scholarly journals Repression of a matrix metalloprotease gene by E1A correlates with its ability to bind to cell type-specific transcription factor AP-2.

1996 ◽  
Vol 93 (7) ◽  
pp. 3088-3093 ◽  
Author(s):  
K. Somasundaram ◽  
G. Jayaraman ◽  
T. Williams ◽  
E. Moran ◽  
S. Frisch ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Matrix Metalloprotease ◽  
Cell Type ◽  
Specific Transcription Factor ◽  
Cell Type Specific

scholarly journals Sequence and chromatin determinants of cell-type-specific transcription factor binding

2012 ◽  
Vol 22 (9) ◽  
pp. 1723-1734 ◽  
Author(s):  
A. Arvey ◽  
P. Agius ◽  
W. S. Noble ◽  
C. Leslie
Keyword(s):  
Transcription Factor ◽  
Transcription Factor Binding ◽  
Cell Type ◽  
Specific Transcription Factor ◽  
Factor Binding ◽  
Cell Type Specific

scholarly journals Leopard: fast decoding cell type-specific transcription factor binding landscape at single-nucleotide resolution

2019 ◽  
Author(s):  
Hongyang Li ◽  
Yuanfang Guan
Keyword(s):  
Transcription Factor ◽  
Network Architecture ◽  
Characteristic Curve ◽  
Cell Types ◽  
Cell Type ◽  
Single Nucleotide ◽  
Specific Transcription Factor ◽  
Cell Type Specific ◽  
Nucleotide Resolution ◽  
Single Nucleotide Resolution

AbstractDecoding the cell type-specific transcription factor (TF) binding landscape at single-nucleotide resolution is crucial for understanding the regulatory mechanisms underlying many fundamental biological processes and human diseases. However, limits on time and resources restrict the high-resolution experimental measurements of TF binding profiles of all possible TF-cell type combinations. Previous computational approaches either can not distinguish the cell-context-dependent TF binding profiles across diverse cell types, or only provide a relatively low-resolution prediction. Here we present a novel deep learning approach, Leopard, for predicting TF-binding sites at single-nucleotide resolution, achieving the median area under receiver operating characteristic curve (AUROC) of 0.994. Our method substantially outperformed state-of-the-art methods Anchor and FactorNet, improving the performance by 19% and 27% respectively despite evaluated at a lower resolution. Meanwhile, by leveraging a many-to-many neural network architecture, Leopard features hundred-fold to thousand-fold speedup compared to current many-to-one machine learning methods.

scholarly journals TACO: a general-purpose tool for predicting cell-type-specific transcription factor dimers

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 208 ◽  
Author(s):  
Aleksander Jankowski ◽  
Shyam Prabhakar ◽  
Jerzy Tiuryn
Keyword(s):  
Transcription Factor ◽  
General Purpose ◽  
Cell Type ◽  
Specific Transcription Factor ◽  
Cell Type Specific

scholarly journals spatzie: An R package for identifying significant transcription factor motif co-enrichment from enhancer-promoter interactions

2021 ◽  
Author(s):  
Jennifer Hammelman ◽  
Konstantin Krismer ◽  
David K. Gifford
Keyword(s):  
Transcription Factor ◽  
Statistical Tests ◽  
Simulated Data ◽  
R Package ◽  
Cell Type ◽  
Specific Transcription Factor ◽  
Transcription Factor Motif ◽  
Cell Type Specific ◽  
Factor Interactions ◽  
Promoter Interaction

Genomic interactions provide important context to our understanding of the state of the genome. One question is whether specific transcription factor interactions give rise to genome organization. We introduce spatzie, an R package and a website that implements statistical tests for significant transcription factor motif cooperativity between enhancer-promoter interactions. We conducted controlled experiments under realistic simulated data from ChIP-seq to confirm spatzie is capable of discovering co-enriched motif interactions even in noisy conditions. We then use spatzie to investigate cell type specific transcription factor cooperativity within recent human ChIA-PET enhancer-promoter interaction data. The method is available online at https://spatzie.mit.edu.

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