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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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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.
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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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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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Related Experiment Video

Updated: Jan 10, 2026

Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
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Combining Motifs, CRE Activity, And Gene Expression Data Using ML Greatly Improves the Accuracy of Tissue-Specific TF

Wooseok J Jung1, Sandeep Acharya2, Daniel P Ruskin3

  • 1Department of Computer Science and Engineering, Washington University, St Louis, MO.

Biorxiv : the Preprint Server for Biology
|November 24, 2025
PubMed
Summary

METANets accurately reconstruct tissue-specific transcription factor networks by integrating TF motifs, binding data, and gene expression. This novel approach significantly improves the identification of direct, functional gene targets compared to existing methods.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Transcription factor (TF) networks are crucial for understanding gene regulation, but current reconstruction methods using only TF motifs or gene expression data have limitations.
  • Existing methods struggle to identify actual TF binding sites and distinguish direct from indirect regulatory relationships.
  • Accurate TF networks must be tissue-specific due to variations in TF activity and expression across different human tissues.

Purpose of the Study:

  • To develop a novel, supervised ensemble learning approach for reconstructing accurate, tissue-specific TF networks.
  • To integrate diverse data types including TF motifs, TF binding locations, cis-regulatory element (CRE) activity, and gene expression data.
  • To enhance the identification of direct, functional TF-gene targets and improve the utility of transcriptional regulatory maps.

Main Methods:

  • Introduced METANets (Motif Expression TF Association Networks), a supervised ensemble learning framework.
  • Utilized XGBoost models to predict TF binding in CREs, incorporating features from linear (LASSO) and non-linear (BART) regression models.
  • Trained models on tissue-specific and aggregated RNA-seq data from 36 human tissues (GTEx) to capture TF motifs, TF binding, and CRE activity.

Main Results:

  • METANets significantly outperformed existing motif-only and expression-only methods in reconstructing TF networks.
  • The approach successfully identified more direct, functional TF targets, validated by ChIP-seq data and gene ontology enrichment.
  • Tissue specificity analysis using expression quantitative trait loci (eQTLs) confirmed METANets' ability to capture tissue-specific regulatory patterns.

Conclusions:

  • METANets provide a robust and accurate method for reconstructing tissue-specific TF networks by integrating complementary data types.
  • The developed approach enhances the accuracy and utility of transcriptional regulatory maps for studying TF-mediated regulation in human tissues.
  • METANets offer a valuable resource for researchers investigating complex gene regulatory mechanisms across diverse human tissues.