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

Research output: Contribution to journalJournal articleResearchpeer-review

  • H Bjørn Nielsen
  • Mathieu Almeida
  • Agnieszka Juncker
  • Rasmussen, Simon
  • Junhua Li
  • Shinichi Sunagawa
  • Damian Rafal Plichta
  • Laurent Gautier
  • Anders G. Pedersen
  • Emmanuelle Le Chatelier
  • Eric Pelletier
  • Ida Bonde
  • Trine Nielsen
  • Chaysavanh Manichanh
  • Arumugam, Mani
  • Jean-Michel Batto
  • Marcelo Bertalan Quintanilha dos Santos
  • Nikolaj Blom
  • Natalia Borruel
  • Burgdorf, Kristoffer Sølvsten
  • Fouad Boumezbeur
  • Francesc Casellas
  • Joël Doré
  • Piotr Dworzynski
  • Francisco Guarner
  • Hansen, Torben
  • Falk Hildebrand
  • Rolf Sommer Kaas
  • Sean Kennedy
  • Kristiansen, Karsten
  • Jens Roat Kultima
  • Pierre Léonard
  • Florence Levenez
  • Ole Lund
  • Bouziane Moumen
  • Denis Le Paslier
  • Nicolas Pons
  • Pedersen, Oluf Borbye
  • Edi Prifti
  • Junjie Qin
  • Jeroen Raes
  • Sørensen, Søren Johannes
  • Julien Tap
  • Sebastian Tims
  • David Ussery
  • Takuji Yamada
  • Pierre Renault
  • Thomas Sicheritz-Pontén
  • Peer Bork
  • Jun Wang
  • MetaHIT Consortium

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.

Original languageEnglish
JournalNature Biotechnology
Issue number8
Pages (from-to)822–828
Number of pages7
Publication statusPublished - 2014

ID: 119293516