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Related Concept Videos

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 Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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...
Global Regulatory Systems01:28

Global Regulatory Systems

Global regulatory systems in bacteria enable rapid and coordinated responses to environmental changes by integrating sensory inputs with gene expression, ensuring efficient adaptation to fluctuating conditions. Key global regulatory mechanisms include regulons, two-component systems, sigma factors, and secondary messengers.Regulons and Global RegulatorsA regulon is a collection of genes and operons controlled by a common global regulator. These regulators enable bacteria to prioritize resource...

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Related Experiment Video

Updated: May 10, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM.

Andrew J Sedgewick1, Stephen C Benz, Shahrooz Rabizadeh

  • 1Joint Carnegie Mellon - University of Pittsburgh Ph.D Program in Computational Biology, Pittsburgh, PA 15260, USA.

Bioinformatics (Oxford, England)
|July 2, 2013
PubMed
Summary

We enhanced the PARADIGM algorithm to analyze multi-omics cancer data, revealing key gene interactions and improving patient survival predictions. This pathway analysis method aids in understanding tumor evasion mechanisms.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • High-dimensional '-omics' data offers detailed cancer insights.
  • Understanding tumor evasion requires knowledge of cellular pathways.
  • Existing algorithms may not fully integrate multi-omics data for pathway analysis.

Purpose of the Study:

  • To extend the PARADIGM algorithm for integrated multi-omics pathway analysis.
  • To learn gene and protein interaction strengths and directions from curated literature.
  • To improve patient stratification and survival prediction using pathway-informed models.

Main Methods:

  • Extended the PARADIGM algorithm to incorporate curated gene and protein interactions.
  • Applied the enhanced algorithm to The Cancer Genome Atlas (TCGA) cohort data (genomic and mRNA expression).
  • Utilized a Naive Bayesian assumption for gene activity prediction and patient clustering.

Main Results:

  • Learned the strength and direction of 78% of curated gene/protein interactions from TCGA data.
  • Identified significant enrichment of strongest interactions in transcriptional regulation, apoptosis, and cell cycle pathways.
  • Found differential interactions between breast cancer subtypes, particularly involving the MYC pathway and ER alpha network.
  • Improved patient survival prediction through clustering of gene activity predictions with the Naive Bayesian assumption.

Conclusions:

  • The extended PARADIGM algorithm effectively integrates multi-omics data to infer biological pathways.
  • Pathway analysis can reveal key molecular mechanisms in cancer, including tumor immune evasion.
  • The Naive Bayesian assumption improves patient stratification based on pathway activity, particularly for survival analysis.
  • Co-regulators largely act independently on shared targets, validating the model's assumption.