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Updated: Jun 3, 2026
09:45
Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response
Published on: August 10, 2017
Complex principal component and correlation structure of 16 yeast genomic variables
Fabian J Theis1, Nadia Latif, Philip Wong
1Helmholtz Center Munich-German Research Center for Environmental Health, Institute of Bioinformatics and Systems Biology, Ingolstädter Landstraße 1, Neuherberg, Germany.
Molecular Biology and Evolution
|March 30, 2011
Summary
Bayesian Principal Component Analysis (PCA) reveals new genomic variable correlations in Saccharomyces cerevisiae, linking translational efficiency, phosphorylation density, and protein age. This study enhances understanding of molecular evolution and introduces the Quagmire database.
Area of Science:
- Genomics
- Molecular Evolution
- Bioinformatics
Background:
- Quantitative genomic variables offer insights into gene function and evolution.
- Multidimensional analysis of these variables is crucial for understanding molecular evolution.
Purpose of the Study:
- To perform a comprehensive Principal Component Analysis (PCA) on 16 genomic variables in Saccharomyces cerevisiae.
- To apply Bayesian PCA to address missing values and outliers in large genomic datasets.
- To identify novel correlations between genomic variables and influential gene classes.
Main Methods:
- Principal Component Analysis (PCA) on 16 genomic variables.
- Bayesian PCA for handling missing data and outliers.
- Enrichment analysis of genes influencing variable correlations.
Main Results:
- Confirmed known correlations (e.g., evolutionary rate vs. protein expression).
- Discovered new correlations: translational efficiency, phosphorylation density, and protein age.
- Identified distinct patterns in principal components related to genomic change, protein expression, gene existence, and protein function.
- Highlighted influential gene classes (ribosomal, nuclear transport, protein synthesis, amino acid metabolism) impacting specific correlations.
Conclusions:
- Bayesian PCA is effective for analyzing complex genomic datasets.
- New insights into molecular evolution through identified genomic variable relationships.
- The Quagmire database provides a resource for exploring genomic variable relationships across multiple model organisms.
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