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Related Concept Videos

Transcription Factors02:16

Transcription Factors

82.2K
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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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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Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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2.4K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

7.1K
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...
7.1K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.4K
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...
9.4K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K

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Related Experiment Video

Updated: Jan 8, 2026

Enhanced Yeast One-hybrid Screens To Identify Transcription Factor Binding To Human DNA Sequences
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InCURA: integrative gene clustering based on transcription factor binding sites.

Lorna Rinck1,2, Ricardo O Ramirez Flores3, Julio Saez-Rodriguez2,3

  • 1European Center for Angioscience (ECAS), Medical Faculty Mannheim, Heidelberg University, 68167 Mannheim, Germany.

Nucleic Acids Research
|December 19, 2025
PubMed
Summary

Interpreting transcriptomic data is difficult. InCURA, an integrative clustering strategy, uses transcription factor (TF) motif patterns to reveal gene modules and regulatory programs missed by other methods.

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Interpreting transcriptomic datasets is challenging due to the lack of context-specific gene sets.
  • Standard enrichment and co-expression analyses often fail to capture the underlying biology.
  • Identifying upstream regulators and regulatory programs requires advanced analytical approaches.

Purpose of the Study:

  • To present InCURA, an integrative clustering strategy for transcriptomic data analysis.
  • To enable biologically meaningful interpretation of gene expression data, especially when context-specific gene sets are unavailable.
  • To uncover novel gene modules and regulatory programs using TF motif occurrence patterns.

Main Methods:

  • InCURA utilizes transcription factor (TF) motif occurrence patterns in gene promoters.
  • It takes expressed genes to identify dataset-specific TFs and differentially regulated genes (DRGs) for clustering.
  • Promoter sequences of DRGs are scanned for TF binding motifs, creating a gene-by-TFBS matrix for unsupervised clustering.

Main Results:

  • InCURA successfully uncovered functionally coherent gene modules across diverse biological datasets.
  • The method revealed upstream regulators and regulatory programs not detected by standard analyses.
  • It demonstrated the ability to dissect transcriptional responses in settings lacking specific gene sets.

Conclusions:

  • InCURA offers a user-friendly, regulation-centric tool for transcriptomic data analysis.
  • It overcomes limitations of generic or unavailable gene sets for biological interpretation.
  • The strategy enhances the discovery of regulatory mechanisms in gene expression data.