Predicting tissue specific transcription factor binding sites

Material for Predicting tissue specific transcription factor binding sites

Our k-mer based PBM model (Figure 1a in main text) is motivated by the biophysics of TF binding to the probes in PBM experiments. Following Zhao et al. [1], we denote by Yi the experimentally measured intensity of the i-Th. probe on the PBM array. We denote by F (i) the (unobserved) binding probability of the TF to this probe. While these two quantities are related, due to experiential errors a...

Predicting Transcription Factor Binding Sites Using Structural Knowledge

Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid-nucleotide recognition preferences. These are...

Assessing phylogenetic motif models for predicting transcription factor binding sites

MOTIVATION A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model...

Predicting Transcription Factor Binding Sites Using a Bayesian Allocation Method

Identifying tissue-selective transcription factor binding sites in vertebrate promoters.

We present a computational method aimed at systematically identifying tissue-selective transcription factor binding sites. Our method focuses on the differences between sets of promoters that are associated with differentially expressed genes, and it is effective at identifying the highly degenerate motifs that characterize vertebrate transcription factor binding sites. Results on simulated dat...