MaghrebDataLibMedV2, Main, Exploration, bibRecord, 000245

COSMOS: Python library for massively parallel workflows

Identifieur interne : 000245 ( Main/Exploration ); précédent : 000244; suivant : 000246

COSMOS: Python library for massively parallel workflows

Auteurs : Erik Gafni [États-Unis] ; Lovelace J. Luquette [États-Unis] ; Alex K. Lancaster [États-Unis] ; Jared B. Hawkins [États-Unis] ; Jae-Yoon Jung [États-Unis] ; Yassine Souilmi [États-Unis, Maroc] ; Dennis P. Wall [États-Unis] ; Peter J. Tonellato [États-Unis]

Source :

Bioinformatics [ 1367-4803 ] ; 2014.

RBID : PMC:4184253

Descripteurs français

KwdFr :
- Génomique (méthodes), Langages de programmation (MeSH), Séquençage nucléotidique à haut débit (méthodes).
MESH :
- méthodes : Génomique, Séquençage nucléotidique à haut débit.
- Langages de programmation.

English descriptors

KwdEn :
- Genomics (methods), High-Throughput Nucleotide Sequencing (methods), Programming Languages (MeSH).
MESH :
- methods : Genomics, High-Throughput Nucleotide Sequencing.
- Programming Languages.

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.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4184253

DOI: 10.1093/bioinformatics/btu385
PubMed: 24982428
PubMed Central: 4184253

Affiliations:

Le document en format XML

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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Genomics (methods)</term>
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<front><div type="abstract" xml:lang="en"><p><bold>Summary:</bold>
 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.</p>
<p><bold>Availability and implementation:</bold>
 Source code is available for academic non-commercial research purposes. Links to code and documentation are provided at <ext-link ext-link-type="uri" xlink:href="http://lpm.hms.harvard.edu">http://lpm.hms.harvard.edu</ext-link>
 and <ext-link ext-link-type="uri" xlink:href="http://wall-lab.stanford.edu">http://wall-lab.stanford.edu</ext-link>
.</p>
<p><bold>Contact:</bold>
<email>dpwall@stanford.edu</email>
 or <email>peter_tonellato@hms.harvard.edu</email>
.</p>
<p><bold>Supplementary information</bold>
: <ext-link ext-link-type="uri" xlink:href="http://bioinformatics.oxfordjournals.org/lookup/suppl/doi:10.1093/bioinformatics/btu385/-/DC1">Supplementary data</ext-link>
 are available at <italic>Bioinformatics</italic>
 online.</p>
</div>
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COSMOS: Python library for massively parallel workflows

COSMOS: Python library for massively parallel workflows

Source :

Descripteurs français

English descriptors

Abstract

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	Serveur sur les données et bibliothèques médicales au Maghreb (version finale)
	Attention, ce site est en cours de développement ! Attention, site généré par des moyens informatiques à partir de corpus bruts. Les informations ne sont donc pas validées.