Augmented Thresholds for MONI

TL;DR

This paper improves the efficiency of the MONI data structure for pattern matching in pangenomic datasets by reducing query times through a small modification, making it faster in practice with minimal size increase.

Contribution

It introduces augmented thresholds for MONI that significantly speed up pattern matching queries while maintaining a small space footprint.

Findings

Query times are reduced by avoiding most LCE queries.

The modified MONI remains space-efficient.

Practical speedup demonstrated in experiments.

Abstract

MONI (Rossi et al., 2022) can store a pangenomic dataset T in small space and later, given a pattern P, quickly find the maximal exact matches (MEMs) of P with respect to T. In this paper we consider its one-pass version (Boucher et al., 2021), whose query times are dominated in our experiments by longest common extension (LCE) queries. We show how a small modification lets us avoid most of these queries and thus significantly speeds up MONI in practice while only slightly increasing its size.