Taxonomic Classification with Maximal Exact Matches in KATKA Kernels and Minimizer Digests

Authors Dominika Draesslerová , Omar Ahmed , Travis Gagie , Jan Holub , Ben Langmead , Giovanni Manzini , Gonzalo Navarro

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Author Details

Dominika Draesslerová
  • Czech Technical University in Prague, Czech Republic
Omar Ahmed
  • Johns Hopkins University, Baltimore, MD, USA
Travis Gagie
  • CeBiB & Dalhousie University, Halifax, Canada
Jan Holub
  • Czech Technical University in Prague, Czech Republic
Ben Langmead
  • Johns Hopkins University, Baltimore, MD, USA
Giovanni Manzini
  • University of Pisa, Italy
Gonzalo Navarro
  • CeBiB & DCC, University of Chile, Chile


Many thanks to Sana Kashgouli and Finlay Maguire for helpful discussions.

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Dominika Draesslerová, Omar Ahmed, Travis Gagie, Jan Holub, Ben Langmead, Giovanni Manzini, and Gonzalo Navarro. Taxonomic Classification with Maximal Exact Matches in KATKA Kernels and Minimizer Digests. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 10:1-10:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


For taxonomic classification, we are asked to index the genomes in a phylogenetic tree such that later, given a DNA read, we can quickly choose a small subtree likely to contain the genome from which that read was drawn. Although popular classifiers such as Kraken use k-mers, recent research indicates that using maximal exact matches (MEMs) can lead to better classifications. For example, we can - build an augmented FM-index over the the genomes in the tree concatenated in left-to-right order; - for each MEM in a read, find the interval in the suffix array containing the starting positions of that MEM’s occurrences in those genomes; - find the minimum and maximum values stored in that interval; - take the lowest common ancestor (LCA) of the genomes containing the characters at those positions. This solution is practical, however, only when the total size of the genomes in the tree is fairly small. In this paper we consider applying the same solution to three lossily compressed representations of the genomes' concatenation: - a KATKA kernel, which discards characters that are not in the first or last occurrence of any k_max-tuple, for a parameter k_max; - a minimizer digest; - a KATKA kernel of a minimizer digest. With a test dataset and these three representations of it, simulated reads and various parameter settings, we checked how many reads' longest MEMs occurred only in the sequences from which those reads were generated ("true positive" reads). For some parameter settings we achieved significant compression while only slightly decreasing the true-positive rate.

Subject Classification

ACM Subject Classification
  • Theory of computation → Pattern matching
  • Taxonomic classification
  • metagenomics
  • maximal exact matches
  • string kernels
  • minimizer digests


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