Efficient Seeding for Error-Prone Sequences with SubseqHash2

Abstract

Seeding is an essential preparatory step for large-scale sequence comparisons. Substring-based seeding methods such as kmers are ideal for sequences with low error rates but struggle to achieve high sensitivity while maintaining a reasonable precision for error-prone long reads. SubseqHash, a novel subsequence-based seeding method we recently developed, achieves superior accuracy to substring-based methods in seeding sequences with high mutation/error rates, while the only drawback is its computation speed. In this paper, we propose SubseqHash2, an improved algorithm that can compute multiple sets of seeds in one run by defining k orders over all length-k subsequences and identifying the optimal subsequence under each of the k orders in a single dynamic programming framework. The algorithm is further accelerated using SIMD instructions. SubseqHash2 achieves a 10-50x speedup over repeating SubseqHash while maintaining the high accuracy of seeds. We demonstrate that SubseqHash2 drastically outperforms popular substring-based methods including kmers, minimizers, syncmers, and Strobemers for three fundamental applications. In read mapping, SubseqHash2 can generate adequate seed-matches for aligning hard reads that minimap2 fails on. In sequence alignment, SubseqHash2 achieves high coverage of correct seeds and low coverage of incorrect seeds. In overlap detection, seeds produced by SubseqHash2 lead to more correct overlapping pairs at the same false-positive rate. With all the algorithmic breakthroughs of SubseqHash2, we clear the path for the wide adoption of subsequence-based seeds in long-read analysis. SubseqHash2 is available at https://github.com/Shao-Group/SubseqHash2.