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Structural Variant Detection from Long-Read Sequencing Data with cuteSV

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Structural Variation (SV) represents genomic rearrangements and is strongly associated with human health and disease. Recently, long-read sequencing technologies provide the opportunity to more comprehensive identification of SVs at an ever-high resolution. However, under the circumstance of high sequencing errors and the complexity of SVs, there remains lots of technical issues to be settled. Hence, we propose cuteSV, a sensitive, fast, and scalable alignment-based SV detection approach to complete comprehensive discovery of diverse SVs. The benchmarking results indicate cuteSV is suitable for large-scale genome project since its excellent SV yields and ultra-fast speed. Here, we explain the overall framework for providing a detailed outline for users to apply cuteSV correctly and comprehensively. More details are available at https://github.com/tjiangHIT/cuteSV.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages137-151
Number of pages15
DOIs
StatePublished - 2022

Publication series

NameMethods in Molecular Biology
Volume2493
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Alignment-based calling
  • Bioinformatics
  • Germline mutation calling
  • Long-read sequencing
  • Population-based calling
  • Scaling performance
  • Structural variants detection

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