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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Structural Variant Breakpoint Detection with novoBreak.

Zechen Chong1, Ken Chen2

  • 1Department of Genetics and Informatics Institute, School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, USA. zchong@uab.edu.

Methods in Molecular Biology (Clifton, N.J.)
|July 25, 2018
PubMed
Summary
This summary is machine-generated.

novoBreak is a novel algorithm for detecting structural variations (SVs) in cancer genomics. It achieved top performance in a major challenge and is available as open-source software for both somatic and germline SV detection.

Keywords:
AlgorithmDNA sequence analysisDe novo assemblyGenetic variationGenomic rearrangementNext generation sequencing data analysisStructural variationsk-mer

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Structural variations (SVs) are crucial genomic alterations in cancer development.
  • Detecting SVs from short sequencing data remains a significant challenge in cancer genomics.

Purpose of the Study:

  • To provide detailed instructions for applying novoBreak, an open-source software, for somatic structural variation detection.
  • To introduce the application of novoBreak for germline SV detection and its use on the Seven Bridges Cancer Genomics Cloud.

Main Methods:

  • Development and application of the novoBreak algorithm for SV detection.
  • Utilizing short sequencing data for comprehensive SV analysis.
  • Leveraging open-source software and cloud-based workflows for genomic analysis.

Main Results:

  • novoBreak demonstrated the highest balanced accuracy in the ICGC-TCGA DREAM 8.5 Somatic Mutation Calling Challenge.
  • The study provides detailed instructions for applying novoBreak for somatic SV detection.
  • The novoBreak pipeline is also applicable for germline SV detection.

Conclusions:

  • novoBreak is a high-performing, open-source tool for detecting structural variations in cancer genomics.
  • The software facilitates both somatic and germline SV analysis, with cloud-based options available.
  • This work offers valuable resources for researchers in cancer genomics.