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Related Concept Videos

What is Gene Expression?01:42

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?01:36

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 processed and...
What is Gene Expression?01:42

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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.
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...
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...
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
However, only 1% of the DNA is composed of genes that encode proteins; the rest, 99% is non-coding DNA. This non-coding DNA performs...

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A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
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Published on: August 16, 2017

Gene set analysis for longitudinal gene expression data.

Ke Zhang1, Haiyan Wang, Arne C Bathke

  • 1School of Medicine & Health Sciences, University of North Dakota, Grand Forks, ND 58202, USA. ke.zhang@med.und.edu

BMC Bioinformatics
|July 5, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new nonparametric method for gene set analysis (GSA) in longitudinal microarray data, outperforming existing methods in power and accuracy for dynamic gene expression studies.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene set analysis (GSA) interprets gene expression profiles by analyzing groups of genes.
  • Longitudinal microarray studies measure gene expression repeatedly over time, requiring methods that account for complex correlations.

Purpose of the Study:

  • To develop a robust nonparametric approach for comparing gene expression in longitudinal studies.
  • To address the challenge of within- and between-gene correlations in time-course gene expression data.

Main Methods:

  • A novel nonparametric statistical approach for gene set analysis.
  • Derivation of limiting distributions for large gene sets and recommendation of permutation tests for smaller sets.
  • Incorporation of unknown within- and between-gene correlations.

Main Results:

  • The proposed method demonstrates superior power compared to existing approaches across various data distributions and correlation structures.
  • Identified significantly altered gene sets in an IL-2 stimulation study, validating its practical application.
  • The method is robust even with small numbers of replications.

Conclusions:

  • The developed gene set analysis tool is a promising approach for analyzing longitudinal microarray data.
  • The method effectively handles complex correlation structures inherent in time-course gene expression data.
  • Associated R scripts and raw data are publicly available for reproducibility and further research.