Identifying Transcription Factor Binding Sites through Markov Chain Optimization

Even though every cell in an organism contains the same genetic　material, each cell does not express the same cohort of genes.　Therefore, one of the major problems facing genomic research today is to determine not only which genes are differentially expressed and under what conditions, but also how the expression of those genes is regulated. The first step in determining　differential gene expression is the binding of sequence-specific　DNA binding proteins (i.e. transcription factors) to　regulatory regions of the genes (i.e. promoters and enhancers). An important aspect to understanding how a given transcription factor functions is to know the entire gamut of binding sites and subsequently potential target genes that the factor may bind/regulate. In this study, we have developed a computer algorithm to scan genomic databases for transcription factor binding sites, based on a novel Markov chain optimization method, and used it to scan the human genome for sites that bind to hepatocyte nuclear factor 4 α(HNF4α). A list of　71 known HNF4α binding sites from the literature were used　to train our Markov chain model. By looking at the window of 600　nucleotides around the transcription start site of each confirmed gene on the human genome, we identified 849 sites with varying binding potential and experimentally tested 109 of those sites for binding to HNF4α. Our results show that the program was very successful in identifying 77 new HNF4α binding sites　with varying binding affinities (i.e. a 71% success rate). 　Therefore, this computational method for searching genomic databases for potential transcription factor binding sites is a powerful tool for investigating mechanisms of differential gene regulation.

This is a joint work with Kyle Ellrott, Chuhu Yang, and Frances Sladek.