scholarly journals Global Mapping of Transcription Factor Binding Sites by Sequencing Chromatin Surrogates: a Perspective on Experimental Design, Data Analysis, and Open Problems

2012 ◽  
Vol 5 (1) ◽  
pp. 156-178 ◽  
Author(s):  
Yingying Wei ◽  
George Wu ◽  
Hongkai Ji
Keyword(s):  
Transcription Factor ◽  
Data Analysis ◽  
Experimental Design ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Open Problems ◽  
Design Data ◽  
Factor Binding ◽  
Global Mapping

scholarly journals De novo motif identification improves the accuracy of predicting transcription factor binding sites in ChIP-Seq data analysis

2010 ◽  
Vol 38 (11) ◽  
pp. e126-e126 ◽  
Author(s):  
Valentina Boeva ◽  
Didier Surdez ◽  
Noëlle Guillon ◽  
Franck Tirode ◽  
Anthony P. Fejes ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Data Analysis ◽  
Binding Sites ◽  
De Novo ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Motif Identification

scholarly journals Global mapping of ZBTB7A transcription factor binding sites in HepG2 cells

2010 ◽  
Vol 15 (2) ◽  
Author(s):  
Xuyu Zu ◽  
Lingling Yu ◽  
Yiming Sun ◽  
Jing Tian ◽  
Feng Liu ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Neural Development ◽  
Binding Sites ◽  
Hepg2 Cells ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Signal Pathways ◽  
Factor Binding ◽  
Cellular Processes ◽  
Global Mapping

AbstractZBTB7A is a known proto-oncogene that is implicated in carcinogenesis and cell differentiation and development. Fully understanding the function of ZBTB7A in cellular processes could provide useful strategies for cancer treatment and development-associated disease therapy. Here, global mapping of ZBTB7A transcription factor binding sites was developed by utilizing microarray technology in HepG2 cells. The data obtained from the microarrays was further validated via chromatin immunoprecipitation-PCR (ChIP-PCR) and real time-PCR, and it was revealed that ZBTB7A may be one of the regulators of neural development. ZBTB7A target signal pathways were identified in signal pathway and GO (Gene Ontology) analyses. This is the first report on the global mapping of ZBTB7A downstream direct targets, and these findings will be useful in understanding the roles of ZBTB7A in cellular processes.

Gene Variants and Haplotypes Modifying Transcription Factor Binding Sites in the Human Cyclooxygenase 1 and 2 (PTGS1 and PTGS2) Genes

2014 ◽  
Vol 15 (2) ◽  
pp. 182-195 ◽  
Author(s):  
Jose Agundez ◽  
David Gonzalez-Alvarez ◽  
Miguel Vega-Rodriguez ◽  
Emilia Botello ◽  
Elena Garcia-Martin
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Gene Variants ◽  
Factor Binding ◽  
Cyclooxygenase 1

scholarly journals Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

2021 ◽  
Vol 11 (11) ◽  
pp. 5123
Author(s):  
Maiada M. Mahmoud ◽  
Nahla A. Belal ◽  
Aliaa Youssif
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Messenger Rna ◽  
Area Under The Curve ◽  
Noisy Data ◽  
Transcription Factor Binding Sites ◽  
Classification Problem ◽  
Transcription Factor Binding ◽  
K Nearest Neighbors ◽  
Factor Binding

Transcription factors (TFs) are proteins that control the transcription of a gene from DNA to messenger RNA (mRNA). TFs bind to a specific DNA sequence called a binding site. Transcription factor binding sites have not yet been completely identified, and this is considered to be a challenge that could be approached computationally. This challenge is considered to be a classification problem in machine learning. In this paper, the prediction of transcription factor binding sites of SP1 on human chromosome1 is presented using different classification techniques, and a model using voting is proposed. The highest Area Under the Curve (AUC) achieved is 0.97 using K-Nearest Neighbors (KNN), and 0.95 using the proposed voting technique. However, the proposed voting technique is more efficient with noisy data. This study highlights the applicability of the voting technique for the prediction of binding sites, and highlights the outperformance of KNN on this type of data. The study also highlights the significance of using voting.

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