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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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A Genetic Algorithm to Optimize Weighted Gene Co-Expression Network Analysis.

David Toubiana1, Rami Puzis2, Avi Sadka3

  • 1Department of Plant Sciences, University of California, Davis, Davis, California.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|July 30, 2019
PubMed
Summary
This summary is machine-generated.

A new genetic algorithm (GA) refines Weighted Gene Co-expression Network Analysis (WGCNA) by identifying smaller gene subsets linked to traits. This improves the specificity of functional insights, aiding in understanding complex biological relationships.

Keywords:
Japanese plumPrunus salicinaRNAseqgenetic algorithmplant hormonesweighted gene co-expression network analysis

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Weighted Gene Co-expression Network Analysis (WGCNA) is a common tool for linking gene expression data to phenotypic traits.
  • WGCNA generates large gene modules, often containing genes with diverse functional roles, making specific functional interpretation challenging.

Purpose of the Study:

  • To develop an optimized method for identifying specific gene subsets associated with phenotypic traits.
  • To enhance the functional interpretation of gene expression data by increasing the specificity of gene-trait relationships.

Main Methods:

  • A novel stochastic optimization algorithm, the genetic algorithm (GA), was developed to refine gene module-trait associations.
  • The GA iteratively increases the correlation between a phenotypic trait and a subset of genes within a WGCNA-generated module.
  • The method was applied to a Japanese plum hormone profile and an RNA-seq dataset.

Main Results:

  • The GA successfully identified smaller gene subsets with higher correlation to the target trait compared to WGCNA's full modules.
  • Gene Ontology (GO) term enrichment analysis of GA-identified gene sets showed increased specificity for biological functions.
  • The refined gene sets provided more focused insights into fruit hormone balance compared to standard WGCNA module analysis.

Conclusions:

  • The developed genetic algorithm offers a more precise approach to dissecting gene-trait relationships within co-expression networks.
  • This method enhances the biological interpretability of gene expression data, particularly for complex traits like hormone regulation.
  • The GA provides a valuable tool for researchers seeking to identify key genes driving specific phenotypic characteristics.