PhyME: A probabilistic algorithm for finding motifs in sets of orthologous sequences
1 Center for Studies in Physics and Biology, The Rockefeller University, New York, NY 10021, USA
2 School of Computer Science, McGill University, 3480 University Street, Montreal, QC, H3A 2A7, CANADA
3 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195-2350, USA
BMC Bioinformatics 2004, 5:170 doi:10.1186/1471-2105-5-170Published: 28 October 2004
This paper addresses the problem of discovering transcription factor binding sites in heterogeneous sequence data, which includes regulatory sequences of one or more genes, as well as their orthologs in other species.
We propose an algorithm that integrates two important aspects of a motif's significance – overrepresentation and cross-species conservation – into one probabilistic score. The algorithm allows the input orthologous sequences to be related by any user-specified phylogenetic tree. It is based on the Expectation-Maximization technique, and scales well with the number of species and the length of input sequences. We evaluate the algorithm on synthetic data, and also present results for data sets from yeast, fly, and human.
The results demonstrate that the new approach improves motif discovery by exploiting multiple species information.