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r-indexing the eBWT.

Christina Boucher¹, Davide Cenzato², Zsuzsanna Lipták²

¹Dept of Computer and Information Science and Engineering, University of Florida.

International Symposium on String Processing and Information Retrieval : SPIRE ... : Proceedings. SPIRE (Symposium)

|May 14, 2024

Summary

This summary is machine-generated.

This study presents an efficient r-indexing method for the extended Burrows Wheeler Transform (eBWT) using Prefix Free Parsing. This approach enables faster construction of compressed eBWT representations and efficient searching for maximal exact matches (MEMs).

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Area of Science:

Bioinformatics
String Algorithms
Data Compression

Background:

The extended Burrows Wheeler Transform (eBWT) generalizes the Burrows Wheeler Transform to collections of strings.
Prior work established a linear-time algorithm for eBWT construction, preserving input order independence.
This algorithm integrates the Suffix Array Induced Sorting (SAIS) and Prefix Free Parsing (PFP) techniques.

Purpose of the Study:

To demonstrate how the eBWT construction algorithm facilitates efficient r-indexing.
To show the construction of run-length encoded eBWT and sampled Suffix Arrays (SA) from PFP components.
To establish efficient support for finding maximal exact matches (MEMs) using the r-indexed eBWT.

Main Methods:

Leveraging a linear-time eBWT construction algorithm based on SAIS and PFP.
Utilizing components of the PFP for constructing the r-indexed eBWT.
Applying the r-indexed eBWT structure for maximal exact match (MEM) queries.

Main Results:

Efficient construction of the r-indexed eBWT from PFP components.
Demonstration of efficient computation of run-length encoded eBWT and SA samples.
Efficient support for finding maximal exact matches (MEMs) against the r-indexed eBWT.

Conclusions:

The proposed method provides an efficient approach to r-indexing the eBWT.
This enables practical applications in sequence analysis and data compression.
The framework efficiently supports critical string matching operations like MEM identification.