Computational detection of transcription factor binding sites through differential Rényi entropy

  • Authors:

  • Joan Maynou;Joan-Josep Gallardo-Chacón;Montserrat Vallverdú;Pere Caminal;Alexandre Perera

  • Affiliations:

  • Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Barcelona, Spain;Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Barcelona, Spain;Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Barcelona, Spain;Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Barcelona, Spain;Department ESAII, Centre for Biomedical Engineering Research, Technical University of Catalonia, Barcelona, Barcelona, Spain

  • Venue:
  • IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
  • Year:
  • 2010

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Abstract

Regulatory sequence detection is a critical facet for understanding the cell mechanisms in order to coordinate the response to stimuli. Protein synthesis involves the binding of a transcription factor to specific sequences in a process related to the gene expression initiation. A characteristic of this binding process is that the same factor binds with different sequences placed along all genome. Thus, any computational approach shows many difficulties related with this variability observed from the binding sequences. This paper proposes the detection of transcription factor binding sites based on a parametric uncertainty measurement (Rényi entropy). This detection algorithm evaluates the variation on the total Rényi entropy of a set of sequences when a candidate sequence is assumed to be a true binding site belonging to the set. The efficiency of the method is measured in form of receiver operating characteristic (ROC) curves on different transcription factors from Saccharomyces cerevisiae organism. The results are compared with other known motif detection algorithms such as Motif Discovery scan (MDscan) and multiple expectation-maximization (EM) for motif elicitation (MEME).