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Related Concept Videos

DNA Microarrays02:34

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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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Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts
07:03

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Published on: January 2, 2018

Gene expression rate comparison for multiple high-throughput datasets.

Chien-Ming Chen, Tsan-Huang Shih, Tun-Wen Pai

    IET Systems Biology
    |September 27, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel system for integrating gene expression data from microarray and RNA-sequencing (RNA-seq) experiments. The system aids in understanding biological functions by analyzing differential gene expression and utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathways.

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    Area of Science:

    • Bioinformatics
    • Genomics
    • Systems Biology

    Background:

    • Microarray and RNA-sequencing (RNA-seq) are high-throughput techniques for measuring gene expression.
    • Integrating data from these platforms is crucial for comprehensive transcriptomic analysis.
    • Existing methods may not fully leverage pathway and functional annotations for comparative studies.

    Purpose of the Study:

    • To develop and present a novel system for integrating and analyzing multiple gene expression datasets from microarray and RNA-seq.
    • To facilitate the exploration of differential gene expression rates and biological functions.
    • To enhance the visualization and understanding of functional relationships among genes.

    Main Methods:

    • The system integrates gene expression data using Kyoto Encyclopedia of Genes and Genomes (KEGG) biological pathways and Gene Ontology (GO) associations.
    • Gene expression levels are estimated from RNA-seq reads or microarray images.
    • Normalization methods, including Reads Per Kilobase per Million mapped reads (RPKM) and housekeeping gene selection, are evaluated.
    • Differential gene expression is visualized in temporal pathways and functional relationships are illustrated using GO term tag clouds.

    Main Results:

    • The developed system successfully integrates and normalizes gene expression data from diverse high-throughput techniques.
    • Differential gene expression patterns are visualized, aiding in the interpretation of biological functions.
    • Functional relationships of gene clusters are enhanced through visual representation of associated Gene Ontology terms.

    Conclusions:

    • The integrated system provides a robust platform for comparative transcriptomic analysis using both microarray and RNA-seq data.
    • The approach enhances the biological interpretation of gene expression data through pathway and functional enrichment visualizations.
    • This system offers valuable insights into gene expression dynamics and functional genomics for researchers.