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OctopusV and TentacleSV: a one-stop toolkit for multi-sample, cross-platform structural variant comparison and

Qingxiang Guo1, Yangyang Li1, Ting-You Wang1

  • 1Department of Urology, Northwestern University Feinberg School of Medicine, 303 E Superior St, Chicago, 60611, IL, USA.

Biorxiv : the Preprint Server for Biology
|April 8, 2025
PubMed
Summary

OctopusV standardizes structural variant (SV) annotations and integrates calls for cohort analysis. This tool, with TentacleSV, offers an end-to-end solution for improved genomic variant identification in research and diagnostics.

Keywords:
Structural variantsbreakend annotationgenomic pipelinesprecision genomicsvariant merging

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Structural variants (SVs) are crucial for genomic variability and disease.
  • Accurate SV analysis across diverse samples and sequencing platforms is a significant challenge.

Purpose of the Study:

  • To develop a computational tool, OctopusV, for standardizing structural variant annotations.
  • To enable robust cohort-specific variant identification through flexible integration of variant calls.
  • To provide an end-to-end solution for structural variant analysis from raw data to final callsets.

Main Methods:

  • OctopusV standardizes ambiguous breakend (BND) annotations into canonical SV types (inversions, duplications, translocations).
  • It integrates variant calls using set operations (union, intersection, difference, complement).
  • TentacleSV provides an automated pipeline for an end-to-end workflow.

Main Results:

  • OctopusV demonstrates improved precision, recall, and consistency in SV analysis.
  • The integrated approach facilitates cohort-specific variant identification.
  • The tools offer a comprehensive solution for structural variant detection.

Conclusions:

  • OctopusV and TentacleSV provide a valuable end-to-end solution for structural variant analysis.
  • These tools enhance accuracy and consistency, aiding cancer genomics and rare disease diagnostics.
  • The standardized approach improves the reliability of variant callsets across studies.