Assessment of clusters of transcription factor binding sites in relationship to human promoter, CpG islands and gene expression.
BACKGROUND
Gene expression is regulated mainly by transcription factors (TFs) that interact with regulatory cis-elements on DNA sequences. To identify functional regulatory elements, computer searching can predict TF binding sites (TFBS) using position weight matrices (PWMs) that represent positional base frequencies of collected experimentally determined TFBS. A disadvantage of this approach is the large output of results for genomic DNA. One strategy to identify genuine TFBS is to utilize local concentrations of predicted TFBS. It is unclear whether there is a general tendency for TFBS to cluster at promoter regions, although this is the case for certain TFBS. Also unclear is the identification of TFs that have TFBS concentrated in promoters and to what level this occurs. This study hopes to answer some of these questions.


RESULTS
We developed the cluster score measure to evaluate the correlation between predicted TFBS clusters and promoter sequences for each PWM. Non-promoter sequences were used as a control. Using the cluster score, we identified a PWM group called PWM-PCP, in which TFBS clusters positively correlate with promoters, and another PWM group called PWM-NCP, in which TFBS clusters negatively correlate with promoters. The PWM-PCP group comprises 47% of the 199 vertebrate PWMs, while the PWM-NCP group occupied 11 percent. After reducing the effect of CpG islands (CGI) against the clusters using partial correlation coefficients among three properties (promoter, CGI and predicted TFBS cluster), we identified two PWM groups including those strongly correlated with CGI and those not correlated with CGI.


CONCLUSIONS
Not all PWMs predict TFBS correlated with human promoter sequences. Two main PWM groups were identified: (1) those that show TFBS clustered in promoters associated with CGI, and (2) those that show TFBS clustered in promoters independent of CGI. Assessment of PWM matches will allow more positive interpretation of TFBS in regulatory regions.
Murakami
Katsuhiko
K
RIKEN Genomic Sciences Center, 1-7-22, Suehiro-cho, Tsurumi, Yokohama, Kanagawa, JAPAN. katsu@gsc.riken.go.jp
Kojima
Toshio
T
Sakaki
Yoshiyuki
Y
D001665:Binding Sites / Q000235:genetics; D002874:Chromosome Mapping / Q000706:statistics & numerical data; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D018899:CpG Islands / Q000235:genetics*; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D011237:Predictive Value of Tests; D011401:Promoter Regions, Genetic / Q000235:genetics*; D015534:Trans-Activators / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics*
D015534:Trans-Activators; D014157:Transcription Factors