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dc.contributor.authorMeyer, Fernando
dc.contributor.authorHofmann, Peter
dc.contributor.authorBelmann, Peter
dc.contributor.authorGarrido-Oter, Ruben
dc.contributor.authorFritz, Adrian
dc.contributor.authorSczyrba, Alexander
dc.contributor.authorMcHardy, Alice C
dc.date.accessioned2018-06-25T14:30:30Z
dc.date.available2018-06-25T14:30:30Z
dc.date.issued2018-06-08
dc.identifier.issn2047-217X
dc.identifier.pmid29893851
dc.identifier.doi10.1093/gigascience/giy069
dc.identifier.urihttp://hdl.handle.net/10033/621410
dc.description.abstractReconstructing the genomes of microbial community members is key to the interpretation of shotgun metagenome samples. Genome binning programs deconvolute reads or assembled contigs of such samples into individual bins, but assessing their quality is difficult due to the lack of evaluation software and standardized metrics. We present AMBER, an evaluation package for the comparative assessment of genome reconstructions from metagenome benchmark data sets. It calculates the performance metrics and comparative visualizations used in the first benchmarking challenge of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). As an application, we show the outputs of AMBER for eleven different binning programs on two CAMI benchmark data sets. AMBER is implemented in Python and available under the Apache 2.0 license on GitHub (https://github.com/CAMI-challenge/AMBER).en_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.titleAMBER: Assessment of Metagenome BinnERs.en_US
dc.typeArticleen_US
dc.contributor.departmentBRICS, Braunschweiger Zentrum für Systembiologie, Rebenring 56, 38106 Braunschweig, Germany.en_US
dc.source.journaltitleGigaScience


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