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Jasmine and Iris: population-scale structural variant comparison and analysis.

Melanie Kirsche1, Gautam Prabhu1,2, Rachel Sherman1

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

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Summary
This summary is machine-generated.

Jasmine and Iris tools enable fast, accurate comparison of structural variants (SVs) from long-read sequencing data. These tools improve SV analysis and reveal their impact on gene expression.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Long-read sequencing is transforming structural variant (SV) studies.
  • Comparing SVs across individuals is challenging due to variations and technical limitations.

Purpose of the Study:

  • To introduce Jasmine and Iris for efficient SV refinement, comparison, and population analysis.
  • To develop a robust method for high-confidence SV discovery and genotyping.

Main Methods:

  • Utilized an SV proximity graph for SV comparison.
  • Developed Jasmine and Iris tools for SV analysis.
  • Created a unified callset of SVs and indels from diverse long-read sequenced samples.

Main Results:

  • Jasmine outperformed six existing SV comparison methods.
  • Achieved a fivefold reduction in Mendelian discordance in trio datasets.
  • Identified high-confidence de novo SVs and genotyped variants in 1,317 samples.
  • Assessed the impact of SVs on gene expression, including in medically relevant genes.

Conclusions:

  • Jasmine and Iris significantly enhance SV analysis accuracy and speed.
  • The study provides a comprehensive SV resource and highlights their functional impact.
  • This work facilitates deeper understanding of genomic variation and its role in health and disease.