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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...

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Obtaining High-Quality Transcriptome Data from Cereal Seeds by a Modified Method for Gene Expression Profiling
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A robust method for estimating gene expression states using Affymetrix microarray probe level data.

Megu Ohtaki1, Keiko Otani, Keiko Hiyama

  • 1Department of Environmetrics and Biometrics, Research Institute for Radiation Biology and Medicine, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. ohtaki@hiroshima-u.ac.jp

BMC Bioinformatics
|April 13, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to determine if a gene is expressed or unexpressed, improving microarray data analysis by reducing false discoveries and identifying biologically meaningful gene expression states.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Molecular Biology

Background:

  • Microarray technology measures gene expression but is affected by non-specific binding, leading to noise.
  • The mismatch (MM) probe on Affymetrix GeneChips aims to quantify non-specific binding, but its utility is debated.
  • Many observed intensities in microarray data represent noise from unexpressed genes, impacting analysis accuracy.

Purpose of the Study:

  • To develop a robust method for estimating gene expression states (expressed or unexpressed).
  • To utilize the relationship between perfect match (PM) and mismatch (MM) probe measures for improved accuracy.
  • To enhance the reliability of microarray data analysis by distinguishing true expression signals from noise.

Main Methods:

  • Proposed a method using the order relationship between PM and MM probe measures.
  • Developed an indicator: 'probability of a gene being expressed', based on the count of probe pairs where PM exceeds MM.
  • Validated the method using Affymetrix Human Genome U95 data sets.

Main Results:

  • Successfully generated the 'probability of a gene being expressed' indicator.
  • Demonstrated the indicator's utility in identifying candidate genes with differing expression states in neuroblastoma prognosis.
  • Confirmed the validity of the identified gene expression states using quantitative RT-PCR.

Conclusions:

  • The 'probability of a gene being expressed' is a valuable qualitative evaluation for enhancing microarray data analysis.
  • This method effectively reduces false discoveries, leading to more reliable results.
  • Distinguishing between expressed and unexpressed gene states provides biologically meaningful insights, akin to gene 'On' and 'Off' functions.