A Computational Approach to Identify Transcription Factor Binding Sites Containing Spacer Regions

A critical challenge in studying gene regulation is deciphering functionally important regions of DNA which when altered, can affect gene activation levels. Bioinformatics tools have been developed to extract motifs from the human genome using methods such as position weight matrices (PWMs), Hidden Markov Models (HMMs), and machine learning (ML). However, these methods are not suitable for motifs with variable spacer regions or when insufficient experimentally validated sequences exist in the literature to build models. In this paper, we present a computational method to identify and extract motifs in conjunction with other high throughput methods such as protein binding microarrays.