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What is Gene Expression?

What is Gene Expression?

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.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...

What is Gene Expression?

What is Gene Expression?

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...

Cell Specific Gene Expression

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 Expression

Cell Specific Gene Expression

No description available

Chromatin Position Affects Gene Expression

Chromatin Position Affects Gene Expression

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.
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...

mRNA Stability and Gene Expression

mRNA Stability and Gene Expression

The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability

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Sparse canonical correlation analysis for identifying, connecting and completing gene-expression networks.

Sandra Waaijenborg¹, Aeilko H Zwinderman

¹Clinical Epidemiology, Biostatistics & Bioinformatics, Academic Medical Center, University of Amsterdam, PO Box 22700, 1100 DE Amsterdam, the Netherlands.

BMC Bioinformatics

|September 30, 2009

Summary

This summary is machine-generated.

Penalized canonical correlation analysis helps identify new genes for metabolic pathways using gene expression data. This method successfully found over 25 candidate genes for the glioma pathway in glioblastoma patients.

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Area of Science:

Bioinformatics
Systems Biology
Genomics

Background:

Metabolic pathway analysis is crucial for understanding cellular functions.
Gene expression data provides insights into biological processes.
Identifying novel genes for pathways aids in disease research.

Purpose of the Study:

To generalize penalized canonical correlation analysis (PCCA) for pathway completeness assessment.
To identify candidate genes for integration into known metabolic pathways.
To analyze microarray gene-expression data for pathway discovery.

Main Methods:

Generalized penalized canonical correlation analysis was employed.
Wold's method was used for canonical variate calculation.
Ridge penalization and elastic net were applied for gene regression analysis.

Main Results:

Simulations demonstrated the model's ability to identify new candidate genes (correlation >= 0.3).
Applied to glioblastoma microarray data (12,209 genes, 45 patients).
Identified over 25 genes correlating > 0.9 with pathway canonical variates for the glioma pathway.

Conclusions:

Penalized canonical correlation analysis is a powerful tool for pathway analysis.
The method effectively identifies candidate genes for pathway enrichment.
This approach enhances the understanding of metabolic pathways through gene expression data.