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Sassy is out now! Ever need to search for approximate matches of short DNA strings? Sassy is the tool to use! Available now wherever you get your code With @rickbitloo.bsky.social curiouscoding.nl/papers/sassy... github.com/ragnarGrootK...

First off, the problem definition :) Sassy solves approximate string matching (ASM): finding all occurrences of a pattern in a text, with up to k errors. As you would expect from me, Sassy uses unit-cost edit distance.

The one long-lasting takeaway from this work: Searching a pattern P in text T and searching rev(P) in rev(T) give different results! Saying 'we find all matches' is kinda undefined, unless you specifically list all *intervals* of the text that align with cost <= k. But nobody does that.

Speaking of Edlib: like many other modern tools, it solves 'semi-global alignment', which finds a (set of) alignments of the pattern to a substring of the text *of optimal cost*. Note that this can be much less than the set of matches of cost ≤k.

Algorithmically, the first main feature of Sassy is to tile the bitvectors horizontally, in the text direction. This way, we don't waste as much compute when only the top rows of the matrix are needed. In particular, usually the top 2*k rows are sufficient to get DP entries >k: O(k*ceil[n/w]).

On top of that, we use SIMD to process 4 chunks of each (long) input text in parallel, similar to simd-minimizers. Final complexity on random text: O(k*ceil[n/W]).