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Related Experiment Videos

Genome-wide pathway analysis and visualization using gene expression data.

M P Kurhekar1, S Adak, S Jhunjhunwala

  • 1Bioinformatics Group, IBM India Research Lab, Block 1, Indian Institute of Technology, Hauz Khas, New Delhi, India 110016. mkurhekar,asudeshn@in.ibm.com

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|April 4, 2002
PubMed
Summary

This study introduces a novel visualization method for complex gene expression data from cDNA microarrays. The approach uses multi-resolution animation to analyze metabolic and regulatory pathways, aiding biological interpretation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-performance computing generates vast datasets, posing visualization challenges.
  • Analyzing gene expression data in biological pathways requires sophisticated methods.

Purpose of the Study:

  • To develop an effective visualization approach for cDNA microarray gene expression data.
  • To characterize biological pathways using gene expression, coregulation, and cascade effects.
  • To assess the biological significance of pathway scores.

Main Methods:

  • Multi-resolution animation for visualizing gene expression data at varying detail levels.
  • Development of three scoring functions to analyze pathways at the transcriptional level.
  • Assessment of pathway score significance based on biological context.

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Main Results:

  • A new method for visualizing complex gene expression data in pathways was established.
  • Scoring functions effectively characterized pathways based on transcriptional data.
  • Biological significance of pathway scores was successfully evaluated.

Conclusions:

  • The proposed visualization technique enhances the analysis of high-performance computing data for gene expression.
  • The scoring functions provide a robust framework for pathway characterization and biological interpretation.