Optimizing sparse and skew hashing: faster k-mer dictionaries.

The refined sparse and skew hashing (SSHash) data structure accelerates k-mer dictionary index construction and query efficiency while preserving SSHash’s space effectiveness.

• Why it matters: Fast, small-space k-mer set representations with streaming lookup and order-preserving hash codes are needed for bioinformatics applications, but existing SSHash lookups can be complex and cache-inefficient.
• What they did: The authors redesign SSHash with less complex lookups and fewer cache misses, and provide a simpler, faster streaming lookup algorithm, then benchmark against SSHash and Burrows-Wheeler–based indexes like SBWT, SBWT variants, and FMSI.
• The result: The refined architecture yields substantial performance gains, outperforming the original SSHash in index construction speed and query efficiency, and is significantly faster to build and query than SBWT-based indexes; it is more than one order of magnitude faster to query than FMSI.

AI summary of the abstract · may contain errors · not medical advice · read the paper.