Epigenetic priors for identifying active transcription factor binding sites

Motivation Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored. Results We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consisten...

• Type: Journal
• Year: 2012
• Where: BIOINFORMATICS
• Authors: Gabriel Cuellar-Partida, Fabian A. Buske, Robert C. McLeay, Tom Whitington, William Stafford Noble, Timothy L. Bailey
• Keywords: BIOINFORMATICS 2012, Distributed And Parallel Computing, Motif Search, Sequence Motif, Transcription Factor Binding Sites