Entropy-based Kullback-Leibler taxonomic classification of biological sequences

Abstract

Accurate classification of biological sequences is fundamental for understanding their functional, structural, and evolutionary significance. Traditional alignment-based methods often face challenges when applied to large, highly diverse datasets, especially when sequences have low identity or are distantly related. Alignment-free methods, an established category in computational biology, have emerged as powerful alternatives to traditional alignment approaches, offering solutions for these challenges.