Sequence-to-graph alignment is an important step in applications such as variant genotyping, read error correction and genome assembly. When a query sequence requires a substantial number of edits to align, approximate alignment tools that follow the seed-and-extend approach require shorter seeds to get any matches. However, in large graphs with high variation, relying on a shorter seed length leads to an exponential increase in spurious matches. We propose a novel seeding approach relying on long inexact matches instead of short exact matches. We demonstrate experimentally that our approach achieves a better time-accuracy trade-off in settings with up to a 25% mutation rate. We achieve this by sketching a subset of graph nodes and storing them in a K-nearest neighbor index. While sketches are more robust to indels, finding the nearest neighbor of a sketch in a high-dimensional space is more computationally challenging than finding exact seeds. We demonstrate that if we store sketch vectors in a K-nearest neighbor index, we can circumvent the curse of dimensionality. Our long sketch-based seed scheme contrasts existing approaches and highlights the important role that tensor sketching can play in bioinformatics applications. Our proposed seeding method and implementation have several advantages: i) We empirically show that our method is efficient and scales to graphs with 1 billion nodes, with time and memory requirements for preprocessing growing linearly with graph size and query time growing quasi-logarithmically with query length. ii) For queries with an edit distance of 25% relative to their length, on the 1 billion node graph, longer sketch-based seeds yield a 4x increase in recall compared to exact seeds. iii) Conceptually, our seeder can be incorporated into other aligners, proposing a novel direction for sequence-to-graph alignment. The implementation is available at: https://github.com/ratschlab/tensor-sketch-alignment.

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 demonstrate that it yields a better time-accuracy trade-off in settings with up to a 25% mutation rate. We use sketches of a subset of graph nodes, which are more robust to indels, and store them in ak-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 25%. For such queries, longer sketch-based seeds yield a 4× increase in recall compared to exact seeds. Our approach can be incorporated into other aligners, providing a novel direction for sequence-to-graph alignment.