Efficient Reconciliation of Genomic Datasets of High Similarity

Authors Yoshihiro Shibuya , Djamal Belazzougui, Gregory Kucherov

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Author Details

Yoshihiro Shibuya
  • LIGM, Université 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, Université Gustave Eiffel, Marne-la-Vallée, France

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Yoshihiro Shibuya, Djamal Belazzougui, and Gregory Kucherov. Efficient Reconciliation of Genomic Datasets of High Similarity. In 22nd International Workshop on Algorithms in Bioinformatics (WABI 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 242, pp. 14:1-14:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


We apply Invertible Bloom Lookup Tables (IBLTs) to the comparison of k-mer sets originated from large DNA sequence datasets. We show that for similar datasets, IBLTs provide a more space-efficient and, at the same time, more accurate method for estimating Jaccard similarity of underlying k-mer sets, compared to MinHash which is a go-to sketching technique for efficient pairwise similarity estimation. This is achieved by combining IBLTs with k-mer sampling based on syncmers, which constitute a context-independent alternative to minimizers and provide an unbiased estimator of Jaccard similarity. A key property of our method is that involved data structures require space proportional to the difference of k-mer sets and are independent of the size of sets themselves. As another application, we show how our ideas can be applied in order to efficiently compute (an approximation of) k-mers that differ between two datasets, still using space only proportional to their number. We experimentally illustrate our results on both simulated and real data (SARS-CoV-2 and Streptococcus Pneumoniae genomes).

Subject Classification

ACM Subject Classification
  • Applied computing
  • k-mers
  • sketching
  • Invertible Bloom Lookup Tables
  • IBLT
  • MinHash
  • syncmers
  • minimizers


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