Sequence to graph alignment using gap-sensitive co-linear chaining

Abstract

AbstractCo-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 colinear chaining between two sequences, efficient subquadratic-time chaining algorithms are well-known for linear, concave and convex gap cost functions [Eppsteinet 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 [Makinenet 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.Implementationhttps://github.com/at-cg/minichain