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Nathaniel K Brown1, Vikram S Shivakumar1, Ben Langmead1

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Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 5, 2025
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Summary

We introduce the col-BWT, a novel data structure that efficiently identifies collinear matches in reference sequences. This method improves read classification accuracy compared to existing compressed indexing techniques.

Keywords:
pangenomicssequence classificationtext indexing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional chaining methods for identifying collinear matches are computationally expensive, requiring superlinear time.
  • Existing compressed full-text indexes capture only fine-grained collinearity, missing coarse-grained information crucial for applications like pangenome-based read classification.
  • Pangenome analysis requires efficient handling of a high multiplicity of matches, which current methods struggle with.

Purpose of the Study:

  • To develop a novel data structure, the col-BWT (collinear Burrows-Wheeler Transform), for efficient computation of both fine-grained and coarse-grained collinearity statistics.
  • To enable accurate and fast read classification against large reference collections, such as pangenomes.
  • To overcome the limitations of existing compressed indexing methods in capturing coarse-grained collinearity.

Main Methods:

  • The col-BWT is constructed directly from a collection of strings, bypassing the need for multiple sequence alignment.
  • It identifies multi-maximal unique matches (multi-MUMs) and BWT sub-runs corresponding to these matches.
  • The data structure efficiently marks 'tunneled' sub-runs with their multi-MUM identifiers, enabling recognition of collinearity.

Main Results:

  • The col-BWT provides an O(r + n/d)-space index for d sequences with an n-length BWT and r runs.
  • It computes both fine-grained and coarse-grained chain statistics in linear time with respect to query length.
  • Classification accuracy using col-BWT significantly surpasses previous compressed-indexing approaches and matches alignment-based methods.

Conclusions:

  • The col-BWT is a highly efficient data structure for identifying collinear matches, offering substantial improvements in read classification.
  • It effectively captures coarse-grained collinearity, a critical feature for large-scale genomic analyses.
  • This approach provides a faster and equally accurate alternative to traditional chaining and alignment-based methods.