A parameterized algorithm for predicting transcription factor binding sites

Abstract

In this paper, we study the Transcription Factor Binding Sites (TFBS) prediction problem in bioinformatics. We develop a novel parameterized approach that can efficiently explore the space of all possible locations of TFBSs in a set of homologous sequences with high accuracy. The exploration is performed by an ensemble of a few Hidden Markov Models (HMM), where the size of the ensemble is the parameter of the algorithm. The ensemble is initially constructed through the local alignments between two sequences that have the lowest similarity value in the sequence set, the parameters of each HMM in the ensemble are revised when the remaining sequences in the set are scanned through by it one by one. A list of possible TFBSs are generated when all sequences in the set have been processed by the ensemble. Testing results showed that this approach can accurately handle the cases where a single sequence may contain multiple binding sites and thus has advantages over most of the existing approaches when a sequence may contain multiple binding sites. © 2014 Springer International Publishing Switzerland.