Identifying statistical dependence in genomic sequences via mutual information estimates

TL;DR

This paper introduces an information-theoretic method based on mutual information to identify and quantify statistical dependencies in genomic sequences, aiding in understanding genetic structures and variations.

Contribution

The paper presents a novel, precise methodology using mutual information for detecting dependencies in DNA and RNA sequences, with applications in gene analysis and genetic profiling.

Findings

Detected significant dependencies in maize gene regions.

Effectively identified short tandem repeats in DNA data.

Method proved reliable for biological sequence analysis.

Abstract

Questions of understanding and quantifying the representation and amount of information in organisms have become a central part of biological research, as they potentially hold the key to fundamental advances. In this paper, we demonstrate the use of information-theoretic tools for the task of identifying segments of biomolecules (DNA or RNA) that are statistically correlated. We develop a precise and reliable methodology, based on the notion of mutual information, for finding and extracting statistical as well as structural dependencies. A simple threshold function is defined, and its use in quantifying the level of significance of dependencies between biological segments is explored. These tools are used in two specific applications. First, for the identification of correlations between different parts of the maize zmSRp32 gene. There, we find significant dependencies between the 5' untranslated region in zmSRp32 and its alternatively spliced exons. This observation may indicate the presence of as-yet unknown alternative splicing mechanisms or structural scaffolds. Second, using data from the FBI's Combined DNA Index System (CODIS), we demonstrate that our approach is particularly well suited for the problem of discovering short tandem repeats, an application of importance in genetic profiling.