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Sequence Similarity Estimation by Random Subsequence Sketching

Authors Ke Chen , Vinamratha Pattar , Mingfu Shao

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Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
  • Applied computing → Computational biology
Keywords
  • Alignment-free sequence comparison
  • Phylogenetic clustering
  • Nearest neighbor search
  • Edit distance embedding

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Abstract

Sequence similarity estimation is essential for many bioinformatics tasks, including functional annotation, phylogenetic analysis, and overlap graph construction. Alignment-free methods aim to solve large-scale sequence similarity estimation by mapping sequences to more easily comparable features that can approximate edit distances efficiently. Substrings or k-mers, as the dominant choice of features, face an unavoidable compromise between sensitivity and specificity when selecting the proper k-value. Recently, subsequence-based features have shown improved performance, but they are computationally demanding, and determining the ideal subsequence length remains an intricate art. In this work, we introduce SubseqSketch, a novel alignment-free scheme that maps a sequence to an integer vector, where the entries correspond to dynamic, rather than fixed, lengths of random subsequences. The cosine similarity between these vectors exhibits a strong correlation with the edit similarity between the original sequences. Through experiments on benchmark datasets, we demonstrate that SubseqSketch is both efficient and effective across various alignment-free tasks, including nearest neighbor search and phylogenetic clustering. A C++ implementation of SubseqSketch is openly available at https://github.com/Shao-Group/SubseqSketch.

Cite As Get BibTex

Ke Chen, Vinamratha Pattar, and Mingfu Shao. Sequence Similarity Estimation by Random Subsequence Sketching. In 25th International Conference on Algorithms for Bioinformatics (WABI 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 344, pp. 7:1-7:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.WABI.2025.7

Author Details

Ke Chen
  • Department of Computer Science and Engineering, School of Electronic Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA
Vinamratha Pattar
  • Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India
Mingfu Shao
  • Department of Computer Science and Engineering, School of Electronic Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA
  • Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA

Funding

This work is supported by the US National Science Foundation (DBI-2019797 to M.S.) and the US National Institutes of Health (R01HG011065 to M.S.).

Supplementary Materials

References

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