PHONI: Streamed Matching Statistics with Multi-Genome References

TL;DR

This paper introduces PHONI, a streaming algorithm for computing matching statistics against multi-genome references, enabling efficient, online analysis of large genomic patterns with low latency and manageable memory use.

Contribution

It simplifies and extends previous solutions to enable streaming computation of matching statistics for large genomic databases, supporting parallel and online processing.

Findings

Supports parallel processing of long patterns like entire human chromosomes

Enables online computation with low latency

Uses reasonable RAM despite large data

Abstract

Computing the matching statistics of patterns with respect to a text is a fundamental task in bioinformatics, but a formidable one when the text is a highly compressed genomic database. Bannai et al. gave an efficient solution for this case, which Rossi et al. recently implemented, but it uses two passes over the patterns and buffers a pointer for each character during the first pass. In this paper, we simplify their solution and make it streaming, at the cost of slowing it down slightly. This means that, first, we can compute the matching statistics of several long patterns (such as whole human chromosomes) in parallel while still using a reasonable amount of RAM; second, we can compute matching statistics online with low latency and thus quickly recognize when a pattern becomes incompressible relative to the database.