In silico prediction of transcription factors that interact with the E2F family of transcription factors

We describe a computational approach to predict transcription factors that interact with a given transcription factor, or a given family of transcription factors. We first collect a set of upstream sequences, to which a particular transcription factor or a family of transcription factors may bind. This set of upstream sequences is regarded as our training set. We collect a set of a large number of randomly chosen upstream sequences as the control set. We define a random variable to represent the clustering information of any putative transcription factor binding sites (TFBSs) in the control set. We calibrate the observed clusters of TFBSs in the training set to the distribution of the random variable representing the clustering information in the control set. We select the significant Clusters from the training set and report the putative transcription factors that can bind to the TFBSs in these clusters. These reported transcription factors are candidates of interactive partners of the transcription factor (family) we started from. We applied this approach to discover transcription factors that may cooperate with E2F family proteins. We have identified 15 candidate interactive partners of E2F. Among them, 5 have been suggested or verified by previous biological studies.