Exact global alignment using A* with chaining seed heuristic and match pruning.

MOTIVATION: Sequence alignment has been at the core of computational biology for half a century. Still, it is an open problem to design a practical algorithm for exact alignment of a pair of related sequences in linear-like time. RESULTS: We solve exact global pairwise alignment with respect to edit distance by using the A* shortest path algorithm. In order to efficiently align long sequences with high divergence, we extend the recently proposed seed heuristic with match chaining, gap costs, and inexact matches. We additionally integrate the novel match pruning technique and diagonal transition to improve the A* search. We prove the correctness of our algorithm, implement it in the A*PA aligner, and justify our extensions intuitively and empirically.On random sequences of divergence d=4% and length n, the empirical runtime of A*PA scales near-linearly with length (best fit n1.06, n≤107 bp). A similar scaling remains up to d=12% (best fit n1.24, n≤107 bp). For n=107 bp and d=4%, A*PA reaches >500× speedup compared to the leading exact aligners Edlib and BiWFA. The performance of A*PA is highly influenced by long gaps. On long (n>500kb) ONT reads of a human sample it efficiently aligns sequences with d<10%, leading to 3× median speedup compared to Edlib and BiWFA. When the sequences come from different human samples, A*PA performs 1.7× faster than Edlib and BiWFA. AVAILABILITY AND IMPLEMENTATION: github.com/RagnarGrootKoerkamp/astar-pairwise-aligner.

Aligning distant sequences to graphs using long seed sketches.

Sequence-to-graph alignment is crucial for applications such as variant genotyping, read error correction, and genome assembly. We propose a novel seeding approach that relies on long inexact matches rather than short exact matches, and show that it yields a better time-accuracy trade-off in settings with up to a [Formula: see text] mutation rate. We use sketches of a subset of graph nodes, which are more robust to indels, and store them in a k-nearest neighbor index to avoid the curse of dimensionality. Our approach contrasts with existing methods and highlights the important role that sketching into vector space can play in bioinformatics applications. We show that our method scales to graphs with 1 billion nodes and has quasi-logarithmic query time for queries with an edit distance of [Formula: see text] For such queries, longer sketch-based seeds yield a [Formula: see text] increase in recall compared with exact seeds. Our approach can be incorporated into other aligners, providing a novel direction for sequence-to-graph alignment.