Bioinformatics Vol. 18 no. 90002 2002
Pages S100-S109
© 2002 Oxford University Press
Identifying transcription factor binding sites through Markov chain optimization
1 Department of Computer Science,
University of California, Riverside, CA, 92521, USA
2 Genetics/Bioinformatics Program,
University of California, Riverside, CA, 92521, USA
4 Department of Cell Biology and Neuroscience,
University of California, Riverside, CA, 92521, USA
4 Protein Informatics Group, Life Sciences Division,
Oak Ridge National Laboratory, Oak Ridge, TN, 37831-6480, USA
Received on April 8, 2002
; accepted on June 15, 2002
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 
(HNF4). A list of
71 known HNF4 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 HNF4. Our results show that the program was
very successful in identifying 77 new HNF4 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.
Contact: jiang{at}cs.ucr.edu
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