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sBWT: memory efficient implementation of the hardware-acceleration-friendly Schindler transform for the fast

Chia-Hua Chang^1,2, Min-Te Chou¹, Yi-Chung Wu³

¹Institute of Bioinformatics and Systems Biology.

Bioinformatics (Oxford, England)

|July 15, 2016

Summary

We introduce sBWT, a hardware-accelerated indexing method for fast string matching. sBWT offers significant speedups for large genomes and repetitive sequences compared to existing FM-index tools.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

The FM-index, derived from the Burrows-Wheeler transform (BWT), is crucial for rapid string matching in large genomic datasets.
Existing GPU-accelerated FM-index aligners face limitations in index construction, often relying on CPUs or lacking scalability for massive genomes.

Purpose of the Study:

To develop a practical, hardware-parallelizable indexing and matching approach for large-scale biological data.
To improve the efficiency and scalability of string matching algorithms in bioinformatics.

Main Methods:

Propose sBWT, a novel approach based on a BWT variant (Schindler transform).
Design hardware-acceleration-friendly algorithms for simplified index construction.
Implement both CPU-only and GPU-accelerated versions of sBWT in C++.

Main Results:

sBWT achieves significant speedups in both indexing and searching compared to existing BWT-based tools.
The method demonstrates accuracy and speed suitable for repetitive reference sequences.
Successfully applied sBWT across various bioinformatics domains.

Conclusions:

sBWT provides a scalable and efficient solution for FM-index construction and string matching.
The GPU-accelerated implementation offers substantial performance gains for large-scale genomic analyses.
sBWT is an open-source tool available for broader research community adoption.