Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite

Takehiro Hashimoto; Michiel J L de Hoon; Sean M Grimmond; Carsten O Daub; Yoshihide Hayashizaki; Geoffrey J Faulkner

doi:10.1093/bioinformatics/btp438

Probabilistic resolution of multi-mapping reads in massively parallel sequencing data using MuMRescueLite

Bioinformatics. 2009 Oct 1;25(19):2613-4. doi: 10.1093/bioinformatics/btp438. Epub 2009 Jul 15.

Authors

Takehiro Hashimoto¹, Michiel J L de Hoon, Sean M Grimmond, Carsten O Daub, Yoshihide Hayashizaki, Geoffrey J Faulkner

Affiliation

¹ Omics Science Center, RIKEN Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.

PMID: 19605420
DOI: 10.1093/bioinformatics/btp438

Abstract

Multi-mapping sequence tags are a significant impediment to short-read sequencing platforms. These tags are routinely omitted from further analysis, leading to experimental bias and reduced coverage. Here, we present MuMRescueLite, a low-resource requirement version of the MuMRescue software that has been used by several next generation sequencing projects to probabilistically reincorporate multi-mapping tags into mapped short read data.

Availability and implementation: MuMRescueLite is written in Python; executables and documentation are available from http://genome.gsc.riken.jp/osc/english/software/.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods*
Sequence Analysis, DNA / methods*
Software*