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Related Concept Videos

Transcription Factors02:16

Transcription Factors

79.9K
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...
79.9K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.8K
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...
6.8K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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2.2K
Conserved Binding Sites01:49

Conserved Binding Sites

4.7K
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...
4.7K
General Transcription Factors01:30

General Transcription Factors

6.1K
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...
6.1K
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

11.0K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
11.0K

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Related Experiment Video

Updated: Nov 3, 2025

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

Published on: February 7, 2019

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DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA

Lei Deng1, Hui Wu1, Xuejun Liu2

  • 1School of Computer Science and Engineering, Central South University, Changsha 410075, China.

International Journal of Molecular Sciences
|June 2, 2021
PubMed
Summary

DeepD2V, a hybrid deep learning model, accurately predicts transcription factor binding sites using k-mer representations and neural networks. This method outperforms existing approaches, advancing drug design and development.

Keywords:
Word2Vecbidirectional long short term memory networkconvolutional neural networkprotein–DNA bindingtranscription factor binding sites

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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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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

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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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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins

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Area of Science:

  • Computational biology
  • Genomics
  • Bioinformatics

Background:

  • Predicting protein-DNA binding sites is crucial for drug design and development.
  • Biochemical identification of transcription factor binding sites is time-consuming and laborious.
  • Existing computational methods for predicting binding sites have limitations.

Purpose of the Study:

  • To present DeepD2V, a novel hybrid deep learning framework for predicting transcription factor binding sites.
  • To improve the accuracy and robustness of in silico prediction of protein-DNA interactions.

Main Methods:

  • Constructing input matrices from DNA sequences and their variants (inverse, complementary, complementary inverse).
  • Utilizing k-mer representation with a sliding window and word2vec for pre-trained distributed representations.
  • Employing a hybrid recurrent and convolutional neural network (CNN and bi-LSTM) for prediction.

Main Results:

  • DeepD2V demonstrated superior performance and robustness on 50 public ChIP-seq benchmark datasets.
  • Word2vec-based k-mer distributed representation outperformed one-hot encoding.
  • The integrated CNN and bi-LSTM framework surpassed individual CNN or bi-LSTM models.

Conclusions:

  • DeepD2V offers a powerful and accurate computational approach for predicting transcription factor binding sites.
  • The study highlights the effectiveness of hybrid deep learning architectures and distributed representations in bioinformatics.
  • The developed framework has significant implications for advancing research in genomics and drug discovery.