SVLR : Genome Structure Variant Detection Using Long Read Sequencing Data

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

  • Wenyan Gu
  • Aizhong Zhou
  • Shiwei Sun
  • Xuefeng Cui
  • Daming Zhu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationBioinformatics Research and Applications - 16th International Symposium, ISBRA 2020, Proceedings
EditorsZhipeng Cai, Ion Mandoiu, Giri Narasimhan
PublisherSpringer
Pages140-153
ISBN (Electronic)9783030578213
ISBN (Print)9783030578206
Publication statusPublished - Dec 2020

Publication series

NameLecture Notes in Computer Science
Volume12304
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title16th International Symposium on Bioinformatics Research and Applications (ISBRA 2020)
PlaceRussian Federation
CityMoscow
Period1 - 4 December 2020

Abstract

Genome structural variants 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 structural variants of as long as ten thousand nucleotides. Thus, long read based structural variant detection has been drawing attention of many recent research projects, and many tools have been developed for long reads to detect structural variants recently. In this article, we present a new method, called SVLR, to detect Structural Variants based on Long Read sequencing data. Comparing to existing methods, SVLR can detect three new kinds of structural variants: block replacements, block interchanges and translocations. Although these new structural variants are structurally more complicated, SVLR achieves accuracies that are comparable to those of the classic structural variants. Moreover, for the classic structural variants that can be detected by state-of-the-art methods (e.g., SVIM and Sniffles), our experiments demonstrate recall improvements of up-to without harming the precisions (i.e., above). We also point out three directions to further improve structural variant detection in the future.

Research Area(s)

  • Genome structural variant, Genome structural variant detection, Long-read sequencing, Single-molecule sequencing, Third generation sequencing

Citation Format(s)

SVLR : Genome Structure Variant Detection Using Long Read Sequencing Data. / Gu, Wenyan; Zhou, Aizhong; Wang, Lusheng; Sun, Shiwei; Cui, Xuefeng; Zhu, Daming.

Bioinformatics Research and Applications - 16th International Symposium, ISBRA 2020, Proceedings. ed. / Zhipeng Cai; Ion Mandoiu; Giri Narasimhan. Springer, 2020. p. 140-153 (Lecture Notes in Computer Science; Vol. 12304).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)