Fractional Hitting Sets for Efficient and Lightweight Genomic Data Sketching

Authors Timothé Rouzé , Igor Martayan , Camille Marchet , Antoine Limasset

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Timothé Rouzé
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
Igor Martayan
  • ENS Rennes, Univ. Rennes, France
Camille Marchet
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
Antoine Limasset
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France

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Timothé Rouzé, Igor Martayan, Camille Marchet, and Antoine Limasset. Fractional Hitting Sets for Efficient and Lightweight Genomic Data Sketching. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 15:1-15:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


The exponential increase in publicly available sequencing data and genomic resources necessitates the development of highly efficient methods for data processing and analysis. Locality-sensitive hashing techniques have successfully transformed large datasets into smaller, more manageable sketches while maintaining comparability using metrics such as Jaccard and containment indices. However, fixed-size sketches encounter difficulties when applied to divergent datasets. Scalable sketching methods, such as Sourmash, provide valuable solutions but still lack resource-efficient, tailored indexing. Our objective is to create lighter sketches with comparable results while enhancing efficiency. We introduce the concept of Fractional Hitting Sets, a generalization of Universal Hitting Sets, which uniformly cover a specified fraction of the k-mer space. In theory and practice, we demonstrate the feasibility of achieving such coverage with simple but highly efficient schemes. By encoding the covered k-mers as super-k-mers, we provide a space-efficient exact representation that also enables optimized comparisons. Our novel tool, SuperSampler, implements this scheme, and experimental results with real bacterial collections closely match our theoretical findings. In comparison to Sourmash, SuperSampler achieves similar outcomes while utilizing an order of magnitude less space and memory and operating several times faster. This highlights the potential of our approach in addressing the challenges presented by the ever-expanding landscape of genomic data.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • k-mer
  • subsampling
  • sketching
  • Jaccard
  • containment
  • metagenomics


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