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Comparative analysis of multiple genome-scale data sets
Margaret Werner-Washburne1, Brian Wylie, Kevin Boyack
1Biology Department, University of New Mexico, Albuquerque, New Mexico 87131, USA. maggieww@unm.edu
Genome Research
|October 9, 2002
Summary
Novel tools reveal yeast gene expression and protein interactions. Analysis shows distinct G(0) exit responses and challenges previous correlations between gene co-expression and protein interactions.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Genome-scale data analysis requires advanced methodologies.
- Existing tools may not fully capture complex biological datasets.
- Yeast gene expression and protein interaction data offer valuable insights into cellular processes.
Purpose of the Study:
- To introduce novel visualization and comparison tools for genome-scale data.
- To analyze yeast cell cycle and G(0) exit gene expression data.
- To compare gene expression data with protein interaction datasets.
Main Methods:
- Utilized new tools for data visualization and comparison.
- Analyzed yeast gene expression datasets (cell cycle, G(0) exit).
- Compared two yeast protein-interaction datasets with gene expression data.
Main Results:
- Identified distinct gene expression patterns during G(0) exit, separable from cell cycle events.
- Ribosomal protein genes show different clustering in cell cycle versus G(0) exit datasets.
- Found no clear correlation between gene co-expression and protein interactions across complete datasets.
Conclusions:
- Novel tools provide new insights into yeast biological processes.
- G(0) exit involves distinct physiological responses from cell cycle events.
- Current protein-interaction data, particularly for ribosomal proteins, may have fewer false positives than previously assumed.