In the search for the low-complexity sequences in prokaryotic and eukaryotic genomes: how to derive a coherent picture from global and local entropy measures

TL;DR

This paper introduces the Non-Stationarity Entropic Index (NSEI) method to detect functional changes in DNA genomes by analyzing low-complexity sequences and base correlation properties without requiring training data.

Contribution

The study presents a novel, parameter-free technique (NSEI) for linking low-complexity sequences with nonstationary base correlations in genomes.

Findings

NSEI effectively detects functional changes in DNA.

Correlation found between low-complexity sequences and correlation ratios.

Method does not require training data or fitting parameters.

Abstract

We investigate on a possible way to connect the presence of Low-Complexity Sequences (LCS) in DNA genomes and the nonstationary properties of base correlations. Under the hypothesis that these variations signal a change in the DNA function, we use a new technique, called Non-Stationarity Entropic Index (NSEI) method, and we prove that this technique is an efficient way to detect functional changes with respect to a random baseline. The remarkable aspect is that NSEI does not imply any training data or fitting parameter, the only arbitrarity being the choice of a marker in the sequence. We make this choice on the basis of biological information about LCS distributions in genomes. We show that there exists a correlation between changing the amount in LCS and the ratio of long- to short-range correlation.