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Predicting In Vivo Transcription Factor Occupancy from In Vitro Binding

dc.contributor.advisor Gordan, Raluca Stamatov, Rumen 2014-05-14T19:22:48Z 2015-05-09T04:30:06Z 2014
dc.description.abstract <p>The spatial pattern of transcription factor (TF) binding and the level of TF occupancy at individual sites across the genome determine how a TF regulates its targets. Consequently, predicting the location and level of TF binding genome-wide is of great importance and has received much attention recently. Protein-binding microarray (PBM) technology has become the golden standard for studying TF-DNA interactions in vitro, while Chromatin Immunoprecipitation followed by DNA Sequencing (ChIP-seq) is the standard method for inferring TF binding in vivo. However, direct interpretation of in vitro results in an in vivo context is challenging and to-date remains scarce. In this study, we focus on the E2F family of paralogous TFs, whose mode of binding to DNA has been controversial. Previous studies have shown that E2F factors bind to the TTTSSCGCG motif, where S can be a C or a G. Still, only a small fraction of in vivo targets are reported to contain this motif, hinting at indirect recruitment of the protein. We observed that genomic occupancy of E2F factors directly correlates with their in vitro binding affinities. By using data from universal PBM experiments, we show that E2F factors likely bind to DNA through direct sequence recognition and not through cofactor interaction. Furthermore, we developed a kinetic binding model using the PBM data to describe competition between different members of the E2F family and successfully distinguished between their unique targets. Overall, these results demonstrate how the straightforward and simple in vitro PBM experiments can be used for inferring the complex in vivo landscape of TF binding and elucidate the mechanism of E2F-DNA interaction.</p>
dc.subject Bioinformatics
dc.title Predicting In Vivo Transcription Factor Occupancy from In Vitro Binding
dc.type Master's thesis
dc.department Computational Biology and Bioinformatics
duke.embargo.months 12

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