The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes.
BACKGROUND
A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate.


RESULTS
Training a Profile Hidden Markov Model with corresponding positions matching a combined-palindromic p53-binding motif creates the best p53-RE predictive model. The p53HMM algorithm is available on-line: (http://tools.csb.ias.edu).


CONCLUSIONS
Using Profile Hidden Markov Models with training methods that exploit the redundant information of the homotetramer p53 binding site provides better predictive models than weight matrices (PSSMs). These methods may also boost performance when applied to other transcription factor binding sites.
Riley
Todd
T
The Institute for Advanced Study, Princeton, NJ, USA. tr2261@columbia.edu
Yu
Xin
X
Sontag
Eduardo
E
Levine
Arnold
A
D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D008390:Markov Chains; D008954:Models, Biological; D008969:Molecular Sequence Data; D020218:Response Elements; D012984:Software; D016159:Tumor Suppressor Protein p53 / Q000737:chemistry / Q000378:metabolism*
D016159:Tumor Suppressor Protein p53