Background: 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. Results: 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. Conclusions: The results demonstrate that the new approach improves motif discovery by exploiting multiple species information.
ASJC Scopus subject areas
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics