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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...

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Related Experiment Video

Updated: May 16, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
09:45

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An order estimation based approach to identify response genes for microarray time course data.

Zhiheng K Lu1, O Brian Allen, Anthony F Desmond

  • 1Metastract Inc.

Statistical Applications in Genetics and Molecular Biology
|December 18, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to analyze gene expression data from microarray time course experiments. It accounts for both treatment and gene context effects, identifying response genes crucial for understanding cellular systems and treatment impacts.

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Last Updated: May 16, 2026

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Microarray time course experiments offer insights into genome-wide gene responses.
  • Gene expression is influenced by both external treatments and complex gene regulatory interactions (gene context effect).
  • Isolating the gene context effect, which can vary with treatment, is challenging but biologically significant.

Purpose of the Study:

  • To develop an approach that addresses confounding effects in gene expression analysis.
  • To incorporate uncontrollable gene context information into the analysis of microarray time course data.
  • To identify and categorize response genes that are coordinated by cellular systems.

Main Methods:

  • Utilizing a hidden Markov model (HMM) to estimate the number of hidden states.
  • Modeling individual gene expression using a gamma distribution dependent on hidden states at each time point.
  • Categorizing genes with multiple hidden states as signaling or response genes.

Main Results:

  • The proposed method effectively handles confounding treatment and gene context effects.
  • Genes exhibiting multiple hidden states are identified as response genes.
  • The approach allows for the investigation of gene context effects across different treatment conditions.

Conclusions:

  • The developed method provides a robust framework for analyzing complex gene expression patterns.
  • Identified response genes are valuable for comparing treatment conditions and understanding cellular responses.
  • This approach enhances the biological interpretation of microarray time course data.