Discovering Functional Transcription Factor Binding from Superimposed Gene Networks

2006 ◽  
Author(s):  
M. Weirauch ◽  
J. Stuart
Keyword(s):  
Transcription Factor ◽  
Gene Networks ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Functional Transcription Factor

scholarly journals Genomic Promoter Analysis Predicts Functional Transcription Factor Binding

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
J. Sunil Rao ◽  
Suresh Karanam ◽  
Colleen D. McCabe ◽  
Carlos S. Moreno
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Proximal Promoter ◽  
Marginal Effect ◽  
Promoter Sequences ◽  
Factor Binding ◽  
Functional Transcription Factor

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.

scholarly journals ConBind: motif-aware cross-species alignment for the identification of functional transcription factor binding sites

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

scholarly journals Large-scale turnover of functional transcription factor binding sites in Drosophila

2005 ◽  
Vol preprint (2006) ◽  
pp. e130
Author(s):  
Alan M. Moses ◽  
Daniel Pollard ◽  
David A Nix ◽  
Venky N. Iyer ◽  
Xiao-Yong Li ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Large Scale ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Functional Transcription Factor

scholarly journals Mapping DNA sequence to transcription factor binding energy in vivo

2018 ◽  
Author(s):  
Stephanie L. Barnes ◽  
Nathan M. Belliveau ◽  
William T. Ireland ◽  
Justin B. Kinney ◽  
Rob Phillips
Keyword(s):  
Transcription Factor ◽  
Binding Energy ◽  
Dna Sequence ◽  
Binding Sites ◽  
Gene Networks ◽  
Transcription Factor Binding ◽  
Binding Energies ◽  
Regulatory Sequences ◽  
Factor Binding

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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