Discovery of transcription factor binding sites through integration of generic motif finders

Abstract

Locating transcription factor binding sites is a key step in understanding gene regulation. Due to its importance, many de novo motif-finding methods have been proposed. Individually, these motif finders perform unimpressively overall based on Tompa's benchmark datasets. Moreover, these motif finders vary in their definitions of what constitute a motif, and in their methods for finding statistically overrepresented motifs. There is no clear way for biologists to choose the motif finder that is most suitable for their task. The purpose of this work is to describe a method called MotifVoter to identify transcription factor binding sites by integrating the results found by motif finders of different models. Validation of our method on Tompa's benchmark, real metazoan and E. coli datasets show that it can improve the sensitivity significantly without sacrificing the precision. Our approach offers a practical alternative for biologists to study novel transcription factors.The MotifVoter software is available for public use at: http://www.comp.nus.edu.sg/~bioinfo/MotifVoter