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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.
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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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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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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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Noise model estimation with application to gene expression.

Polina Zhornikova1, Nina Golyandina1, Alexander Spirov2

  • 1* Faculty of Mathematics and Mechanics, St. Petersburg State University, Universitetskaya Nab. 7/9, 199034 St. Petersburg, Russia.

Journal of Bioinformatics and Computational Biology
|May 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces algorithms to estimate noise levels and detect noise models in gene expression data from Drosophila embryos. The methods help distinguish biological noise from processing errors, improving data analysis accuracy.

Keywords:
Nucleus-to-nucleus variabilitygene expressionnoise modelsingular spectrum analysis

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Gene expression data analysis requires accurate noise estimation.
  • Distinguishing biological variation from technical noise is crucial for reliable results.
  • Existing methods may not adequately address processing errors in extracted data.

Purpose of the Study:

  • To propose algorithms for estimating noise levels and detecting noise models in gene expression data.
  • To develop methods for separating processing errors from biological noise in 1D gene expression profiles.
  • To discriminate between additive and multiplicative noise models in both 1D and 2D gene expression data.

Main Methods:

  • Development of algorithms for noise level estimation and noise model detection.
  • Application of singular spectrum analysis (SSA) and its 2D extension for pattern extraction.
  • Construction of an algorithm to separate processing errors from biological noise in 1D data.
  • Testing algorithms on artificial data simulating real gene expression patterns.

Main Results:

  • Algorithms successfully estimated noise levels and identified noise models in Drosophila embryo gene expression data.
  • A method for separating processing errors from biological noise was effective for 1D data.
  • Discrimination between additive and multiplicative noise models was achieved for both 1D and 2D data.
  • Comparison of 1D and 2D methods showed distinct results for Krüppel and giant genes.

Conclusions:

  • The proposed algorithms provide robust tools for noise analysis in gene expression data.
  • Accurate noise estimation and model detection enhance the reliability of biological interpretations.
  • The developed methods are applicable to both 1D and 2D gene expression datasets, offering flexibility in analysis.