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Updated: Jun 12, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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VISTA: an integrated framework for structural variant discovery.

Varuni Sarwal1, Seungmo Lee1, Jianzhi Yang2

  • 1Department of Computer Science, University of California Los Angeles, 580 Portola Plaza, Los Angeles, CA 90095, United States.

Briefings in Bioinformatics
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

VISTA is a new computational framework that accurately identifies structural variants (SVs) in genomes. It overcomes limitations of existing tools by integrating multiple callers and optimizing for precision or recall, improving SV detection in genomic research.

Keywords:
bioinformaticscomputational biologymachine learningstructural variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Structural variations (SVs) like insertions, deletions, inversions, and duplications are key to human genome diversity.
  • Despite their small proportion, SVs are implicated in various diseases including autoimmune disorders, neurodevelopmental disorders, and schizophrenia.
  • Current methods for detecting SVs from whole-genome sequencing (WGS) data often suffer from high false-positive rates and struggle to accurately identify the full spectrum of SVs.

Purpose of the Study:

  • To develop an integrated structural variant calling framework, VISTA, that improves the accuracy and comprehensiveness of SV detection.
  • To address the limitations of existing SV callers, particularly their inability to maintain high accuracy across different SV lengths and genomic coverages.
  • To provide a robust and accurate tool for SV detection that outperforms current consensus-based methods.

Main Methods:

  • Developed VISTA, an integrated framework that uses a novel filtering and merging algorithm to leverage results from individual SV callers.
  • Implemented a strategy where VISTA executes combinations of top-performing callers tailored to specific variant lengths and genomic coverages.
  • Evaluated VISTA's performance on gold-standard datasets, including the Genome-in-a-Bottle SV set, Human Pangenome Reference Consortium assemblies, and a PCR-validated mouse dataset.

Main Results:

  • VISTA achieved the highest F1 score among leading consensus-based tools on comprehensive gold-standard datasets for both human and mouse genomes.
  • VISTA demonstrated superior accuracy in detecting structural variants across varying lengths and genomic coverages.
  • An optimized mode of VISTA (VISTA-optimized) achieved 100% precision and the highest sensitivity compared to other variant callers.

Conclusions:

  • VISTA represents a significant advancement in structural variant calling, offering a robust and accurate framework.
  • The VISTA framework overcomes key limitations of existing tools, setting a new standard for SV detection in genomic research.
  • VISTA's ability to integrate multiple callers and adapt to variant characteristics enhances its utility for diverse genomic studies.