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Related Concept Videos

Transcription Factors02:16

Transcription Factors

81.7K
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...
81.7K
Conserved Binding Sites01:49

Conserved Binding Sites

4.9K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
4.9K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.0K
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...
7.0K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

2.3K
2.3K
General Transcription Factors01:30

General Transcription Factors

6.4K
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...
6.4K
RNA Polymerase II Accessory Proteins02:36

RNA Polymerase II Accessory Proteins

10.5K
Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
10.5K

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Related Experiment Video

Updated: Dec 9, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

9.1K

Deep learning for inferring transcription factor binding sites.

Peter K Koo1, Matt Ploenzke2

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.

Current Opinion in Systems Biology
|September 9, 2020
PubMed
Summary
This summary is machine-generated.

Deep learning accurately predicts transcription factor binding sites. New methods are needed to ensure these models learn true biological mechanisms, not just correlations, for reliable genomic insights.

Keywords:
Deep learninginterpretabilitymotifsneural networkstranscription factor binding

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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

Last Updated: Dec 9, 2025

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
06:38

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Published on: February 7, 2019

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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis
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Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

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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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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Deep learning models show high accuracy in predicting transcription factor binding sites from DNA sequences.
  • However, high performance does not guarantee the models capture causal sequence-function relationships.
  • There is a need to move beyond simple performance metrics on benchmark datasets.

Purpose of the Study:

  • To highlight advances in deep learning for genomics, specifically for inferring transcription factor binding sites.
  • To discuss the importance of model interpretability in understanding biological mechanisms.
  • To review current local and global interpretability methods.

Main Methods:

  • Review of recent applications of deep learning in genomics.
  • Description of various deep learning model architectures used for transcription factor binding site prediction.
  • Discussion of local and global model interpretability techniques.

Main Results:

  • Deep learning offers powerful predictive capabilities for transcription factor binding sites.
  • Model interpretability methods are crucial for identifying key features driving predictions.
  • Advances in interpretability can provide insights into underlying biological mechanisms.

Conclusions:

  • Interpreting deep learning models is essential for validating their biological relevance beyond predictive accuracy.
  • Future research should focus on developing and applying interpretability methods to gain deeper biological understanding.
  • This work emphasizes the need for causal inference in deep learning for genomics.