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Sequence to Graph Alignment Using Gap-Sensitive Co-linear Chaining

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Research in Computational Molecular Biology (RECOMB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13976))

Abstract

Co-linear chaining is a widely used technique in sequence alignment tools that follow seed-filter-extend methodology. It is a mathematically rigorous approach to combine short exact matches. For co-linear chaining between two sequences, efficient subquadratic-time chaining algorithms are well-known for linear, concave and convex gap cost functions [Eppstein et al. JACM’92]. However, developing extensions of chaining algorithms for directed acyclic graphs (DAGs) has been challenging. Recently, a new sparse dynamic programming framework was introduced that exploits small path cover of pangenome reference DAGs, and enables efficient chaining [Makinen et al. TALG’19, RECOMB’18]. However, the underlying problem formulation did not consider gap cost which makes chaining less effective in practice. To address this, we develop novel problem formulations and optimal chaining algorithms that support a variety of gap cost functions. We demonstrate empirically the ability of our provably-good chaining implementation to align long reads more precisely in comparison to existing aligners. For mapping simulated long reads from human genome to a pangenome DAG of 95 human haplotypes, we achieve \(98.7\%\) precision while leaving \(<2\%\) reads unmapped.

Implementation: https://github.com/at-cg/minichain.

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Acknowledgements

This work was supported by funding from the National Supercomputing Mission, India under DST/NSM/R &D_HPC_Applications. We used computing resources provided by the C-DAC National PARAM Supercomputing Facility, India, and the National Energy Research Scientific Computing Center, USA.

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Correspondence to Chirag Jain .

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Chandra, G., Jain, C. (2023). Sequence to Graph Alignment Using Gap-Sensitive Co-linear Chaining. In: Tang, H. (eds) Research in Computational Molecular Biology. RECOMB 2023. Lecture Notes in Computer Science(), vol 13976. Springer, Cham. https://doi.org/10.1007/978-3-031-29119-7_4

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  • DOI: https://doi.org/10.1007/978-3-031-29119-7_4

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