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Gap-Sensitive Colinear Chaining Algorithms for Acyclic Pangenome Graphs.

Ghanshyam Chandra1, Chirag Jain1

  • 1Department of Computational and Data Sciences, Indian Institute of Science Bengaluru, India.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|October 30, 2023
PubMed
Summary
This summary is machine-generated.

Pangenome graphs offer a compact genomic reference for species. This study introduces novel algorithms for sequence-to-graph alignment, improving accuracy and efficiency for genomic studies.

Keywords:
minimum path coversequence alignmentsparse dynamic programmingvariation graph

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Pangenome graphs provide a compact representation of multiple genomes within a species, crucial for advanced genomic studies.
  • Sequence alignment to pangenome graphs is essential for pangenome-based resequencing.

Purpose of the Study:

  • To develop efficient and accurate sequence-to-graph alignment algorithms that incorporate various gap cost functions.
  • To address the limitations of existing frameworks that do not consider gap costs.

Main Methods:

  • Developed novel problem formulations for sequence-to-graph chaining.
  • Designed provably good chaining algorithms supporting diverse gap cost functions.
  • Utilized a sparse dynamic programming framework exploiting the path cover property of acyclic pangenome graphs.

Main Results:

  • Achieved 98.7% precision in mapping simulated long reads to a 95-haplotype human pangenome graph.
  • Left less than 2% of reads unmapped, demonstrating high efficiency.
  • Empirically demonstrated superior performance compared to existing aligners.

Conclusions:

  • The novel chaining algorithms effectively handle various gap cost functions for sequence-to-graph alignment.
  • The developed methods significantly improve the practical effectiveness of pangenome-based resequencing.
  • This work advances the utility of pangenome graphs as a reference in genomic research.