sequencing (NGS) pipeline is 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 non-commercial purposes, in addition to documentation. Links to both are provided at http://lpm.hms.harvard.edu/ and http://wall-lab.stanford.edu.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
author = {Gafni, Erik and Luquette, Lovelace J and Lancaster, Alex K and Hawkins, Jared B and Jung, Jae-Yoon and Souilmi, Yassine and Wall, Dennis P and Tonellato, Peter J},
doi = {10.1093/bioinformatics/btu385},
issn = {1367-4811},
journal = {Bioinformatics (Oxford, England)},
month = jun,
pmid = {24982428},
title = {{COSMOS: Python library for massively parallel workflows.}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24982428},
year = {2014}
}