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dc.contributor.authorDröge, Johannes
dc.contributor.authorSchönhuth, Alexander
dc.contributor.authorMcHardy, Alice Carolyn
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.
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
refterms.dateFOA2018-06-13T09:25:12Z
html.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.


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