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Related Concept Videos

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...
Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...
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.
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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.
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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...
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...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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A Bayesian model for pooling gene expression studies that incorporates co-regulation information.

Erin M Conlon1, Bradley L Postier, Barbara A Methé

  • 1Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA, USA. econlon@mathstat.umass.edu

Plos One
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for analyzing gene expression data by incorporating operon information. This approach improves the accuracy of detecting differential gene expression in prokaryotes by leveraging co-regulation.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Systems Biology

Background:

  • Current Bayesian models for gene expression studies often assume gene independence.
  • Prokaryotic gene organization involves operons, where genes are co-regulated.
  • This co-regulation is typically ignored in standard gene expression pooling models.

Purpose of the Study:

  • To develop a novel Bayesian model for pooling gene expression studies.
  • To integrate prokaryotic operon structure into gene expression analysis.
  • To enhance the estimation of gene expression by borrowing information within operons.

Main Methods:

  • Developed a new Bayesian statistical model for gene expression data analysis.
  • Incorporated gene operon information into the Bayesian framework.
  • Calculated gene-specific posterior probabilities of differential expression for inference.

Main Results:

  • The proposed Bayesian model significantly improves gene expression estimation compared to models assuming independence.
  • Simulations and biological data analyses confirmed the benefits of incorporating co-regulation information.
  • The model effectively leverages information from co-regulated genes within operons.

Conclusions:

  • Integrating operon information into Bayesian models offers a more accurate approach for analyzing prokaryotic gene expression.
  • This method enhances the detection of differential gene expression by accounting for biological co-regulation.
  • The model is most effective when known operon structures are available for analysis.