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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...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Automated multidimensional phenotypic profiling using large public microarray repositories.

Min Xu1, Wenyuan Li, Gareth M James

  • 1Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA.

Proceedings of the National Academy of Sciences of the United States of America
|July 11, 2009
PubMed
Summary
This summary is machine-generated.

PhenoProfiler is a new computational method for in silico phenotype profiling. It accurately predicts complex phenotypes from gene expression data, aiding disease analysis and understanding the transcriptome-phenome link.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Phenotypes are complex and challenging to quantify, hindering genotype-phenotype mapping.
  • Lack of comprehensive phenotype data can lead to distorted biological inferences.

Purpose of the Study:

  • To introduce PhenoProfiler, a computational method for in silico phenotype profiling.
  • To supplement missing quantitative phenotype information using gene expression data.

Main Methods:

  • PhenoProfiler leverages the principle that similar gene expression patterns correlate with similar phenotype patterns.
  • It integrates data from multiple microarray datasets to predict phenotype profiles.
  • The method uses cross-platform microarray data without direct value comparisons.

Main Results:

  • PhenoProfiler was applied to 587 human microarray datasets (>14,000 samples).
  • Predicted phenotype profiles showed high consistency with true phenotype descriptions.
  • The method enables automated, multidimensional profiling and extrapolation beyond known classes.

Conclusions:

  • PhenoProfiler facilitates analysis and treatment design for complex diseases.
  • It detects confounding phenotype factors, improving biological inference accuracy.
  • A compendium of phenotype profiles for NCBI GEO datasets was generated, aiding transcriptome-phenome mapping.