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Space-Efficient Representation of Genomic k-Mer Count Tables

Authors Yoshihiro Shibuya, Djamal Belazzougui, Gregory Kucherov

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Author Details

Yoshihiro Shibuya
  • LIGM, CNRS, Univ. Gustave Eiffel, Marne-la-Vallée, France
Djamal Belazzougui
  • CAPA, DTISI, Centre de Recherche sur l'Information Scientifique et Technique, Algiers, Algeria
Gregory Kucherov
  • LIGM, CNRS, Univ. Gustave Eiffel, Marne-la-Vallée, France
  • Skolkovo Institute of Science and Technology, Moscow, Russia

Funding

  • Kucherov, Gregory: partially funded by RFBR project 20-07-00652 and joint RFBR and JSPS project 20-51-50007.

Cite As Get BibTex

Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov. Space-Efficient Representation of Genomic k-Mer Count Tables. In 21st International Workshop on Algorithms in Bioinformatics (WABI 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 201, pp. 8:1-8:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.WABI.2021.8

Abstract

Motivation. k-mer counting is a common task in bioinformatic pipelines, with many dedicated tools available. Output formats could rely on quotienting to reduce the space of k-mers in hash tables, however counts are not usually stored in space-efficient formats. Overall, k-mer count tables for genomic data take a considerable space, easily reaching tens of GB. Furthermore, such tables do not support efficient random-access queries in general.

Results. In this work, we design an efficient representation of k-mer count tables supporting fast random-access queries. We propose to apply Compressed Static Functions (CSFs), with space proportional to the empirical zero-order entropy of the counts. For very skewed distributions, like those of k-mer counts in whole genomes, the only currently available implementation of CSFs does not provide a compact enough representation. By adding a Bloom Filter to a CSF we obtain a Bloom-enhanced CSF (BCSF) effectively overcoming this limitation. Furthermore, by combining BCSFs with minimizer-based bucketing of k-mers, we build even smaller representations breaking the empirical entropy lower bound, for large enough k. We also extend these representations to the approximate case, gaining additional space. We experimentally validate these techniques on k-mer count tables of whole genomes (E.Coli and C.Elegans) as well as on k-mer document frequency tables for 29 E.Coli genomes. In the case of exact counts, our representation takes about a half of the space of the empirical entropy, for large enough k’s.

Subject Classification

ACM Subject Classification
  • Applied computing
Keywords
  • k-mer counting
  • data structures
  • compression
  • minimizers
  • compressed static function
  • Bloom filter
  • empirical entropy

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Supplementary Materials

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