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

TL;DR

This paper introduces KeBaB, a $k$-mer based filtering method using Bloom filters that enhances the speed of long MEM detection in genomics, improving efficiency in applications like metagenomic classification.

Contribution

The paper presents a novel $k$-mer filtration step with Bloom filters that significantly accelerates MEM-finding tools like ropebwt3 while maintaining accuracy.

Findings

Accelerates MEM detection in genomics applications

Reduces computational resources needed for sequence analysis

Maintains high accuracy in metagenomic classification

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.