In-silico Prediction of the Regulatory Element Patterns of Human Pathogen Mycobacterium Tuberculosis

Computational prediction of nucleotide binding specificity for transcription factors remains a fundamental and largely unsolved problem. Determination of binding positions is a prerequisite for research in gene regulation. Many computational technologies make it feasible to identify potential targets of transcription factors. Regulatory elements can be used for bio-control in the case of disease causing bacteria. Cluster analysis of gene expression data, is often used to infer regulatory modules or biological function by associating unknown genes with other well known genes that have similar expression patterns. Using simple clustering algorithm for microarray datasets we grouped those genes that are very similar at expression level, and then analyzed them for the prediction of regulatory elemetns by using RSAT. In present work we applied this approach on virulence genes of human pathogen M. tuberculosis and predict its regulatory sequence pattern. We found that among 76 treated clusters of virulence genes, 36 clusters shown the presence of both oligo and spaced dyad sequence patterns of regulatory elements.