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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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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 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

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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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AGEP_TWAS: A Deep Learning-Based Framework for Predicting Gene Expression Levels in Tissues.

Sizhe Wang, Zhichao Zhou, Chen Li

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    Summary
    This summary is machine-generated.

    This study introduces AGEP_TWAS, a novel gene expression prediction method that uses landmark genes to accurately predict gene expression levels across tissues, outperforming existing models. This advance aids in understanding gene function and improving transcriptome-wide association studies (TWAS).

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • Predicting gene expression across tissues is crucial for functional genomics and transcriptome-wide association studies (TWAS).
    • Traditional gene expression prediction methods are inefficient and do not account for missing single nucleotide polymorphisms (SNPs), limiting their predictive power.
    • Landmark genes, which reflect cellular states, offer a promising approach for predicting genome-wide gene expression.

    Purpose of the Study:

    • To develop an advanced gene expression prediction method, AGEP_TWAS, that leverages landmark genes for improved accuracy and efficiency.
    • To address limitations of existing methods, including inefficiency and the handling of missing SNPs.
    • To enhance the utility of TWAS by providing more reliable gene expression predictions.

    Main Methods:

    • AGEP_TWAS employs a dense connection network, adaptive activation functions, and parameter pruning within a nonlinear feature extraction framework.
    • The method utilizes tissue-specific landmark genes to predict expression levels of other genes.
    • The approach was validated on human GEO and CattleGTEx datasets.

    Main Results:

    • AGEP_TWAS achieved a mean squared error (MSE) of 0.1821 and a Pearson correlation coefficient (PCC) of 0.9004 on the human GEO dataset, outperforming state-of-the-art models.
    • The method demonstrated superior predictive performance on the CattleGTEx dataset for inferring gene expression across cattle tissues.
    • A TWAS on cattle milk production traits identified six significant genes associated with the trait, showcasing practical application.

    Conclusions:

    • AGEP_TWAS offers a significant advancement in gene expression prediction, particularly for genes challenging to predict with traditional methods.
    • The method's ability to leverage landmark genes and handle missing SNPs improves predictive accuracy and efficiency.
    • AGEP_TWAS has practical implications for advancing functional genomics, TWAS, and identifying genes related to complex traits.