title: Spaced seeds improve k-mer-based metagenomic classification.
abstract: BACKGROUND
  Metagenomics is a powerful approach to study genetic content of environmental samples, which has been strongly promoted by next-generation sequencing technologies. To cope with massive data involved in modern metagenomic projects, recent tools rely on the analysis of k-mers shared between the read to be classified and sampled reference genomes.
  
  
  RESULTS
  Within this general framework, we show that spaced seeds provide a significant improvement of classification accuracy, as opposed to traditional contiguous k-mers. We support this thesis through a series of different computational experiments, including simulations of large-scale metagenomic projects.Availability and implementation, Supplementary information: Scripts and programs used in this study, as well as supplementary material, are available from http://github.com/gregorykucherov/spaced-seeds-for-metagenomics.
  
  
  BACKGROUND
  gregory.kucherov@univ-mlv.fr.
authors.affiliation: LIGM/CNRS, Université Paris-Est, 77454 Marne-la-Vallée, France.
authors.forename: Karel
authors.initials: K
authors.lastname: Břinda
authors.affiliation: LIGM/CNRS, Université Paris-Est, 77454 Marne-la-Vallée, France.
authors.forename: Maciej
authors.initials: M
authors.lastname: Sykulski
authors.affiliation: LIGM/CNRS, Université Paris-Est, 77454 Marne-la-Vallée, France.
authors.forename: Gregory
authors.initials: G
authors.lastname: Kucherov
mesh_terms: D000465:Algorithms; D001407:Bacillus / Q000235:genetics; D030541:Databases, Genetic; D016680:Genome, Bacterial; D056186:Metagenomics / Q000145:classification*; D009161:Mycobacterium / Q000235:genetics; D011336:Probability; D016415:Sequence Alignment; D018709:Statistics, Nonparametric