An Efficient Data Structure and Algorithm for Long-Match Query in Run-Length Compressed BWT
Ahsan Sanaullah, Degui Zhi, Shaojie Zhang
TL;DR
This work tackles the problem of extracting long, informative substring matches between a pattern and a highly repetitive text by introducing long locally maximal exact matches (LEMs) and presenting an index- and algorithmic framework tailored to BWT runs (r). The authors extend the OptBWTR index to OptBWTRL by enabling constant-time PLCP and φ computations via move data structures, maintaining $O(r)$ space, and enabling $O(m+occ)$-time output of all long LEMs given matching statistics, where $m$ is the pattern length and $occ$ the number of long LEMs. A detailed method combines balanced sa_{lcp}-interval extension and long sa_{lcp}-interval advancement, using a dynamic dictionary to output matches with provable expected time bounds and a deterministic variant. The approach yields a linear-time, space-efficient mechanism for identifying long, non-maximal but highly informative pattern–text matches, with significant potential impact for pangenome and haplotype analyses, read-to-pangenome mapping, and seed-based genome alignment in massive datasets. Overall, OptBWTRL enables scalable long LEM querying in compressed space, facilitating deeper similarity insights beyond MEMs/SMEMs in biomedically relevant, ultra-large genomic collections.
Abstract
In this paper, we describe a new type of match between a pattern and a text that aren't necessarily maximal in the query, but still contain useful matching information: locally maximal exact matches (LEMs). There are usually a large amount of LEMs, so we only consider those above some length threshold $\mathcal{L}$. These are referred to as long LEMs. The purpose of long LEMs is to capture substring matches between a query and a text that are not necessarily maximal in the pattern but still long enough to be important. Therefore efficient long LEMs finding algorithms are desired for these datasets. However, these datasets are too large to query on traditional string indexes. Fortunately, these datasets are very repetitive. Recently, compressed string indexes that take advantage of the redundancy in the data but retain efficient querying capability have been proposed as a solution. We therefore give an efficient algorithm for computing all the long LEMs of a query and a text in a BWT runs compressed string index. We describe an $O(m+occ)$ expected time algorithm that relies on an $O(r)$ words space string index for outputting all long LEMs of a pattern with respect to a text given the matching statistics of the pattern with respect to the text. Here $m$ is the length of the query, $occ$ is the number of long LEMs outputted, and $r$ is the number of runs in the BWT of the text. The $O(r)$ space string index we describe relies on an adaptation of the move data structure by Nishimoto and Tabei. We are able to support $LCP[i]$ queries in constant time given $SA[i]$. In other words, we answer $PLCP[i]$ queries in constant time. Long LEMs may provide useful similarity information between a pattern and a text that MEMs may ignore. This information is particularly useful in pangenome and biobank scale haplotype panel contexts.