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Related Concept Videos

Transcription Factors02:16

Transcription Factors

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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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...
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Cooperative Binding of Transcription Regulators02:13

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General Transcription Factors01:30

General Transcription Factors

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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...
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Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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...
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Related Experiment Video

Updated: Nov 19, 2025

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ 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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Locating transcription factor binding sites by fully convolutional neural network.

Qinhu Zhang1, Siguo Wang2, Zhanheng Chen3

  • 1Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University, Shanghai, China.

Briefings in Bioinformatics
|January 26, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces FCNA, a deep learning model that accurately identifies transcription factor binding motifs and sites. FCNA improves upon existing methods by performing nucleotide-level classification for precise TF-DNA binding analysis.

Keywords:
TFBSs locationfully Convolutional Neural Networkglobal Average Poolingnucleotide-level prediction

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Area of Science:

  • Genomics and Molecular Biology
  • Computational Biology and Bioinformatics
  • Deep Learning Applications in Biology

Background:

  • Transcription factors (TFs) are crucial regulators of gene expression.
  • Identifying TF binding sites (TFBSs) is fundamental in molecular and cellular biology.
  • Current deep learning (DL) methods primarily focus on sequence-level prediction, often failing to accurately identify motifs and TFBSs.

Purpose of the Study:

  • To develop a novel deep learning model for accurate identification of TF binding motifs and TFBSs.
  • To overcome the limitations of sequence-level classification in existing TF binding prediction methods.
  • To enable precise localization of TFBSs and accurate motif identification at the nucleotide level.

Main Methods:

  • Development of a fully convolutional network coupled with global average pooling (FCNA).
  • FCNA performs a nucleotide-level binary classification task for TFBS and motif identification.
  • Validation using human ChIP-seq datasets and comparison with existing computational methods.

Main Results:

  • FCNA significantly outperforms competing methods in identifying TF binding sites and motifs.
  • Regions identified by FCNA enhance the performance of downstream motif discovery tools.
  • FCNA accurately identifies TF-DNA binding motifs across diverse cell lines and infers indirect TF-DNA bindings.

Conclusions:

  • FCNA offers a significant advancement in predicting TF-DNA interactions at a nucleotide resolution.
  • The model's ability to identify motifs and refine predictions makes it valuable for biological research.
  • FCNA provides a robust tool for understanding gene regulation and TF-DNA binding dynamics.