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svclassify: a method to establish benchmark structural variant calls.

Hemang Parikh1,2, Marghoob Mohiyuddin3, Hugo Y K Lam4

  • 1Genome-Scale Measurements Group, Material Measurement Laboratory, National Institute of Standards and Technology, 100 Bureau Dr, MS8313, Gaithersburg, MD, 20899, USA. parikhhemangm@gmail.com.

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Summary
This summary is machine-generated.

Developing a new method, svclassify, improves the accuracy of identifying structural variants (SVs) in human genomes. This approach combines evidence from multiple sequencing technologies to reliably classify SVs, aiding in benchmark genome creation.

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Area of Science:

  • Genomics and Bioinformatics
  • Human Genetics
  • Computational Biology

Background:

  • Human genomes contain a wide spectrum of variants, from single nucleotide polymorphisms (SNPs) to large structural variants (SVs).
  • While high-quality benchmark small variant calls exist for reference genomes like NA12878, reliable benchmark calls for large structural variants are lacking.
  • Existing structural variant callers produce highly discordant results, necessitating improved methods for accurate variant identification.

Purpose of the Study:

  • To develop and validate a computational method for classifying candidate structural variants (SVs) into true or false positives.
  • To establish a high-confidence benchmark set of structural variants for the NA12878 genome.
  • To improve the reliability and concordance of structural variant detection across different sequencing technologies.

Main Methods:

  • Developed 'svclassify', a method that integrates evidence from multiple sequencing technologies and aligned BAM files.
  • Employed pedigree analysis to define high-confidence breakpoint-resolved deletions.
  • Utilized a supervised one-class classification model trained on non-SV regions to identify SVs with abnormal genomic annotations.

Main Results:

  • Likely structural variants (SVs) demonstrated distinct clustering based on annotations compared to non-SVs.
  • The svclassify method successfully clustered deletions into different types and classified insertions.
  • High-scoring candidate SVs showed high concordance (99.7%) with PCR validation and the MetaSV consensus method.

Conclusions:

  • The svclassify method effectively distinguishes true structural variants from false positives using multi-technology evidence.
  • A benchmark set of 2676 high-confidence deletions and 68 high-confidence insertions is provided for SV caller evaluation.
  • This approach is valuable for establishing high-confidence SV calls in benchmark samples characterized by multiple sequencing technologies.