title: Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data.
abstract: BACKGROUND
  The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures.
  
  
  RESULTS
  In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master-slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time.
  
  
  BACKGROUND
  Musket is available at http://musket.sourceforge.net.
authors.affiliation: Institut für Informatik, Johannes Gutenberg Universität Mainz, Mainz 55099, Germany. liuy@uni-mainz.de
authors.forename: Yongchao
authors.initials: Y
authors.lastname: Liu
authors.forename: Jan
authors.initials: J
authors.lastname: Schröder
authors.forename: Bertil
authors.initials: B
authors.lastname: Schmidt
mesh_terms: D000465:Algorithms; D002877:Chromosomes, Human; D004926:Escherichia coli / Q000235:genetics; D016680:Genome, Bacterial; D006801:Humans; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000592:standards; D012984:Software