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Related Concept Videos

Transcription Factors02:16

Transcription Factors

76.3K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
76.3K
General Transcription Factors01:30

General Transcription Factors

5.5K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
5.5K
Master Transcription Regulators02:23

Master Transcription Regulators

7.0K
Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
7.0K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.5K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
6.5K
Chromatin Immunoprecipitation- ChIP02:36

Chromatin Immunoprecipitation- ChIP

11.2K
Chromatin immunoprecipitation, or ChIP, is an antibody-based technique used to identify sites on DNA that bind to transcription factors of interest or histone proteins. It also helps determine the type of histone modifications such as acetylation, phosphorylation, or methylation.
Types of ChIP
ChIP can be divided into two types - X-ChIP and N-ChIP. X-ChIP involves in vivo cross-linking of histones and regulatory proteins to DNA, fragmenting the DNA by sonication, and isolating the protein-DNA...
11.2K

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Related Experiment Video

Updated: Aug 16, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

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A capsule network-based method for identifying transcription factors.

Peijie Zheng1, Yue Qi1, Xueyong Li1

  • 1School of Electrical Engineering, Shaoyang University, Shaoyang, China.

Frontiers in Microbiology
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning method using capsule networks accurately identifies transcription factors (TFs). This computational approach surpasses existing methods, offering a faster and more cost-effective alternative to physical detection for gene expression research.

Keywords:
LSTMcapsule networkdeep learningsemanticstranscription factors

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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

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Related Experiment Videos

Last Updated: Aug 16, 2025

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences

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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter
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Identification of Transcription Factor Regulators using Medium-Throughput Screening of Arrayed Libraries and a Dual-Luciferase-Based Reporter

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcription factors (TFs) regulate gene expression and cellular functions.
  • Experimental TF detection is resource-intensive, necessitating computational solutions.

Purpose of the Study:

  • To develop and evaluate a novel deep learning method for accurate TF identification.
  • To provide a user-friendly web server for computational TF detection.

Main Methods:

  • An end-to-end deep learning model incorporating an embedding layer, bidirectional LSTM, and capsule network.
  • Utilizing capsule network-based representation for distinguishing TFs from non-TFs.

Main Results:

  • Achieved a high accuracy of 0.8820 in TF identification.
  • Demonstrated superior performance compared to state-of-the-art methods.
  • Capsule network representation proved more effective than property-based representation.

Conclusions:

  • The capsule network-based deep learning method offers a highly accurate and efficient approach for TF identification.
  • The developed web server provides a valuable, accessible tool for researchers in gene expression studies.