Discovering Functional Transcription Factor Binding from Superimposed Gene Networks

Author(s):  
M. Weirauch ◽  
J. Stuart
2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
J. Sunil Rao ◽  
Suresh Karanam ◽  
Colleen D. McCabe ◽  
Carlos S. Moreno

Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%. Conclusion. Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.


2015 ◽  
Vol 44 (8) ◽  
pp. e72-e72 ◽  
Author(s):  
Stefan H. Lelieveld ◽  
Judith Schütte ◽  
Maurits J.J. Dijkstra ◽  
Punto Bawono ◽  
Sarah J. Kinston ◽  
...  

2005 ◽  
Vol preprint (2006) ◽  
pp. e130
Author(s):  
Alan M. Moses ◽  
Daniel Pollard ◽  
David A Nix ◽  
Venky N. Iyer ◽  
Xiao-Yong Li ◽  
...  

2018 ◽  
Author(s):  
Stephanie L. Barnes ◽  
Nathan M. Belliveau ◽  
William T. Ireland ◽  
Justin B. Kinney ◽  
Rob Phillips

AbstractDespite the central importance of transcriptional regulation in systems biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to analyze a promoter sequence and identify the locations, regulatory roles, and energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for interpreting transcriptional regulatory sequences using in vivo methods (i.e. the massively parallel reporter assay Sort-Seq) to formulate quantitative models that map a transcription factor binding site’s DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 kBT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor’s sequence specificity.


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