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Updated: Jul 9, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

RLBWT-based LCP computation in compressed space for terabase-scale pangenome analysis.

Ahsan Sanaullah¹, Nathaniel K Brown², Pramesh Shakya¹

¹Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States.

Bioinformatics (Oxford, England)

|July 7, 2026

Summary

This summary is machine-generated.

We developed faster algorithms for building compressed full-text indexes using the run-length Burrows-Wheeler transform (RLBWT). These new methods significantly reduce memory usage for large biological datasets, making complex sequence analysis more accessible.

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Published on: August 20, 2021

Area of Science:

Bioinformatics
Computational Biology
Data Science

Background:

Lossless full-text indexes are crucial for bioinformatics applications.
Increasing biological data necessitates efficient indexing of large datasets.
Existing run-length Burrows-Wheeler transform (RLBWT) indexes are computationally expensive to construct.

Purpose of the Study:

To present novel algorithms for constructing RLBWT-based compressed full-text indexes.
To develop supporting data structures that operate in compressed space.
To reduce the computational cost and memory requirements of building these indexes.

Main Methods:

Developed algorithms with O(r) space complexity and O(n) time for repetitive datasets.
Introduced the first algorithm for computing LCP-related information in O(r) space and optimal time.
Utilized r samples of the inverse suffix array at regular intervals.

Main Results:

Achieved O(r) space complexity and O(n) time for repetitive dataset index construction.
Significantly reduced peak memory usage, e.g., from 2135 GiB to 170 GiB for the Human Pangenome Reference Consortium Release 2 dataset.
Enabled efficient computation of LCP-related information, reducing memory requirements substantially.

Conclusions:

The new algorithms offer a computationally efficient and memory-saving approach to building compressed full-text indexes.
These advancements are critical for handling the growing scale of biological data.
The implementation is publicly available, facilitating broader adoption in bioinformatics research.