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Related Concept Videos

Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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 addition of a...

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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Experiment specific expression patterns.

Tobias Petri1, Robert Küfner, Ralf Zimmer

  • 1LMU Munich, Department of Informatics, Munich, Germany. petri@bio.ifi.lmu.de

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|September 17, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces PAttern DEviation SCOring (Padesco), a new method to identify genes with unexpected behavior across experiments. Padesco accurately detects specific gene effects, improving biological discovery in gene expression analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Differential gene expression analysis identifies significant gene changes between experimental groups.
  • Genes are not regulated in isolation; their behavior is influenced by other genes, necessitating co-expression analysis.
  • Existing methods analyze patterns across many experiments to understand gene regulation networks.

Purpose of the Study:

  • To develop a novel method for detecting genes that deviate from expected behavior within a set of experiments.
  • To introduce PAttern DEviation SCOring (Padesco) for identifying previously unseen specific gene effects.
  • To provide a new approach for estimating the experiment-specific behavior of individual genes.

Main Methods:

  • Developed PAttern DEviation SCOring (Padesco), a method utilizing linear regression models.
  • Models are trained on a background set of experiments to predict expected gene behavior.
  • Gene-specific prediction accuracy distributions are computed to identify deviations from expected patterns.

Main Results:

  • Padesco effectively identifies genes exhibiting unexpected behavior.
  • The method provides a novel way to estimate the experiment specificity of genes.
  • Validation procedures demonstrate an average detection accuracy of approximately 85% for specific gene candidates.

Conclusions:

  • Padesco offers a powerful new tool for discovering specific gene effects across diverse experimental conditions.
  • The method enhances the understanding of gene regulation by highlighting deviations from expected co-expression patterns.
  • Accurate identification of specific gene candidates has significant implications for biological research and discovery.