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Computing the original eBWT faster, simpler, and with less memory.

Christina Boucher¹, Davide Cenzato², Zsuzsanna Lipták²

¹Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States.

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Summary

This summary is machine-generated.

This study introduces a new linear-time algorithm for the extended Burrows-Wheeler Transform (eBWT), crucial for genomic sequence analysis. The novel method, pfpebwt, significantly speeds up the construction of eBWT for large genomic collections, improving efficiency and memory usage.

Keywords:

Data compression Data structures design and analysis Pattern matching SAIS algorithm Theory of computation extended BWT omega-order prefix-free parsing

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Area of Science:

Bioinformatics
Computational Biology
String Algorithms

Background:

The Burrows-Wheeler Transform (BWT) is vital for genomic data analysis, enabling compression and substring queries.
The extended Burrows-Wheeler Transform (eBWT) was defined to handle collections of strings, maintaining order independence.
Existing eBWT methods often disregard the original order-independent property, necessitating improved algorithms.

Purpose of the Study:

To present a novel, linear-time algorithm for constructing the original extended Burrows-Wheeler Transform (eBWT).
To develop an efficient method for computing the BWT of a single string without special symbols or Lyndon rotations.
To enable the construction of eBWT on large genomic sequence collections by combining the new eBWT algorithm with prefix-free parsing (PFP).

Main Methods:

A new linear-time algorithm for the original eBWT construction, avoiding preprocessing steps.
A linear-time algorithm for single-string BWT computation, omitting end-of-string symbols and Lyndon rotations.
Integration of the eBWT algorithm with a prefix-free parsing (PFP) variation for large-scale genomic data.

Main Results:

The developed algorithm (pfpebwt) achieves the fastest construction times for eBWT on large genomic collections, with speedups up to 7.6x.
Peak memory usage of pfpebwt is at most 2x that of the second-best method.
Compared to methods reporting suffix array samples, pfpebwt offers a 57.1x improvement in peak memory.

Conclusions:

The new eBWT construction algorithm is efficient and practical for analyzing large genomic datasets.
pfpebwt provides a significant advancement in speed and memory efficiency for genomic sequence analysis tasks.
The publicly available source code facilitates further research and application of these improved BWT algorithms.