scholarly journals Data on master regulators and transcription factor binding sites found by upstream analysis of multi-omics data on methotrexate resistance of colon cancer

Data in Brief ◽  
2017 ◽  
Vol 10 ◽  
pp. 499-504 ◽  
Author(s):  
Alexander E. Kel
Keyword(s):  
Colon Cancer ◽  
Transcription Factor ◽  
Binding Sites ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Omics Data ◽  
Factor Binding ◽  
Master Regulators ◽  
Methotrexate Resistance

scholarly journals A flexible repertoire of transcription factor binding sites and diversity threshold determines enhancer activity in embryonic stem cells

2020 ◽  
Author(s):  
Gurdeep Singh ◽  
Shanelle Mullany ◽  
Sakthi D Moorthy ◽  
Richard Zhang ◽  
Tahmid Mehdi ◽  
...  
Keyword(s):  
Stem Cells ◽  
Transcription Factor ◽  
Embryonic Stem Cells ◽  
Binding Sites ◽  
Embryonic Stem ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Enhancer Activity ◽  
Factor Binding ◽  
Master Regulators

ABSTRACTTranscriptional enhancers are critical for development, phenotype evolution and often mutated in disease contexts; however, even in well-studied cell types, the sequence code conferring enhancer activity remains unknown. We found genomic regions with conserved binding of multiple transcription factors in mouse and human embryonic stem cells (ESCs) contain on average 12.6 conserved transcription factor binding sites (TFBS). These TFBS are a diverse repertoire of 70 different sequences representing the binding sites of both known and novel ESC regulators. Remarkably, using a diverse set of TFBS from this repertoire was sufficient to construct short synthetic enhancers with activity comparable to native enhancers. Site directed mutagenesis of conserved TFBS in endogenous enhancers or TFBS deletion from synthetic sequences revealed a requirement for more than ten different TFBS. Furthermore, specific TFBS, including the OCT4:SOX2 co-motif, are dispensable, despite co-binding the OCT4, SOX2 and NANOG master regulators of pluripotency. These findings reveal a TFBS diversity threshold overrides the need for optimized regulatory grammar and individual TFBS that bind specific master regulators.

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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