Rick Beeloo | Search PubMed

PubMed2026

Computational tool choice impacts CRISPR spacer-protospacer detection.

MOTIVATION: CRISPR spacer-protospacer matching is widely used to infer host-virus interactions in microbial and viromics studies, but the choice of sequence search or alignment tool and its reporting behavior is often under-evaluated for this specific task. RESULTS: Using synthetic, semi-synthetic, and real datasets, we benchmarked commonly used tools and observed substantial differences in recall, runtime, and resource usage across distance metrics and thresholds. Our analyses support practical defaults for large-scale spacer-target matching and clarify trade-offs between exhaustive and heuristic approaches. AVAILABILITY: Source code and benchmark workflows are available at https://github.com/UriNeri/spacer_matching_bench. Data and run artifacts are archived on Zenodo (https://doi.org/10.5281/zenodo.15171878).

Software↗

PubMed2026

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 O(k⌈n/W⌉) 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 ≤1000 bp, and can search text with a throughput near 2 Gbp/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 20 min. Sassy also scales well to larger k, unlike CHOPOFF whose index took over 10 h 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.

Sequence Analysis, DNA↗