ChIP-based genome-wide assays of transcription factor (TF) occupancy possess emerged as a powerful, high-throughput method to understand transcriptional regulation, especially on a global scale. chosen based on RNA-SEQ expression data from the time point of the ChIP experiment. We found widespread evidence of both cooperative and antagonistic effects by secondary TFs, and explicitly quantified these effects. We were able to identify multiple classes of interactions, including (1) long-range interactions between primary and secondary motifs (separated by 150 bp), suggestive of indirect effects such as chromatin remodeling, (2) short-range interactions with specific inter-site spacing biases, suggestive of direct physical interactions, and (3) overlapping binding sites suggesting competitive binding. Furthermore, by factoring out the previously reported strong correlation between TF occupancy and DNA accessibility, we were able to categorize the effects into those that are likely to be mediated by the secondary TF’s effect on local accessibility and those that utilize accessibility-independent mechanisms. Finally, we conducted pull-down assays to test model-based predictions of short-range cooperative interactions, and found that seven of the eight TF pairs tested physically interact and that some of these interactions mediate cooperative binding to DNA. Author Summary Chromatin Immunoprecipitation (ChIP)-based genome-wide assays of transcription factor (TF) occupancy have emerged as a powerful, high throughput method to understand transcriptional regulation, especially on a global scale. Here, we utilize 45 ChIP-chip and ChIP-SEQ data sets from to explore the underlying mechanisms of TF-DNA binding. For this, we employ a biophysically motivated computational model, in conjunction with over 300 TF motifs (binding specificities) as well as gene expression and DNA accessibility data from different developmental stages in embryos. Our findings provide robust statistical evidence of the role played by TF-TF interactions in shaping genome-wide TF-DNA binding profiles, and thus in directing gene regulation. Our method allows us to go beyond simply recognizing the existence of such interactions, to quantifying their effects on TF occupancy. We are able to categorize the probable mechanisms of these effects 925705-73-3 as involving direct Rabbit polyclonal to RAD17 physical interactions versus accessibility-mediated indirect interactions, long-range versus short-range interactions, and cooperative versus antagonistic interactions. Our analysis reveals widespread evidence of combinatorial regulation present in recently generated ChIP data sets, and sets the stage for rich integrative models of the future that will predict cell type-specific TF occupancy values from sequence and expression data. Introduction A major challenge in the analysis of genomic sequences is the annotation of DNA accessibility were tested for the ability to help describe TF ChIP data. These studies clearly demonstrate that TF occupancy has a close relationship with DNA accessibility [6], [7], with both factors likely influencing each other [6], [15]C[19]. While these studies reveal that experimental analysis of accessibility can improve modeling of ChIP data, they do not reveal the underlying genomic sequence features 925705-73-3 that contribute to accessibility. In another study [5], sequence motifs experimentally and computationally identified in were shown to contribute to context-specific TF occupancy. Application of discriminative motif analysis to a TF assayed across multiple conditions can successfully identify predictive motifs associated 925705-73-3 with context-specific binding. However, whether TFs bound to these discriminative motifs contribute to occupancy by direct interaction with the primary TF, accessibility or other mechanisms is not assessed. In this work, we test the influence of various potential sequence determinants of TF-DNA binding C the TF’s binding motif, as well as the positive or negative influence of other TFs binding in the vicinity C on each of 45 TF-ChIP data sets in For this analysis, we took advantage of over 925705-73-3 300 distinct DNA binding specificity motifs determined for individual TFs [20], which encompasses approximately 40% of all predicted TFs, and relied upon stage-specific whole-genome RNA-SEQ data [21] to determine which secondary TFs are expressed at the time of the ChIP experiment. We follow the general framework proposed by Kaplan et al. [6], which involves: (1) building computational models that predict TF binding at a location, and (2) assessing how well a baseline model that only uses the primary motif (i.e., binding motif of the ChIP’ed TF) fits.