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Tickling, a Technique for Inducing Positive Affect When Handling Rats

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RLBWT Tricks.

Nathaniel K Brown¹, Travis Gagie¹, Massimiliano Rossi²

¹Faculty of Computer Science, Dalhousie University, NS, Canada.

Lipics : Leibniz International Proceedings in Informatics

|August 19, 2024

Summary

This summary is machine-generated.

Researchers developed a faster backward-stepping method for run-length compressed Burrows-Wheeler Transforms (RLBWT). This new approach offers competitive time and space efficiency, especially when applied to genomic datasets after compression.

Keywords:

Burrows-Wheeler Transform Compressed String Indexes Repetitive Text Collections Theory of computation → Data compression

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Area of Science:

Data Structures and Algorithms
Bioinformatics
Computational Biology

Background:

Constant-time backward-stepping in run-length compressed Burrows-Wheeler Transforms (RLBWT) was previously considered infeasible due to reliance on rank queries on sparse bitvectors.
Nishimoto and Tabei (ICALP '21) introduced a novel RLBWT implementation using an O(n)-space table for constant-time backward-stepping.

Purpose of the Study:

To refine and experimentally evaluate Nishimoto and Tabei's constant-time backward-stepping approach for RLBWT.
To investigate time-space tradeoffs and compression strategies for practical application.
To assess the performance of the improved RLBWT on real-world genomic data.

Main Methods:

Implementation and experimental evaluation of a refined RLBWT data structure.
Development of a decomposition scheme for the permutation corresponding to the LF-mapping.
Testing on large-scale genomic datasets to compare performance against existing methods.

Main Results:

Experimental results demonstrate that even without table row augmentation, backward-stepping queries are practically fast, scanning few entries.
The proposed decomposition scheme improves data structure compression while maintaining query time.
Compressed RLBWT backward-stepping achieves competitive time and space complexity compared to state-of-the-art implementations on genomic data.

Conclusions:

The refined RLBWT approach offers a practical and efficient solution for constant-time backward-stepping.
Compression is key to balancing the time and space performance of this method for real-world applications.
This work advances the utility of RLBWT in bioinformatics and large-scale data processing.