Expression breadth and expression abundance behave differently in correlations with evolutionary rates

BACKGROUND: One of the main objectives of the molecular evolution and evolutionary systems biology field is to reveal the underlying principles that dictate protein evolutionary rates. Several studies argue that expression abundance is the most critical component in determining the rate of evolution, especially in unicellular organisms. However, the expression breadth also needs to be considered for multicellular organisms. RESULTS: In the present paper, we analyzed the relationship between the two expression variables and rates using two different genome-scale expression datasets, microarrays and ESTs. A significant positive correlation between the expression abundance (EA) and expression breadth (EB) was revealed by Kendall's rank correlation tests. A novel random shuffling approach was applied for EA and EB to compare the correlation coefficients obtained from real data sets to those estimated based on random chance. A novel method called a Fixed Group Analysis (FGA) was designed and applied to investigate the correlations between expression variables and rates when one of the two expression variables was evenly fixed. CONCLUSIONS: In conclusion, all of these analyses and tests consistently showed that the breadth rather than the abundance of gene expression is tightly linked with the evolutionary rate in multicellular organisms.