Sassy: fuzzy searching DNA sequences using SIMD

Motivation: Approximate string matching (ASM) is the problem of finding all occurrences of a pattern in a text while allowing up to k errors. Many modern methods use seed-chain-extend, which is fast in practice, but does not guarantee finding all matches with ≤k errors. However, applications such as CRISPR off-target detection require exhaustive results. Results: We introduce Sassy, a library and tool for ASM of short patterns in long texts. Sassy splits the text into four parts that are searched in parallel, and uses bitvectors in the text direction rather than the pattern direction. This has complexity Oðkdn=WeÞ when searching a random text of length n, where W=256 is the SIMD width, and provides significant speedups for small k. Separately, we allow matches of the pattern to extend beyond the text for an overhang cost of, e.g. α=0:5 per character, to find matches near contig or read ends. Sassy is 4× to 15× faster than Edlib for patterns ≤1000bp, and can search text with a throughput near 2Gbp/s. Likewise, Sassy is over 100× faster than parasail. We apply Sassy to CRISPR off-target detection by searching 61 guide sequences in a human genome. Sassy is 100× faster than SWOffinder and only slightly slower (for k≤3) than CHOPOFF, for which building its index takes 20min. Sassy also scales well to larger k, unlike CHOPOFF whose index took over 10h to build for k=5. Availability and implementation: Sassy is available as library and binary at https://github.com/RagnarGrootKoerkamp/sassy, and archived at swh:1:dir:e884758dce5777a441bc2799dc8824e563c5f97b.