Information Measures for Long-Range Correlated Sequences: the Case of the 24 Human Chromosome Sequences

TL;DR

This paper introduces a novel method to estimate Shannon entropy in long-range correlated sequences, applied to human chromosomes, revealing that ordered clusters contain most of the sequence's information and disordered clusters relate to biological features.

Contribution

A new entropy estimation approach that separates ordered and disordered components, applied to human chromosomes, linking sequence structure to biological properties.

Findings

Ordered clusters have similar nucleotide composition to entire sequences.

Disordered clusters show fluctuating nucleotide composition.

Fluctuations in disordered clusters relate to biological features like gene density.

Abstract

A new approach to estimate the Shannon entropy of a long-range correlated sequence is proposed. The entropy is written as the sum of two terms corresponding respectively to power-law (\emph{ordered}) and exponentially (\emph{disordered}) distributed blocks (clusters). The approach is illustrated on the 24 human chromosome sequences by taking the nucleotide composition as the relevant information to be encoded/decoded. Interestingly, the nucleotide composition of the \emph{ordered} clusters is found, on the average, comparable to the one of the whole analyzed sequence, while that of the \emph{disordered} clusters fluctuates. From the information theory standpoint, this means that the power-law correlated clusters carry the same information of the whole analysed sequence. Furthermore, the fluctuations of the nucleotide composition of the disordered clusters are linked to relevant biological properties, such as segmental duplications and gene density.