Data from: ERC2.0-evolutionary rate covariation update provides more powerful inference of functional interactions across large phylogenies

Evolutionary Rate Covariation (ERC) is an established comparative genomics
method that identifies sets of genes sharing patterns of sequence
evolution, which suggests shared function. Whereas many functional
predictions of ERC have been empirically validated, its predictive power
has hitherto been limited by its inability to tackle the large numbers of
species in contemporary comparative genomics datasets. This study
introduces ERC2.0, an enhanced methodology for studying ERC across
phylogenies with hundreds of species and tens of thousands of genes.
ERC2.0 improves upon previous iterations of ERC in algorithm speed,
normalizing for heteroskedasticity, and normalizing correlations via
Fisher transformations. These improvements have resulted in greater
statistical power to predict biological function. In exemplar yeast and
mammalian datasets, we demonstrate that the predictive power of ERC2.0 is
improved relative to the previous method, ERC1.0, and that further
improvements are obtained by using larger yeast and mammalian phylogenies.
We attribute the improvements to both the larger datasets and improved
rate normalization. We demonstrate that ERC2.0 has high predictive
accuracy for known annotations and can predict the functions of genes in
non-model systems. Our findings underscore the potential for ERC2.0 to be
used as a single-pass computational tool in candidate gene screening and
functional predictions.