scholarly journals Predicting the effects of SNPs on transcription factor binding affinity

2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Cody Morterud ◽  
Colten Williams ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Affinity ◽  
Binding Sites ◽  
Experimental Validation ◽  
Transcription Factor Binding ◽  
Noncoding Regions ◽  
Factor Binding ◽  
Genome Wide ◽  
Regulatory Loci ◽  
Functional Transcription Factor

AbstractGWAS have revealed that 88% of disease associated SNPs reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). SEMpl estimates transcription factor binding affinity by observing differences in ChIP-seq signal intensity for SNPs within functional transcription factor binding sites genome-wide. By cataloging the effects of every possible mutation within the transcription factor binding site motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci.

scholarly journals Genome-Wide Identification of Transcription Start Sites, Promoters and Transcription Factor Binding Sites in E. coli

PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7526 ◽  
Author(s):  
Alfredo Mendoza-Vargas ◽  
Leticia Olvera ◽  
Maricela Olvera ◽  
Ricardo Grande ◽  
Leticia Vega-Alvarado ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Transcription Start ◽  
Transcription Start Sites ◽  
E Coli ◽  
Factor Binding ◽  
Genome Wide

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 Predicting the effects of SNPs on transcription factor binding affinity

2019 ◽  
Author(s):  
Sierra S Nishizaki ◽  
Natalie Ng ◽  
Shengcheng Dong ◽  
Robert S Porter ◽  
Cody Morterud ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Binding Affinity ◽  
Association Studies ◽  
Transcription Factor Binding ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Factor Binding ◽  
Genome Wide

Abstract Motivation Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). Results SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. Availability and implementation SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. Supplementary information Supplementary data are available at Bioinformatics online.

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