| Literature DB >> 24997787 |
H Bjørn Nielsen1, Mathieu Almeida2, Agnieszka Sierakowska Juncker3, Simon Rasmussen4, Junhua Li5, Shinichi Sunagawa6, Damian R Plichta4, Laurent Gautier4, Anders G Pedersen4, Emmanuelle Le Chatelier7, Eric Pelletier8, Ida Bonde3, Trine Nielsen9, Chaysavanh Manichanh10, Manimozhiyan Arumugam11, Jean-Michel Batto7, Marcelo B Quintanilha Dos Santos4, Nikolaj Blom12, Natalia Borruel10, Kristoffer S Burgdorf9, Fouad Boumezbeur7, Francesc Casellas10, Joël Doré7, Piotr Dworzynski4, Francisco Guarner10, Torben Hansen13, Falk Hildebrand14, Rolf S Kaas15, Sean Kennedy7, Karsten Kristiansen16, Jens Roat Kultima6, Pierre Léonard7, Florence Levenez7, Ole Lund4, Bouziane Moumen7, Denis Le Paslier8, Nicolas Pons7, Oluf Pedersen17, Edi Prifti7, Junjie Qin18, Jeroen Raes19, Søren Sørensen20, Julien Tap6, Sebastian Tims21, David W Ussery4, Takuji Yamada22, Pierre Renault23, Thomas Sicheritz-Ponten3, Peer Bork24, Jun Wang25, Søren Brunak3, S Dusko Ehrlich26.
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.Entities:
Mesh:
Year: 2014 PMID: 24997787 DOI: 10.1038/nbt.2939
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908