Reference-free prediction of rearrangement breakpoint reads

Edward Wijaya, Kana Shimizu, Kiyoshi Asai, Michiaki Hamada

    Research output: Contribution to journalArticle

    4 Citations (Scopus)

    Abstract

    Availability and implementation: The source code of SlideSort-BPRcan be freely downloaded from https://code.google.com/p/slidesortbpr/.

    Motivation: Chromosome rearrangement events are triggered by atypical breaking and rejoining of DNA molecules, which are observed in many cancer-related diseases. The detection of rearrangement is typically done by using short reads generated by next-generation sequencing (NGS) and combining the reads with knowledge of a reference genome. Because structural variations and genomes differ from one person to another, intermediate comparison via a reference genome may lead to loss of information.

    Results: In this article, we propose a reference-free method for detecting clusters of breakpoints from the chromosomal rearrangements. This is done by directly comparing a set of NGS normal reads with another set that may be rearranged. Our method SlideSort-BPR (breakpoint reads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100×, it finds ∼88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

    Original languageEnglish
    Pages (from-to)2559-2567
    Number of pages9
    JournalBioinformatics
    Volume30
    Issue number18
    DOIs
    Publication statusPublished - 2014

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    ASJC Scopus subject areas

    • Biochemistry
    • Molecular Biology
    • Computational Theory and Mathematics
    • Computer Science Applications
    • Computational Mathematics
    • Statistics and Probability
    • Medicine(all)

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