Precise physical models of protein-DNA interaction from high-throughput data

@article{citeulike:1032936, abstract = {A cell's ability to regulate gene transcription depends in large part on the energy with which transcription factors (TFs) bind their DNA regulatory sites. Obtaining accurate models of this binding energy is therefore an important goal for quantitative biology. In this article, we present a principled likelihood-based approach for inferring physical models of TF-DNA binding energy from the data produced by modern high-throughput binding assays. Central to our analysis is the ability to assess the relative likelihood of different model parameters given experimental observations. We take a unique approach to this problem and show how to compute likelihood without any explicit assumptions about the noise that inevitably corrupts such measurements. Sampling possible choices for model parameters according to this likelihood function, we can then make probabilistic predictions for the identities of binding sites and their physical binding energies. Applying this procedure to previously published data on the Saccharomyces cerevisiae TF Abf1p, we find models of TF binding whose parameters are determined with remarkable precision. Evidence for the accuracy of these models is provided by an astonishing level of phylogenetic conservation in the predicted energies of putative binding sites. Results from in vivo and in vitro experiments also provide highly consistent characterizations of Abf1p, a result that contrasts with a previous analysis of the same data. 10.1073/pnas.0609908104}, author = {Kinney, Justin B. and Tkacik, Gasper and Callan, Curtis G. }, citeulike-article-id = {1032936}, doi = {10.1073/pnas.0609908104}, journal = {PNAS}, keywords = {cis\_regulatory\_elements, dna-protein\_interaction, motif\_searching, statistical\_method}, month = {January}, number = {2}, pages = {501--506}, posted-at = {2008-03-20 15:56:33}, priority = {2}, title = {Precise physical models of protein-DNA interaction from high-throughput data}, url = {http://dx.doi.org/10.1073/pnas.0609908104}, volume = {104}, year = {2007} }

TY - JOUR ID - citeulike:1032936 TI - Precise physical models of protein-DNA interaction from high-throughput data JF - PNAS VL - 104 IS - 2 SP - 501 EP - 506 N2 - A cell's ability to regulate gene transcription depends in large part on the energy with which transcription factors (TFs) bind their DNA regulatory sites. Obtaining accurate models of this binding energy is therefore an important goal for quantitative biology. In this article, we present a principled likelihood-based approach for inferring physical models of TF-DNA binding energy from the data produced by modern high-throughput binding assays. Central to our analysis is the ability to assess the relative likelihood of different model parameters given experimental observations. We take a unique approach to this problem and show how to compute likelihood without any explicit assumptions about the noise that inevitably corrupts such measurements. Sampling possible choices for model parameters according to this likelihood function, we can then make probabilistic predictions for the identities of binding sites and their physical binding energies. Applying this procedure to previously published data on the Saccharomyces cerevisiae TF Abf1p, we find models of TF binding whose parameters are determined with remarkable precision. Evidence for the accuracy of these models is provided by an astonishing level of phylogenetic conservation in the predicted energies of putative binding sites. Results from in vivo and in vitro experiments also provide highly consistent characterizations of Abf1p, a result that contrasts with a previous analysis of the same data. 10.1073/pnas.0609908104 KW - cis_regulatory_elements KW - dna-protein_interaction KW - motif_searching KW - statistical_method AU - Kinney, Justin AU - Tkacik, Gasper AU - Callan, Curtis PY - 2007/01/09/ UR - http://dx.doi.org/10.1073/pnas.0609908104 ER -