Introduction

MOTIVATION: 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. AVAILABILITY: Musket is available at http://musket.sourceforge.net.

Publications

  1. Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data.
    Cite this
    Liu Y, Schröder J, Schmidt B, 2013-02-01 - Bioinformatics (Oxford, England)

Credits

  1. Yongchao Liu
    Developer

  2. Jan Schröder
    Developer

  3. Bertil Schmidt
    Investigator

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Summary
AccessionBT007015
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByBertil Schmidt