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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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...
Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
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Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules.

Bruno M Tesson1, Rainer Breitling, Ritsert C Jansen

  • 1Groningen Bioinformatics Center, University of Groningen, Kerklaan 30, 9751 NN Haren, The Netherlands.

BMC Bioinformatics
|October 8, 2010
PubMed
Summary
This summary is machine-generated.

We developed DiffCoEx, a novel method for identifying differential gene coexpression modules. This sensitive approach reveals subtle correlation changes across conditions, aiding gene regulation studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene coexpression analysis of large microarray datasets aids in studying gene regulation.
  • Differential coexpression analysis is an emerging field with limited sensitive, untargeted methods for module identification.
  • Existing methods struggle to identify subtle, differential correlation patterns within de novo gene modules.

Purpose of the Study:

  • To introduce DiffCoEx, a novel method for identifying differentially coexpressed gene modules.
  • To provide a sensitive and untargeted approach for detecting differential coexpression patterns.
  • To enable the construction of de novo gene modules based on subtle correlation shifts.

Main Methods:

  • DiffCoEx builds upon the Weighted Gene Coexpression Network Analysis (WGCNA) framework.
  • The method identifies changes in gene correlation patterns between conditions.
  • It constructs de novo modules by grouping genes with shared differential correlation.

Main Results:

  • DiffCoEx successfully identified biologically relevant, differentially coexpressed modules.
  • The method was demonstrated on a rat cancer dataset, highlighting its practical application.
  • The results showcase the ability to detect subtle differential coexpression patterns.

Conclusions:

  • DiffCoEx offers a simple and sensitive method for detecting gene coexpression differences.
  • The approach is applicable across multiple conditions.
  • It advances the field of differential coexpression analysis by providing a robust tool.