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KeBaB: $k$-mer based breaking for finding long MEMs

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arxiv 2502.20338 v3 pith:VIVIXIRE submitted 2025-02-27 cs.DS

Nathaniel K. Brown , Lore Depuydt , Mohsen Zakeri , Anas Alhadi , Nour Allam , Dove Begleiter , Nithin Bharathi Kabilan Karpagavalli , Suchith Sridhar Khajjayam
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Hamza Wahed Travis Gagie Ben Langmead
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classification cs.DS
keywords longmemsclassificationevenignoreropebwt3accelerateaccuracy
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abstract

Long maximal exact matches (MEMs) are used in many genomics applications such as read classification and sequence alignment. Li's ropebwt3 finds long MEMs quickly because it can often ignore much of its input. In this paper we show that a fast and space efficient $k$-mer filtration step using a Bloom filter speeds up MEM-finders such as ropebwt3 even further by letting them ignore even more. We also show experimentally that our approach can accelerate metagenomic classification without significantly hurting accuracy.

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