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Related Concept Videos

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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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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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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Structure of a Gene01:30

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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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RNA-Seq Analysis of Differential Gene Expression in Electroporated Chick Embryonic Spinal Cord
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Two-way learning with one-way supervision for gene expression data.

Monica H T Wong1, David M Mutch2, Paul D McNicholas3

  • 1Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4L8, ON, Canada. monica.wong@math.mcmaster.ca.

BMC Bioinformatics
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Summary
This summary is machine-generated.

This study introduces a novel Gaussian mixture model for gene expression biclustering. The method effectively identifies biologically relevant biclusters, enabling blood to serve as a surrogate tissue for biomarker discovery.

Keywords:
BiclusteringBiomarker discoveryFinite mixture modelsMicroarray gene expressionSurrogate tissue

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression data analysis often requires sophisticated biclustering techniques.
  • Biomarker discovery is crucial in various medical research areas.
  • Utilizing surrogate tissues like blood can simplify sample collection for certain analyses.

Purpose of the Study:

  • Introduce a family of parsimonious Gaussian mixture models for gene expression biclustering.
  • Develop a one-way supervised algorithm leveraging prior knowledge for factor loadings.
  • Demonstrate the utility of the model in biomarker discovery using blood as a surrogate tissue.

Main Methods:

  • Employed a mixture of factor analyzers model with a binary, row-stochastic factor loadings matrix.
  • Utilized a variant of the expectation-maximization algorithm for parameter estimation.
  • Selected the best-fitting model using the Bayesian information criterion.

Main Results:

  • Simulation studies confirmed accurate bicluster identification, especially when the number of observation clusters was known.
  • The algorithm successfully identified biologically meaningful biclusters related to insulin resistance in rats.
  • Biologically relevant biclusters associated with immune function were found in human transcriptomics data.

Conclusions:

  • The developed biclustering technique offers a novel approach for biomarker discovery.
  • The model facilitates the use of peripheral blood as a surrogate biopsy material for hard-to-obtain tissues.
  • This method holds promise for advancing biomarker discovery in various research applications.