The colored longest common prefix array computed via sequential scans

TL;DR

This paper introduces the colored longest common prefix array (cLCP), a new data structure that enables efficient, alignment-free comparison of large biological sequence datasets through sequential scans in semi-external memory.

Contribution

The paper presents the cLCP data structure and demonstrates its ability to efficiently compute sequence similarities without extensive in-memory processing.

Findings

cLCP can be computed via sequential scans in semi-external memory

The approach effectively solves the multi-string Average Common Substring problem

Experimental results confirm the method's efficiency and practicality

Abstract

Due to the increased availability of large datasets of biological sequences, the tools for sequence comparison are now relying on efficient alignment-free approaches to a greater extent. Most of the alignment-free approaches require the computation of statistics of the sequences in the dataset. Such computations become impractical in internal memory when very large collections of long sequences are considered. In this paper, we present a new conceptual data structure, the colored longest common prefix array (cLCP), that allows to efficiently tackle several problems with an alignment-free approach. In fact, we show that such a data structure can be computed via sequential scans in semi-external memory. By using cLCP, we propose an efficient lightweight strategy to solve the multi-string Average Common Substring (ACS) problem, that consists in the pairwise comparison of a single string against a collection of $m$ strings simultaneously, in order to obtain $m$ ACS induced distances. Experimental results confirm the effectiveness of our approach.