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dc.contributor.authorMünch, Philipp C
dc.contributor.authorStecher, Bärbel
dc.contributor.authorMcHardy, Alice C
dc.date.accessioned2019-05-09T09:01:54Z
dc.date.available2019-05-09T09:01:54Z
dc.date.issued2017-10-15
dc.identifier.citationBioinformatics. 2017 Oct 15;33(20):3292-3295. doi: 10.1093/bioinformatics/btx394.en_US
dc.identifier.issn1367-4811
dc.identifier.pmid28637301
dc.identifier.doi10.1093/bioinformatics/btx394
dc.identifier.urihttp://hdl.handle.net/10033/621765
dc.description.abstractMetagenomics revolutionized the field of microbial ecology, giving access to Gb-sized datasets of microbial communities under natural conditions. This enables fine-grained analyses of the functions of community members, studies of their association with phenotypes and environments, as well as of their microevolution and adaptation to changing environmental conditions. However, phylogenetic methods for studying adaptation and evolutionary dynamics are not able to cope with big data. EDEN is the first software for the rapid detection of protein families and regions under positive selection, as well as their associated biological processes, from meta- and pangenome data. It provides an interactive result visualization for detailed comparative analyses. Availability and implementation: EDEN is available as a Docker installation under the GPL 3.0 license, allowing its use on common operating systems, at http://www.github.com/hzi-bifo/eden.en_US
dc.language.isoenen_US
dc.publisherOxford Academicen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.titleEDEN: evolutionary dynamics within environments.en_US
dc.typeArticleen_US
dc.contributor.departmentBRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56,38106 Braunschweig, Germany.en_US
dc.identifier.journalBioinformaticsen_US
refterms.dateFOA2019-05-09T09:01:54Z
dc.source.journaltitleBioinformatics (Oxford, England)


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