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 ..., , , # ERC2.0 data for 343 yeast and 120 mammals

[Access this dataset on Dryad](10.5061/dryad.6m905qg8q)

This dataset contains output files for the analyses found in
“ERC2.0-evolutionary rate covariation update provides more powerful
inference of functional interactions across large phylogenies”. These
files include ERC matrices calculated using a phylogeny of 343 yeast
species and 120 mammal species. This repository also includes Cytoscape
networks for high scoring ERC pairs genome-wide and focused on the
histone chaperone network.

## Description of the data and file structure

Table_S1_ERC_runtime_comparison: excel file showing the comparison
of run times for a subset of 500 genes between ERC1.0 and ERC2.0

#### ERC\_matrices:

yeast_ftERC.RDS mammal_ftERC.RDS

These are the full genome matrices calculated on a phylogeny of 343
yeast species and 120 mammal species respectively.

#### glmnet\_analysis\_output: the glmnet objects generated from running cv.glmnet on various iterations of E...,