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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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svpluscnv: analysis and visualization of complex structural variation data.

Gonzalo Lopez1, Laura E Egolf2,3, Federico M Giorgi4

  • 1Department of Genetics and Genomics Sciences, Icahn School of Medicine, New York, NY, 10029, USA.

Bioinformatics (Oxford, England)
|October 14, 2020
PubMed
Summary
This summary is machine-generated.

The svpluscnv R package integrates structural variation (SV) and copy number variant data for cancer research. It aids in analyzing chromosomal instability, identifying driver genes, and detecting complex genomic rearrangements.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic structural variations (SVs) are prevalent in cancer but poorly understood due to their heterogeneity.
  • Interpreting the pathogenic role of SVs is challenging, necessitating integrated analysis tools.
  • Characterizing the cancer somatic landscape requires tools that can handle large, diverse SV datasets.

Purpose of the Study:

  • To introduce the svpluscnv R package for integrating and interpreting copy number variant (CNV) and structural variant (SV) data.
  • To provide tools for evaluating chromosomal instability, ploidy, and identifying genes affected by recurrent SVs.
  • To enable the detection of complex genomic rearrangements and shattered genomic regions.

Main Methods:

  • The svpluscnv R package integrates copy number variant segmentation profiles with sequencing-based structural variant calls.
  • It offers analysis and visualization tools for assessing chromosomal instability and ploidy.
  • The package facilitates the identification of genes with recurrent SVs and complex rearrangements like chromothripsis.

Main Results:

  • svpluscnv enables comprehensive analysis of orthogonal datasets for SVs and CNVs.
  • The package successfully identifies genes harboring recurrent SVs and detects complex rearrangements.
  • It systematically identifies hot-spot shattered genomic regions, demonstrating reproducibility across methods.

Conclusions:

  • svpluscnv is a versatile tool for the integrated analysis of SVs and CNVs in cancer genomics.
  • The package enhances the understanding of molecular implications of structural variations.
  • svpluscnv facilitates the characterization of the cancer somatic landscape and complex genomic events.