CLOVE: Classification of genomic fusions into structural variation events

Jan Schröder, Adrianto Wirawan, Bertil Schmidt, Anthony T. Papenfuss

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Background: A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than one breakpoint or fusion). Results: We present CLOVE, an algorithm for integrating the results of multiple breakpoint or SV callers and classifying the results as a particular SV. CLOVE is based on a graph data structure that is created from the breakpoint information. The algorithm looks for patterns in the graph that are characteristic of more complex rearrangement types. CLOVE is able to integrate the results of multiple callers, producing a consensus call. Conclusions: We demonstrate using simulated and real data that re-classified SV calls produced by CLOVE improve on the raw call set of existing SV algorithms, particularly in terms of accuracy. CLOVE is freely available from http://www.github.com/PapenfussLab.

Original languageEnglish
Article number346
Number of pages10
JournalBMC Bioinformatics
Volume18
Issue number1
DOIs
Publication statusPublished - 20 Jul 2017
Externally publishedYes

Keywords

  • Genomic rearrangements
  • Structural variations

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