RT Journal
A1 Little, Jordan H.
A1 Meyer, Guillermo Hoffmann
A1 Grover, Aakash
A1 Francette, Alex Michael
A1 Partha, Raghavendran
A1 Arndt, Karen M.
A1 Smith, Martin
A1 Clark, Nathan
A1 Chikina, Maria
T1 ERC2.0 evolutionary rate covariation update improves inference of functional interactions across large phylogenies
JF Genome Research 
JO Genome Research 
YR 2025 
FD September 01 
VO 35 
IS 9 
SP 2041 
OP 2051 
DO 10.1101/gr.280586.125 
UL http://genome.cshlp.org/content/35/9/2041.abstract 
AB 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 data sets. 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 data sets, 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 data sets and improved rate normalization. We demonstrate that ERC2.0 has high predictive accuracy for known annotations and can predict the functions of genes in nonmodel 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.