Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
Proceedings of the sixth annual international conference on Computational biology
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
A boosting approach for motif modeling using ChIP-chip data
Bioinformatics
A fast, alignment-free, conservation-based method for transcription factor binding site discovery
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
| Hi-index | 0.00 |
A complete understanding of transcriptional regulatory processes in the cell requires identification of transcription factor binding sites on a genomewide scale. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known transcription factor binding sites occur in the genome than are actually functional. Chromatin structure is known to play an important role in guiding transcription factors to those sites that are functional. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling transcription factors to bind DNA in those regions [1]. In this paper, we describe a novel algorithm which employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy; the nucleosome occupancy information comes from a recently published computational model [2]. When a Gibbs sampling algorithm with our informative prior is applied to yeast sequencesets identified by ChIP-chip [3], the correct motif is found in 50% more cases than with an uninformative uniform prior. Moreover, if nucleosome occupancy information is not available, our informative prior reduces to a new kind of prior that can exploit discriminative information in a purely generative setting.