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Combinatorial Gene Control02:33

Combinatorial Gene Control

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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.
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...
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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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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...
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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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Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Related Experiment Video

Updated: Nov 15, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Improving gene function predictions using independent transcriptional components.

Carlos G Urzúa-Traslaviña1, Vincent C Leeuwenburgh1,2, Arkajyoti Bhattacharya1

  • 1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

Nature Communications
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

Researchers developed a new method using Independent Component Analysis (ICA) to predict the functions of coding and non-coding transcripts from high throughput sequencing data. This approach enhances functional predictions and aids in understanding gene roles.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Interpreting high throughput sequencing data is challenging due to limited functional knowledge of coding and non-coding transcripts.
  • Accurate functional prediction of transcripts is crucial for advancing transcriptomics and molecular biology.

Purpose of the Study:

  • To develop and validate a novel computational approach for predicting the functions of a large number of coding and non-coding transcripts.
  • To compare the efficacy of Independent Component Analysis (ICA) against Principal Component Analysis (PCA) for functional prediction.

Main Methods:

  • Utilized a consensus Independent Component Analysis (ICA) and guilt-by-association strategy.
  • Applied the method to publicly available transcriptomic profiles to predict functional groups.
  • Compared ICA-derived components with PCA for prediction accuracy and robustness.

Main Results:

  • Successfully predicted over 23,000 functional groups involving more than 55,000 transcripts.
  • ICA-derived components demonstrated superior performance over PCA in functional prediction confidence and accuracy.
  • The ICA approach showed improved prediction when new gene sets were introduced and was less susceptible to issues arising from gene multi-functionality.

Conclusions:

  • The developed ICA-based method offers a more reliable way to predict transcript functions compared to traditional methods like PCA.
  • This work significantly expands the functional annotation of coding and non-coding transcripts, aiding in the interpretation of complex biological data.
  • Predictions are accessible via a public web portal for human and mouse transcriptomic data.