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Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment.

Yilei Fu1, Medhat Mahmoud2,3, Viginesh Vaibhav Muraliraman1

  • 1Department of Computer Science, Rice University, Houston, TX 77251-1892, USA.

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Vulcan improves structural variant detection by using dual-mode alignment for long-read sequencing data. This novel approach enhances read mapping accuracy, leading to better identification of genomic variations.

Keywords:
gap penaltylong-readread mappingstructural variation

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Long-read sequencing advances structural variation analysis in the human genome.
  • Existing read mappers (minimap2, NGMLR) balance speed and accuracy with variable performance.
  • Single gap penalty models can reduce alignment accuracy at mutational hotspots.

Purpose of the Study:

  • To test the hypothesis that a single gap penalty hinders structural variant detection.
  • To develop a novel read-mapping pipeline to improve structural variant calling accuracy.

Main Methods:

  • Implemented Vulcan, a dual-mode alignment pipeline.
  • Vulcan uses minimap2 for initial mapping and NGMLR for realignment of poorly mapped reads.
  • Leverages normalized edit distance to identify and realign reads.

Main Results:

  • Vulcan demonstrated improved alignment of Oxford Nanopore Technology long reads on simulated and real datasets.
  • Enhanced read alignments translated to improved structural variant calling accuracy.
  • Achieved better recall and precision for structural variant detection compared to single-mapper approaches.

Conclusions:

  • Vulcan is the first framework to combine distinct gap penalty modes for enhanced structural variant analysis.
  • The dual-mode alignment strategy improves structural variant recall and precision.
  • Vulcan is open-source, promoting wider adoption in genomic research.