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Related Experiment Video

Updated: Jul 22, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Locality-sensitive bucketing functions for the edit distance.

Ke Chen1, Mingfu Shao2,3

  • 1Department of Computer Science and Engineering, The Pennsylvania State University, State College, United States.

Algorithms for Molecular Biology : AMB
|July 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces locality-sensitive bucketing (LSB) to improve sequence analysis in bioinformatics, especially for data with high error rates. LSB functions efficiently group similar sequences while separating dissimilar ones, overcoming limitations of existing methods.

Keywords:
EmbeddingLocality-sensitive bucketingLocality-sensitive hashingLong reads

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Sequence Analysis

Background:

  • Bioinformatics applications often require assigning sequences to multiple buckets.
  • Existing k-mer methods struggle with high-error-rate data, while Locality-Sensitive Hashing (LSH) has limitations.
  • There's a need for sensitive and precise bucketing methods for error-prone sequences.

Purpose of the Study:

  • To generalize Locality-Sensitive Hashing (LSH) for improved sequence bucketing.
  • To develop new bucketing functions that are sensitive to edit distances.
  • To analyze the theoretical efficiency and optimality of these new functions.

Main Methods:

  • Generalization of LSH functions to map sequences into multiple buckets.
  • Definition of locality-sensitive bucketing ([Formula: see text])-sensitive functions.
  • Construction and analysis of Locality-Sensitive Bucketing (LSB) functions for various [Formula: see text] values.

Main Results:

  • Construction of LSB functions that hash sequences into multiple buckets.
  • Analysis of LSB function efficiency regarding the number of buckets used.
  • Proof of lower bounds for bucketing parameters, demonstrating optimality for some LSB functions.

Conclusions:

  • LSB functions provide a theoretical foundation for analyzing error-prone sequences.
  • The study offers insights into the challenges of designing ungapped LSH functions.
  • LSB methods enhance sensitivity and precision in sequence bucketing for bioinformatics.