2.50
Hdl Handle:
http://hdl.handle.net/10033/621140
Title:
A probabilistic model to recover individual genomes from metagenomes
Authors:
Dröge, Johannes ( 0000-0002-6752-2204 ) ; Schönhuth, Alexander; McHardy, Alice Carolyn
Abstract:
Shotgun metagenomics of microbial communities reveal information about strains of relevance for applications in medicine, biotechnology and ecology. Recovering their genomes is a crucial but very challenging step due to the complexity of the underlying biological system and technical factors. Microbial communities are heterogeneous, with oftentimes hundreds of present genomes deriving from different species or strains, all at varying abundances and with different degrees of similarity to each other and reference data. We present a versatile probabilistic model for genome recovery and analysis, which aggregates three types of information that are commonly used for genome recovery from metagenomes. As potential applications we showcase metagenome contig classification, genome sample enrichment and genome bin comparisons. The open source implementation MGLEX is available via the Python Package Index and on GitHub and can be embedded into metagenome analysis workflows and programs.
Affiliation:
BRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.
Citation:
A probabilistic model to recover individual genomes from metagenomes 2017, 3:e117 PeerJ Computer Science
Journal:
PeerJ Computer Science
Issue Date:
22-May-2017
URI:
http://hdl.handle.net/10033/621140
DOI:
10.7717/peerj-cs.117
Additional Links:
https://peerj.com/articles/cs-117
Type:
Article
ISSN:
2376-5992
Appears in Collections:
publications of the research group bioinformatics in infection research ([BRICS] BIFO)

Full metadata record

DC FieldValue Language
dc.contributor.authorDröge, Johannesen
dc.contributor.authorSchönhuth, Alexanderen
dc.contributor.authorMcHardy, Alice Carolynen
dc.date.accessioned2017-10-20T14:38:56Z-
dc.date.available2017-10-20T14:38:56Z-
dc.date.issued2017-05-22-
dc.identifier.citationA probabilistic model to recover individual genomes from metagenomes 2017, 3:e117 PeerJ Computer Scienceen
dc.identifier.issn2376-5992-
dc.identifier.doi10.7717/peerj-cs.117-
dc.identifier.urihttp://hdl.handle.net/10033/621140-
dc.description.abstractShotgun metagenomics of microbial communities reveal information about strains of relevance for applications in medicine, biotechnology and ecology. Recovering their genomes is a crucial but very challenging step due to the complexity of the underlying biological system and technical factors. Microbial communities are heterogeneous, with oftentimes hundreds of present genomes deriving from different species or strains, all at varying abundances and with different degrees of similarity to each other and reference data. We present a versatile probabilistic model for genome recovery and analysis, which aggregates three types of information that are commonly used for genome recovery from metagenomes. As potential applications we showcase metagenome contig classification, genome sample enrichment and genome bin comparisons. The open source implementation MGLEX is available via the Python Package Index and on GitHub and can be embedded into metagenome analysis workflows and programs.en
dc.relation.urlhttps://peerj.com/articles/cs-117en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleA probabilistic model to recover individual genomes from metagenomes
dc.typeArticleen
dc.contributor.departmentBRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.en
dc.identifier.journalPeerJ Computer Scienceen
dc.contributor.institutionComputational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany-
dc.contributor.institutionCentrum Wiskunde & Informatica, Amsterdam, The Netherlands-
dc.contributor.institutionComputational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany-
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