title: Compareads: comparing huge metagenomic experiments.
abstract: BACKGROUND
  Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.
  
  
  METHODS
  This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.
  
  
  RESULTS
  We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.
  
  
  CONCLUSIONS
  Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.
authors.affiliation: INRIA Rennes - Bretagne Atlantique/IRISA, EPI GenScale, Rennes, France. nicolas.maillet@inria.fr
authors.forename: Nicolas
authors.initials: N
authors.lastname: Maillet
authors.forename: Claire
authors.initials: C
authors.lastname: Lemaitre
authors.forename: Rayan
authors.initials: R
authors.lastname: Chikhi
authors.forename: Dominique
authors.initials: D
authors.lastname: Lavenier
authors.forename: Pierre
authors.initials: P
authors.lastname: Peterlongo
mesh_terms: D000465:Algorithms; D016247:Information Storage and Retrieval / Q000379:methods*; D056186:Metagenomics / Q000379:methods*