Locality-sensitive bucketing functions for the edit distance

Ke Chen, Mingfu Shao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Many bioinformatics applications involve bucketing a set of sequences where each sequence is allowed to be assigned into multiple buckets. To achieve both high sensitivity and precision, bucketing methods are desired to assign similar sequences into the same bucket while assigning dissimilar sequences into distinct buckets. Existing k-mer-based bucketing methods have been efficient in processing sequencing data with low error rates, but encounter much reduced sensitivity on data with high error rates. Locality-sensitive hashing (LSH) schemes are able to mitigate this issue through tolerating the edits in similar sequences, but state-of-the-art methods still have large gaps. Results: In this paper, we generalize the LSH function by allowing it to hash one sequence into multiple buckets. Formally, a bucketing function, which maps a sequence (of fixed length) into a subset of buckets, is defined to be (d1, d2) -sensitive if any two sequences within an edit distance of d1 are mapped into at least one shared bucket, and any two sequences with distance at least d2 are mapped into disjoint subsets of buckets. We construct locality-sensitive bucketing (LSB) functions with a variety of values of (d1, d2) and analyze their efficiency with respect to the total number of buckets needed as well as the number of buckets that a specific sequence is mapped to. We also prove lower bounds of these two parameters in different settings and show that some of our constructed LSB functions are optimal. Conclusion: These results lay the theoretical foundations for their practical use in analyzing sequences with high error rates while also providing insights for the hardness of designing ungapped LSH functions.

Original languageEnglish (US)
Article number7
JournalAlgorithms for Molecular Biology
Volume18
Issue number1
DOIs
StatePublished - Dec 2023

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Molecular Biology
  • Computational Theory and Mathematics
  • Applied Mathematics

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