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Related Concept Videos

Master Transcription Regulators02:23

Master Transcription Regulators

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...
Master Transcription Regulators02:23

Master Transcription Regulators

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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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 dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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 dimers that...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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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Related Experiment Video

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
03:37

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

Published on: March 1, 2024

Learning transcriptional regulatory relationships using sparse graphical models.

Xiang Zhang1, Wei Cheng, Jennifer Listgarten

  • 1Microsoft Research, Los Angeles, California, USA.

Plos One
|May 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a sparse graphical model to map gene regulatory networks, improving predictions by incorporating hidden factors for unknown regulatory elements.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Genomics

Background:

  • Transcriptional regulatory networks are crucial for understanding gene function.
  • Challenges include incomplete regulatory knowledge, confounding factors, and high dimensionality.
  • Existing methods struggle with these complexities in gene expression data analysis.

Purpose of the Study:

  • To develop a robust computational model for inferring gene regulatory networks.
  • To address limitations of existing methods in handling unknown regulators and confounding factors.
  • To improve prediction accuracy in analyzing high-throughput gene expression profiles.

Main Methods:

  • A sparse (L1 regularized) graphical model was developed.
  • The model incorporates known transcription factors and introduces hidden variables.
  • Gene expression levels are modeled as linear combinations of known and hidden factors.

Main Results:

  • The proposed model demonstrated superior out-of-sample prediction accuracy compared to models without hidden variables.
  • Gene sets associated with hidden variables showed strong correlations with Gene Ontology categories.
  • The approach effectively handles challenges in inferring complex regulatory relationships.

Conclusions:

  • Sparse graphical models with hidden variables offer a powerful framework for dissecting transcriptional regulatory networks.
  • This method enhances the understanding of gene regulation by accounting for unknown factors.
  • The findings have implications for computational biology and systems biology research.