SVLR : Genome Structural Variant Detection Using Long-Read Sequencing Data

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Wenyan GU
  • Aizhong ZHOU
  • Shiwei SUN
  • Xuefeng CUI
  • Daming ZHU

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)774-788
Journal / PublicationJournal of Computational Biology
Volume28
Issue number8
Online published5 Aug 2021
Publication statusPublished - Aug 2021

Abstract

Genome structural variants (SVs) have great impacts on human phenotype and diversity, and have been linked to numerous diseases. Long-read sequencing technologies arise to make it possible to find SVs of as long as 10,000 nucleotides. Thus, long read-based SV detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect SVs recently. In this article, we present a new method, called SVLR, to detect SVs based on long-read sequencing data. Comparing with existing methods, SVLR can detect three new kinds of SVs: block replacements, block interchanges, and translocations. Although these new SVs are structurally more complicated, SVLR achieves accuracies that are comparable with those of the classic SVs. Moreover, for the classic SVs that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up to 38% without harming the precisions (i.e., >78%). We also point out three directions to further improve SV detection in the future. Source codes: https://github.com/GWYSDU/SVLR

Research Area(s)

  • genome structural variant, genome structural variant detection, long-read sequencing and single-molecule sequencing, third-generation sequencing

Citation Format(s)

SVLR : Genome Structural Variant Detection Using Long-Read Sequencing Data. / GU, Wenyan; ZHOU, Aizhong; WANG, Lusheng; SUN, Shiwei; CUI, Xuefeng; ZHU, Daming.

In: Journal of Computational Biology, Vol. 28, No. 8, 08.2021, p. 774-788.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review