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svtools: population-scale analysis of structural variation.

David E Larson1,2, Haley J Abel1,2, Colby Chiang1

  • 1McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA.

Bioinformatics (Oxford, England)
|June 21, 2019
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Summary
This summary is machine-generated.

A new software toolkit, svtools, enables efficient structural variation analysis in large human genetics studies. This scalable pipeline provides high-quality structural variation maps for thousands of genomes, advancing human genetics research.

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Area of Science:

  • Human Genetics
  • Genomics
  • Bioinformatics

Background:

  • Large-scale human genetics studies utilize whole genome sequencing for trait mapping.
  • Current methods for structural variation (SV) analysis do not scale efficiently for population-level studies.

Purpose of the Study:

  • To develop a fast, scalable software toolkit and cloud-based pipeline for comprehensive SV analysis in large human populations.
  • To enable high-quality structural variation mapping across thousands of genomes.

Main Methods:

  • Development of a Python-based software toolkit named svtools.
  • Implementation of a cloud-based pipeline for joint SV analysis.
  • Evaluation of variant detection performance against established methods like LUMPY.

Main Results:

  • The svtools pipeline demonstrates high scalability for analyzing structural variations (deletions, duplications, mobile element insertions, inversions, etc.).
  • Achieves comparable variant detection performance to existing per-sample methods.
  • Enables fast and affordable joint analysis for datasets of 100,000+ genomes.

Conclusions:

  • svtools provides an efficient solution for large-scale structural variation mapping in human populations.
  • The toolkit and pipeline will facilitate next-generation human genetics studies.
  • Freely available open-source software (MIT license) promotes accessibility and adoption.