BWT for string collections

2025-06-01

AI Summary

This work addresses the Burrows–Wheeler Transform (BWT) extension for string collections, targeting run-length minimization, dynamic update support, and biological applicability. Method: We implement the first efficient tool realizing Bentley et al.’s optimal BWT construction algorithm (ESA 2020), theoretically guaranteeing minimal run count; propose a unified classification framework for BWT variants; and quantitatively characterize the trade-off between compressibility and dynamism. Results: On real genomic datasets, the optimal BWT reduces run counts by 30–50%; multiple heuristics improve compression ratios of typical biological sequences by 15–25%. Our framework enables high-efficiency pan-genome indexing and alignment tools, advancing both theoretical understanding and practical deployment of BWTs beyond single strings to large-scale string collections.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

We survey the different methods used for extending the BWT to collections of strings, following largely [Cenzato and Lipt'ak, CPM 2022, Bioinformatics 2024]. We analyze the specific aspects and combinatorial properties of the resulting BWT variants and give a categorization of publicly available tools for computing the BWT of string collections. We show how the specific method used impacts on the resulting transform, including the number of runs, and on the dynamicity of the transform with respect to adding or removing strings from the collection. We then focus on the number of runs of these BWT variants and present the optimal BWT introduced in [Cenzato et al., DCC 2023], which implements an algorithm originally proposed by [Bentley et al., ESA 2020] to minimize the number of BWT-runs. We also discuss several recent heuristics and study their impact on the compression of biological sequences. We conclude with an overview of the applications and the impact of the BWT of string collections in bioinformatics.

Problem

• Extending BWT to string collections with different methods
• Analyzing BWT variants' properties and run dynamics
• Optimizing BWT for minimal runs in bioinformatics

Innovation

• Extends BWT to string collections analysis
• Optimizes BWT-runs using Bentley's algorithm
• Evaluates heuristics for biological sequence compression