TY - JOUR

T1 - Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes

AU - Nielsen, H Bjørn

AU - Almeida, Mathieu

AU - Juncker, Agnieszka

AU - Rasmussen, Simon

AU - Li, Junhua

AU - Sunagawa, Shinichi

AU - Plichta, Damian Rafal

AU - Gautier, Laurent

AU - Pedersen, Anders G.

AU - Le Chatelier, Emmanuelle

AU - Pelletier, Eric

AU - Bonde, Ida

AU - Nielsen, Trine

AU - Manichanh, Chaysavanh

AU - Arumugam, Manimozhiyan

AU - Batto, Jean-Michel

AU - dos Santos, Marcelo Bertalan Quintanilha

AU - Blom, Nikolaj

AU - Borruel, Natalia

AU - Burgdorf, Kristoffer Sølvsten

AU - Boumezbeur, Fouad

AU - Casellas, Francesc

AU - Doré, Joël

AU - Dworzynski, Piotr

AU - Guarner, Francisco

AU - Hansen, Torben

AU - Hildebrand, Falk

AU - Kaas, Rolf Sommer

AU - Kennedy, Sean

AU - Kristiansen, Karsten

AU - Kultima, Jens Roat

AU - Léonard, Pierre

AU - Levenez, Florence

AU - Lund, Ole

AU - Moumen, Bouziane

AU - Le Paslier, Denis

AU - Pons, Nicolas

AU - Pedersen, Oluf Borbye

AU - Prifti, Edi

AU - Qin, Junjie

AU - Raes, Jeroen

AU - Sørensen, Søren Johannes

AU - Tap, Julien

AU - Tims, Sebastian

AU - Ussery, David

AU - Yamada, Takuji

AU - Renault, Pierre

AU - Sicheritz-Pontén, Thomas

AU - Bork, Peer

AU - Wang, Jun

AU - MetaHIT Consortium

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1038/nbt.2939

DO - 10.1038/nbt.2939

M3 - Journal article

C2 - 24997787

VL - 32

SP - 822

EP - 828

JO - Nature Biotechnology

JF - Nature Biotechnology

SN - 1087-0156

IS - 8

ER -