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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.
Genetic Information Flows from DNA to RNA to Protein
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What is Gene Expression?01:36

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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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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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...
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Chromatin Position Affects Gene Expression02:35

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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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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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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.
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Related Experiment Video

Updated: Jan 25, 2026

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Maximal information coefficient applied to differentially expressed genes identification: A feasibility study.

Dan Yang, Hanming Liu

    Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
    |May 3, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Maximal Information Coefficient (MIC) shows strong performance in identifying differentially expressed genes, outperforming Limma and matching other methods. MIC demonstrates superior robustness and adaptability in gene expression analysis.

    Keywords:
    Maximal information coefficientdifferentially expressed genefeasibilityidentification

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    Area of Science:

    • Bioinformatics
    • Genomics
    • Statistical Analysis

    Background:

    • Microarray technology presents challenges in extracting valuable gene information and function.
    • Accurate identification of differentially expressed genes is crucial for understanding biological processes.

    Purpose of the Study:

    • To evaluate the feasibility of Maximal Information Coefficient (MIC) for identifying differentially expressed genes using simulation data.
    • To compare MIC's performance against established methods in gene expression analysis.

    Main Methods:

    • Utilized simulation data to test the application of MIC in differentially expressed gene identification.
    • Compared MIC's performance metrics, including AUC, against Limma, SAM, ROTS, and DESeq2.

    Main Results:

    • MIC consistently outperformed Limma and demonstrated comparable performance to SAM, ROTS, and DESeq2.
    • MIC showed a significantly lower rate of AUC < 0.5 compared to other methods.
    • MIC did not exhibit the phenomenon of increasing AUC with increasing noise, indicating better stability.

    Conclusions:

    • MIC is a top-tier method for identifying differentially expressed genes, offering excellent noise immunity.
    • MIC exhibits superior robustness and adaptability to various data and environmental conditions compared to existing methods.