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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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...
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Updated: Feb 27, 2026

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
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GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles.

Michael I Klein1, David F Stern2, Hongyu Zhao3

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA.

BMC Bioinformatics
|June 28, 2017
PubMed
Summary
This summary is machine-generated.

Gene-Ranking Analysis of Pathway Expression (GRAPE) identifies abnormal pathways in individual cancer samples. This robust method overcomes platform effects, improving personalized treatment strategies and generalizability across datasets.

Keywords:
CancerGene expressionPersonalized medicineRelative expression analysisSurvival analysisTemplate

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Personalized cancer treatment relies on gene expression profiles.
  • Existing methods for identifying perturbed pathways often lack generalizability due to platform/batch effects.
  • A need exists for robust methods to identify sample-specific perturbed pathways.

Purpose of the Study:

  • To introduce Gene-Ranking Analysis of Pathway Expression (GRAPE), a novel method for identifying abnormal pathways in individual samples.
  • To demonstrate GRAPE's robustness against platform/batch effects in gene expression data.
  • To showcase GRAPE's utility in personalized cancer treatment and disease analysis.

Main Methods:

  • GRAPE establishes pathway templates by ranking gene expression levels in reference samples.
  • These templates assess individual sample conformity to normative pathway behavior.
  • Gene expression profiles are represented as pathway scores for analysis.

Main Results:

  • GRAPE demonstrates superior robustness and generalizability across datasets compared to existing methods.
  • The method effectively classifies tissue types within a dataset.
  • GRAPE pathway scores show strong performance in survival analysis for TCGA subtypes.

Conclusions:

  • GRAPE templates provide a robust approach to summarizing gene-set behavior across diverse gene expression profiles.
  • GRAPE pathway scores enable identification of abnormal gene-set behavior in individual samples via a unique non-competitive method.
  • GRAPE is a valuable tool for researchers analyzing individual samples and group differences in gene-set behavior.