The effects of differential gene expression on coding sequence features: analysis by one-way ANOVA.
It is well-established that non-random patterns in coding DNA sequence (CDS) features can be partially explained by translational selection. Recent extensions of microarray and proteomic expression data have stimulated many genome-wide investigations of the relationships between gene expression and various CDS features. However, only modest correlations have been found. Here we introduced the one-way ANOVA, a more powerful extension of previous grouping methods, to re-examine these relationships at the whole genome scale for Saccharomyces cerevisiae, where genome-wide protein abundance has been recently quantified. Our results clarify that coding sequence features are inappropriate for use as genome-wide estimators for protein expression levels. This analysis also demonstrates that one-way ANOVA is a powerful and simple method to explore the influence of gene expression on CDS features.
Wu
Gang
G
Department of Biological Sciences, University of Maryland at Baltimore County, Baltimore, MD 21250, USA. wug1@umbc.edu
Nie
Lei
L
Freeland
Stephen J
SJ
D000704:Analysis of Variance; D002874:Chromosome Mapping / Q000379:methods*; D015870:Gene Expression / Q000235:genetics*; D016681:Genome, Fungal / Q000235:genetics*; D016366:Open Reading Frames / Q000235:genetics*; D020543:Proteome / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D013223:Statistics as Topic
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins