Dustin T. Holloway[1] (dth128@bu.edu)
Mark Kon (mkon@bu.edu)[2]
Charles DeLisi[3] (delisi@bu.edu)
[1]Molecular Biology Cell Biology and Biochemistry, Boston University,
Boston, MA 02215, USA
[2]Department of Mathematics and Statistics, Boston University, Boston, MA
02215, USA
[3]Bioinformatics and Systems Biology, Boston University, Boston, MA
02215, USA
Transcription factor binding sites (TFBS) in gene promoter regions are
often predicted by using position specific scoring matrices (PSSMs),
which summarize sequence patterns of experimentally determined TF
binding sites. Although PSSMs are more reliable than simple consensus
string matching in predicting a true binding site, they generally
result in high numbers of false positive hits. This study attempts to
reduce the number of false positive matches and generate new
predictions by integrating various types of genomic data by two
methods: a Bayesian allocation procedure, and support vector machine
classification.
Several methods will be explored to strengthen the prediction of a true
TFBS in the Saccharomyces cerevisiae genome: binding site degeneracy,
binding site conservation, phylogenetic profiling, TF binding site
clustering, gene expression profiles, GO functional annotation, and
k-mer counts in promoter regions. Binding site degeneracy (or
redundancy) refers to the number of times a particular transcription
factor's binding motif is discovered in the upstream region of a gene.
Phylogenetic conservation takes into account the number of orthologous
upstream regions in other genomes that contain a particular binding
site. Phylogenetic profiling refers to the presence or absence of a
gene across a large set of genomes. Binding site clusters are
statistically significant clusters of TF binding sites detected by the
algorithm ClusterBuster. Gene expression takes into account the idea
that when the gene expression profiles of a transcription factor and a
potential target gene are correlated, then it is more likely that the
gene is a genuine target. Also, genes with highly correlated
expression profiles are often regulated by the same TF(s). The GO
annotation data takes advantage of the idea that common transcription
targets often have related function. Finally, the distribution of the
counts of all k-mers of length 4, 5, and 6 in gene's promoter region
were examined as means to predict TF binding. In each case the data
are compared to known true positives taken from ChIP-chip
data [11,14], Transfac, and the Saccharomyces Genome Database.
First, degeneracy, conservation, expression, and binding site clusters
were examined independently and in combination via Bayesian
allocation. Then, binding sites were predicted with a support vector
machine (SVM) using all methods alone and in combination. The SVM
works best when all genomic data are combined, but can also identify
which methods contribute the most to accurate classification. On
average, a support vector machine can classify binding sites with high
sensitivity and an accuracy of almost 80%.