%0 Journal Article %1 Gafni:2014:Bioinformatics:24982428 %A Gafni, E %A Luquette, L J %A Lancaster, A K %A Hawkins, J B %A Jung, J Y %A Souilmi, Y %A Wall, D P %A Tonellato, P J %D 2014 %J Bioinformatics %K cloud-computing fulltext python sequencing software workflow %N 20 %P 2956-2958 %R 10.1093/bioinformatics/btu385 %T COSMOS: Python library for massively parallel workflows %U https://www.ncbi.nlm.nih.gov/pubmed/24982428?dopt=Abstract %V 30 %X 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.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.dpwall@stanford.edu or peter\_tonellato@hms.harvard.edu.Supplementary data are available at Bioinformatics online.

@article{Gafni:2014:Bioinformatics:24982428, abstract = {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.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.dpwall@stanford.edu or peter\_tonellato@hms.harvard.edu.Supplementary data are available at Bioinformatics online.}, added-at = {2017-07-22T21:52:18.000+0200}, author = {Gafni, E and Luquette, L J and Lancaster, A K and Hawkins, J B and Jung, J Y and Souilmi, Y and Wall, D P and Tonellato, P J}, biburl = {https://www.bibsonomy.org/bibtex/29c30bc99e0926e26afc9750900938fd8/marcsaric}, description = {COSMOS: Python library for massively parallel workflows. - PubMed - NCBI}, doi = {10.1093/bioinformatics/btu385}, interhash = {d95c231f29ea1409c3c2040a770bb1e6}, intrahash = {9c30bc99e0926e26afc9750900938fd8}, journal = {Bioinformatics}, keywords = {cloud-computing fulltext python sequencing software workflow}, month = oct, number = 20, pages = {2956-2958}, pmid = {24982428}, timestamp = {2017-07-22T21:52:18.000+0200}, title = {COSMOS: Python library for massively parallel workflows}, url = {https://www.ncbi.nlm.nih.gov/pubmed/24982428?dopt=Abstract}, volume = 30, year = 2014 }