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Haplotype-aware sequence alignment to pangenome graphs.

Ghanshyam Chandra1, Daniel Gibney2, Chirag Jain3

  • 1Department of Computational and Data Sciences, Indian Institute of Science, Bangalore Karnataka 560012, India.

Genome Research
|July 16, 2024
PubMed
Summary
This summary is machine-generated.

We developed new algorithms for sequence alignment in pangenome graphs, incorporating a recombination penalty to improve accuracy. This method enhances read mapping consistency with known haplotypes, reducing spurious alignments in complex genomic regions.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pangenome graphs, built from haplotype-resolved assemblies, are crucial for accurate read mapping.
  • Current alignment methods overlook haplotype paths, leading to incorrect alignments and genotyping errors.

Purpose of the Study:

  • To develop novel algorithms for sequence-to-graph alignment and chaining that account for haplotype information.
  • To introduce a recombination penalty to penalize unlikely haplotype switches during alignment.

Main Methods:

  • Developed a haplotype-aware sequence-to-graph alignment algorithm with a recombination penalty.
  • Formulated a haplotype-aware chaining algorithm for improved anchor selection.
  • Proved theoretical lower bounds for both alignment and chaining problems under the Strong Exponential Time Hypothesis (SETH).

Main Results:

  • Achieved an alignment algorithm running in O(QE|H|) time, with a proven near-optimal lower bound.
  • Developed a chaining algorithm running in O(N^2) time post-preprocessing, also with a proven near-optimal lower bound.
  • Demonstrated improved consistency with ground-truth recombinations using the Minichain aligner on human MHC data.

Conclusions:

  • Haplotype-aware alignment and chaining algorithms significantly improve read mapping accuracy in pangenome graphs.
  • The proposed recombination penalty effectively models sequence variations as mosaics of reference haplotypes.
  • These advancements are critical for accurate genotyping and understanding genomic diversity.