MMBIRFinder: A Tool to Detect Microhomology-Mediated Break-Induced Replication

Matthew W. Segar, Cynthia J. Sakofsky, Anna Malkova, Yunlong Liu

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The introduction of next-generation sequencing technologies has radically changed the way we view structural genetic events. Microhomology-mediated break-induced replication (MMBIR) is just one of the many mechanisms that can cause genomic destabilization that may lead to cancer. Although the mechanism for MMBIR remains unclear, it has been shown that MMBIR is typically associated with template-switching events. Currently, to our knowledge, there is no existing bioinformatics tool to detect these template-switching events. We have developed MMBIRFinder, a method that detects template-switching events associated with MMBIR from whole-genome sequenced data. MMBIRFinder uses a half-read alignment approach to identify potential regions of interest. Clustering of these potential regions helps narrow the search space to regions with strong evidence. Subsequent local alignments identify the template-switching events with single-nucleotide accuracy. Using simulated data, MMBIRFinder identified 83 percent of the MMBIR regions within a five nucleotide tolerance. Using real data, MMBIRFinder identified 16 MMBIR regions on a normal breast tissue data sample and 51 MMBIR regions on a triple-negative breast cancer tumor sample resulting in detection of 37 novel template-switching events. Finally, we identified template-switching events residing in the promoter region of seven genes that have been implicated in breast cancer. The program is freely available for download at https://github.com/msegar/MMBIRFinder.

Original languageEnglish (US)
Article number6948337
Pages (from-to)799-806
Number of pages8
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume12
Issue number4
DOIs
StatePublished - Jul 1 2015

Keywords

  • Biology and genetics
  • Life and Medical Sciences

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

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