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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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Related Experiment Video

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

Pathway analysis in microarray data: a comparison of two different pathway analysis devices in the same data set.

Viktoria Bogner1, Bernd A Leidel, Karl-Georg Kanz

  • 1Chirurgische Klinik und Poliklinik-Innenstadt, Ludwig-Maximilians University, Munich, Germany. Viktoria.Bogner@med.uni-muenchen.de

Shock (Augusta, Ga.)
|October 8, 2010
PubMed
Summary
This summary is machine-generated.

Pathway analysis software for oligonucleotide microarray data yields significantly different results. Researchers must be aware of these discrepancies and potential pitfalls when interpreting biological meaning from extensive datasets.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Oligonucleotide microarray technology generates vast datasets.
  • Interpreting the biological significance of this data presents a significant challenge.
  • Numerous software tools exist for pathway analysis of microarray data.

Purpose of the Study:

  • To compare pathway analysis results from different commercial software.
  • To determine if pathway analyses on the same dataset yield comparable results.
  • To identify potential reasons for any observed discrepancies.

Main Methods:

  • Two commercial pathway analysis programs (GeneGo and Pathway Studio 6) were selected.
  • The same microarray dataset was analyzed using both programs.
  • Analyses focused on top networks, top diseases, and top canonical networks.

Main Results:

  • Significant differences were observed in top networks, with only one shared gene.
  • While some overlap existed for top diseases, results were not uniform.
  • Discrepancies stem from variations in literature extraction, database construction, and network algorithms.

Conclusions:

  • Pathway analysis software offers valuable tools for exploring biological relevance.
  • The lack of standardization in software development leads to substantial result variations.
  • Users should be aware of the potential pitfalls of automated pathway analysis tools.