FM-Indexing Grammars Induced by Suffix Sorting for Long Patterns

TL;DR

This paper introduces a hybrid indexing method combining grammar-based compression with the run-length compressed Burrows-Wheeler transform, improving index size and query efficiency for long patterns in biological data.

Contribution

It presents a novel hybrid index that integrates grammar induction via suffix sorting with RLBWT, enhancing performance over traditional methods.

Findings

Outperforms classic RLBWT in index size

Faster query times for long patterns on biological data

Effective on highly-repetitive datasets

Abstract

The run-length compressed Burrows-Wheeler transform (RLBWT) used in conjunction with the backward search introduced in the FM index is the centerpiece of most compressed indexes working on highly-repetitive data sets like biological sequences. Compared to grammar indexes, the size of the RLBWT is often much bigger, but queries like counting the occurrences of long patterns can be done much faster than on any existing grammar index so far. In this paper, we combine the virtues of a grammar with the RLBWT by building the RLBWT on top of a special grammar based on induced suffix sorting. Our experiments reveal that our hybrid approach outperforms the classic RLBWT with respect to the index sizes, and with respect to query times on biological data sets for sufficiently long patterns.