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Related Concept Videos

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Detecting intergene correlation changes in microarray analysis: a new approach to gene selection.

Rui Hu1, Xing Qiu, Galina Glazko

  • 1Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Box 630, Rochester, New York 14642, USA. Rui_Hu@urmc.rochester.edu

BMC Bioinformatics
|January 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to find differentially correlated genes using microarray data. This approach identifies important genes missed by traditional methods focusing only on differential expression.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Microarray technology is a common screening tool for identifying genes with large differential expression between phenotypes.
  • Traditional methods often overlook genes that change their relationships (correlations) with other genes across different biological conditions.
  • There is a need to identify these differentially correlated genes, which may hold significant biological insights.

Purpose of the Study:

  • To propose a novel nonparametric selection procedure for identifying differentially correlated genes.
  • To enhance existing gene selection methods by incorporating intergene correlation information.
  • To uncover candidate genes missed by traditional univariate analysis.

Main Methods:

  • Development of a nonparametric selection procedure to detect differentially correlated genes.
  • Validation using both simulation studies and resampling techniques.
  • Application of the procedure to real biological microarray data.

Main Results:

  • The proposed procedure successfully identified genes that were differentially correlated but not differentially expressed.
  • Simulations and resampling confirmed the accuracy of the method in detecting these specific genes.
  • Analysis of biological data revealed potentially important candidate genes not found by conventional methods.

Conclusions:

  • Intergene correlation provides valuable multidimensional genomic information beyond simple differential expression.
  • The developed method effectively utilizes correlation as a new criterion for gene selection.
  • This approach offers biologists additional, potentially crucial, candidate genes for further investigation.