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  • Research Article
  • Open Access

Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates

  • 1Email author,
  • 2,
  • 3,
  • 4,
  • 1 and
  • 1
EURASIP Journal on Bioinformatics and Systems Biology20072007:14741

  • Received: 26 February 2007
  • Accepted: 25 September 2007
  • Published:


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.


  • Genomic Sequence
  • Mutual Information
  • System Biology
  • Statistical Dependence
  • Information Estimate


Authors’ Affiliations

Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
Department of Informatics, Athens University of Economics & Business, Patission 76, Athens, 10434, Greece
Pioneer Hi-Breed International, Johnston, IA, USA
Bioinformatics Program, University of California, San Diego, CA 92093, USA


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© Hasan Metin Aktulga et al. 2007

This article is published under license to BioMed Central Ltd. 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.