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, they are used 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.
Highlights
Questions of quantification, representation, and description of the overall flow of information in biosystems are of central importance in the life sciences
First we show that it can be used effectively to identify statistical dependence between regions of the maize zmSRp32 gene that may be involved in alternative processing of pre-mRNA transcripts
We present experimental results on DNA sequences from the FBI’s combined DNA index system (CODIS), which clearly indicate that the empirical mutual information can be a powerful tool for this computationally intensive task
Summary
Representation, and description of the overall flow of information in biosystems are of central importance in the life sciences. We develop statistical tools based on information-theoretic ideas, and demonstrate their use in identifying informative parts in biomolecules. Our goal is to detect statistically dependent segments of biosequences, hoping to reveal potentially important biological phenomena. It is well known [1,2,3] that various parts of biomolecules, such as DNA, RNA, and proteins, are significantly (statistically) correlated. Formal measures and techniques for quantifying these correlations are topics of current investigation. The biological implications of these correlations are deep, and they themselves remain unresolved. We propose to develop precise and reliable methodologies for quantifying and identifying such dependencies, based on the information-theoretic notion of mutual information
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