This article is part of the series Information Theoretic Methods for Bioinformatics.

Open Access Research Article

Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates

Hasan M Aktulga1*, Ioannis Kontoyiannis2, L Alex Lyznik3, Lukasz Szpankowski4, Ananth Y Grama1 and Wojciech Szpankowski1

Author Affiliations

1 Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA

2 Department of Informatics, Athens University of Economics & Business, Patission 76, Athens 10434, Greece

3 Pioneer Hi-Breed International, Johnston, IA, USA

4 Bioinformatics Program, University of California, San Diego, CA 92093, USA

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EURASIP Journal on Bioinformatics and Systems Biology 2007, 2007:14741 doi:10.1155/2007/14741


The electronic version of this article is the complete one and can be found online at: http://bsb.eurasipjournals.com/content/2007/1/14741


Received:26 February 2007
Accepted:25 September 2007
Published:5 December 2007

© 2007 Hasan Metin Aktulga et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 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.

Research Article

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