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Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

Item Type:Article
Title:Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes
Creators Name:Nielsen, H.B. and Almeida, M. and Juncker, A.S. and Rasmussen, S. and Li, J. and Sunagawa, S. and Plichta, D.R. and Gautier, L. and Pedersen, A.G. and Le Chatelier, E. and Pelletier, E. and Bonde, I. and Nielsen, T. and Manichanh, C. and Arumugam, M. and Batto, J.M. and Quintanilha Dos Santos, M.B. and Blom, N. and Borruel, N. and Burgdorf, K.S. and Boumezbeur, F. and Casellas, F. and Dore, J. and Dworzynski, P. and Guarner, F. and Hansen, T. and Hildebrand, F. and Kaas, R.S. and Kennedy, S. and Kristiansen, K. and Kultima, J.R. and Leonard, P. and Levenez, F. and Lund, O. and Moumen, B. and Le Paslier, D. and Pons, N. and Pedersen, O. and Prifti, E. and Qin, J. and Raes, J. and Sorensen, S. and Tap, J. and Tims, S. and Ussery, D.W. and Yamada, T. and Renault, P. and Sicheritz-Ponten, T. and Bork, P. and Wang, J. and Brunak, S. and Ehrlich, S.D.
Abstract:Most current approaches for analyzing metagenomic data rely on comparisons to reference genomes, but the microbial diversity of many environments extends far beyond what is covered by reference databases. De novo segregation of complex metagenomic data into specific biological entities, such as particular bacterial strains or viruses, remains a largely unsolved problem. Here we present a method, based on binning co-abundant genes across a series of metagenomic samples, that enables comprehensive discovery of new microbial organisms, viruses and co-inherited genetic entities and aids assembly of microbial genomes without the need for reference sequences. We demonstrate the method on data from 396 human gut microbiome samples and identify 7,381 co-abundance gene groups (CAGs), including 741 metagenomic species (MGS). We use these to assemble 238 high-quality microbial genomes and identify affiliations between MGS and hundreds of viruses or genetic entities. Our method provides the means for comprehensive profiling of the diversity within complex metagenomic samples.
Keywords:Cluster Analysis, Genetic Databases, Metagenomics
Source:Nature Biotechnology
ISSN:1087-0156
Publisher:Nature Publishing Group (U.S.A.)
Volume:32
Number:8
Page Range:822-828
Date:August 2014
Official Publication:https://doi.org/10.1038/nbt.2939
PubMed:View item in PubMed

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