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Volume 9


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In Silico Biology 9, 0018 (2009); ©2009, Bioinformation Systems e.V.  



Systematic analysis of disease-related regulatory mutation classes reveals distinct effects on transcription factor binding

Kirsti Laurila1* and Harri Lähdesmäki1,2

1 Department of Signal Processing, Tampere University of Technology, P.O. Box 527, FI-33101 Tampere, Finland
2 Department of Information and Computer Science, Helsinki University of Technology, P.O. Box 5400, FI-02015 TKK, Finland

* Corresponding author
   Email: kirsti.laurila@tut.fi


Edited by E. Wingender; received February 25, 2009; revised April 21, 2009; accepted April 25, 2009; published May 15, 2009


Abstract

Detailed knowledge of the mechanisms of transcriptional regulation is essential in understanding the gene expression in its entirety. Transcription is regulated, among other things, by transcription factors that bind to DNA and can enhance or repress the transcription process. If a transcription factor fails to bind to DNA or binds to a wrong DNA region that can cause severe effects to the gene expression, to the cell and even to the individual. The problems in transcription factor binding can be caused by alterations in DNA structure which often occurs when parts of the DNA strands are mutated. An increasing number of the identified disease-related mutations occur in gene regulatory sequences. These regulatory mutations can disrupt transcription factor binding sites or create new ones. We have studied effects of mutations on transcription factor binding affinity computationally. We have compared our results with experimentally verified cases where a mutation in a gene regulatory region either creates a new transcription factor binding site or deletes a previously existing one. We have investigated the statistical properties of the changes on transcription factor binding affinity according to the mutation type. Our analysis shows that the probability of a loss of a transcription factor binding site and a creation of a new one varies remarkably by the mutation type. Our results demonstrate that computational analysis provides valuable information about the effect of mutations on transcription factor binding sites. The analysis results also give a useful test set for in vitro studies of regulatory mutation effects.


Keywords: transcription factors, binding affinity, regulatory mutation