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Updated: May 15, 2025

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Approximating edit distances between complex tandem repeats efficiently.

Riki Kawahara1, Shinichi Morishita1

  • 1Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.

Bioinformatics (Oxford, England)
|April 9, 2025
PubMed
Summary
This summary is machine-generated.

We developed an efficient algorithm to measure evolutionary distances between complex tandem repeats (TRs), crucial for understanding TR diversity and associated diseases. This method significantly speeds up analysis while maintaining high accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Extended tandem repeats (TRs) are linked to over 60 diseases, with complex TRs showing divergence and expansion between individuals.
  • Understanding the evolution of TR diversity requires measuring edit distances between complex TRs, accounting for unit duplication and contraction.
  • Existing algorithms for calculating edit distances in complex TRs are computationally intensive.

Purpose of the Study:

  • To propose an efficient heuristic algorithm for estimating edit distance with duplication and contraction of units (EDDC) in complex TRs.
  • To develop a software tool (hEDDC) for implementing this algorithm.
  • To enable faster and more accurate phylogenetic analysis of complex TRs.

Main Methods:

  • Selected frequent units within complex TRs and encoded them as symbols.
  • Compressed TRs into optimal series of unit symbols using Levenshtein distance.
  • Estimated EDDC between TR pairs from their compressed representations.

Main Results:

  • The heuristic algorithm achieves orders of magnitude performance speedup compared to traditional methods.
  • Estimated EDDC highly correlates with accurate EDDC (Pearson correlation coefficient > 0.983) on synthetic datasets.
  • The developed software, hEDDC, implements the proposed efficient algorithm.

Conclusions:

  • The proposed heuristic algorithm provides an efficient and accurate method for estimating EDDC in complex TRs.
  • This advancement facilitates the study of TR evolution and its association with diseases.
  • The hEDDC software is publicly available for researchers.