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Co-Linear Chaining on Pangenome Graphs

Authors Jyotshna Rajput , Ghanshyam Chandra , Chirag Jain



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Author Details

Jyotshna Rajput
  • Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
Ghanshyam Chandra
  • Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India
Chirag Jain
  • Department of Computational and Data Sciences, Indian Institute of Science, Bangalore, India

Acknowledgements

The authors thank Manuel Cáceres, Shravan Mehra and Sunil Chandran for providing useful feedback.

Cite AsGet BibTex

Jyotshna Rajput, Ghanshyam Chandra, and Chirag Jain. Co-Linear Chaining on Pangenome Graphs. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 12:1-12:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.WABI.2023.12

Abstract

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width [Makinen et al., TALG'19] and how incorporating gap cost in the scoring function improves alignment accuracy [Chandra and Jain, RECOMB'23]. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational genomics
Keywords
  • Sequence alignment
  • variation graph
  • genome sequencing
  • path cover

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