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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.
Cis-acting Elements involved in mRNA stability
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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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RNA Stability01:53

RNA Stability

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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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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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Related Experiment Video

Updated: Mar 27, 2026

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

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Extracting reliable gene expression signatures through Stable Bootstrap Validation.

N K Chlis, E S Bei, K Moirogiorgou

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new bioinformatics framework to identify stable genomic signatures from DNA Microarray data. It addresses the instability of traditional methods, enhancing the reliability of cancer gene discovery.

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    Area of Science:

    • Bioinformatics
    • Genomics
    • Computational Biology

    Background:

    • Identifying genes linked to phenotypes like cancer is challenging.
    • Traditional methods for DNA Microarray analysis lack stability, impacting research reliability.
    • Instability in candidate gene lists raises doubts about findings.

    Purpose of the Study:

    • To propose a novel framework for extracting stable genomic signatures.
    • To enhance the reliability of gene identification from DNA Microarray data.
    • To assess the statistical significance of selected gene sets.

    Main Methods:

    • Developed a framework enforcing stability during the validation step.
    • Ensured method independence from specific feature selection or classification techniques.
    • Assessed the statistical significance of the identified genomic signatures.

    Main Results:

    • The proposed framework successfully extracts stable genomic signatures.
    • Stability was enforced independently of the chosen feature selection and classification methods.
    • The statistical significance of the selected gene sets was rigorously assessed.

    Conclusions:

    • Stability is a critical factor in genomic signature identification.
    • The proposed framework improves the reliability of candidate gene discovery.
    • Genomic signature stability is crucial beyond mere prediction accuracy.