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A Hitchhiker Guide to Structural Variant Calling: A Comprehensive Benchmark Through Different Sequencing

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Choosing the right alignment software significantly impacts structural variant (SV) detection accuracy in whole-genome sequencing (WGS). Performance varies between short-read (srWGS) and long-read (lrWGS) technologies, highlighting the need for standardized methods.

Keywords:
benchmarkbest practiceslong readsstructural variantsvariant callingwhole-genome sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Structural variants (SVs) are crucial for gene function and are linked to human diseases.
  • Whole-genome sequencing (WGS) is vital for identifying SVs, but tool variability hinders accuracy.
  • Standardized methodologies for SV detection are lacking.

Purpose of the Study:

  • To evaluate the accuracy of structural variant detection across different sequencing technologies.
  • To assess the impact of variant calling algorithms, reference genomes, alignment strategies, and sequencing coverage on SV detection.
  • To compare short-read (srWGS) and long-read (lrWGS) sequencing performance for SV identification.

Main Methods:

  • Assessed SV detection accuracy using deletion calls from the HG002 benchmark dataset.
  • Tested Illumina short reads, PacBio long reads, and Oxford Nanopore Technologies (ONT) long reads.
  • Examined the influence of variant callers, reference genomes, alignment strategies, and sequencing coverage.

Main Results:

  • DRAGEN v4.2 showed highest accuracy for srWGS; graph-based references improved calling in complex regions.
  • Combining minimap2 with Manta achieved srWGS performance comparable to DRAGEN.
  • Sniffles2 excelled for PacBio lrWGS; minimap2 alignment was best for ONT lrWGS; Duet and Dysgu performed best at different coverages.

Conclusions:

  • Alignment software choice significantly impacts srWGS structural variant calling accuracy.
  • Long-read sequencing (lrWGS) performance for SV detection is dependent on the specific technology and sequencing coverage.
  • These findings offer insights into optimizing SV detection across diverse sequencing platforms.