A new distance based on minimal absent words and applications to biological sequences

TL;DR

This paper introduces a novel distance measure based on minimal absent words for comparing biological sequences, demonstrating its effectiveness through experiments on genetic data from 11 species.

Contribution

It proposes a new distance metric based on a specific subset of minimal absent words, improving sequence comparison methods.

Findings

The new distance captures sequence features more effectively.

Experimental results show better differentiation among species.

The method outperforms existing minimal absent word-based distances.

Abstract

A minimal absent word of a sequence x, is a sequence yt hat is not a factorof x, but all of its proper factors are factors of x as well. The set of minimal absent words uniquely defines the sequence itself. In recent times minimal absent words have been used in order to compare sequences. In fact, to do this, one can compare the sets of their minimal absent words. Chairungasee and Crochemorein [2] define a distance between pairs of sequences x and y, where the symmetric difference of the sets of minimal absent words of x and y is involved. Here, weconsider a different distance, introduced in [1], based on a specific subset of such symmetric difference that, in our opinion, better capture the different features ofthe considered sequences. We show the result of some experiments where the distance is tested on a dataset of genetic sequences by 11 living species, in order to compare the new distance with the ones existing in literature.