DOI: 10.5061/dryad.6m905qg8q
title: Data from: ERC2.0-evolutionary rate covariation update provides more powerful inference of functional interactions across large phylogenies

description: 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. 
description: # 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 ERC
 datasets. glmnet_urls: the urls used to call the various annotation
 databases: KEGG and GO Biological processes glmnet_analysis: R script to
 generate a glmnet object for each annotation in a given dataset downloaded
 from url glmnet_analysis_output/yeast: output from cv.glmnet for GO
 biological processes and KEGG for various iterations of ERC calcuated with
 yeast phylogenies. The files are named as such: database_species
 number_erc iteration.RDS glmnet_analysis_output/mammal: output from
 cv.glmnet for GO biological processes and KEGG for various iterations of
 ERC calculated with mammalian phylogenies. The files are named as such:
 database_species number_erc iteration.RDS
 non_cerevisiae_predicted_local.xlsx: excel file showing non-cerevisiae
 orthologs and their subcellular localization predicted by glmnet trained
 on the 343 yeast ERC matrix. The predicted subcellular localizations are
 listed in seperate tabs. Each row is a gene predicted to that localization
 with the name in the first column, and the BLAST result from a
 non-cerevisiae ortholog in the second column #### network\_data: ERC and
 GO Biological process annotation data for clusters generated in Cytoscape
 Figure5_yeast_FtERC_clusters: Full list of GO Biological processes
 enrichment terms for the clusters shown in Figure 5. Each tab represents
 the enriched GO Biological process terms for a given cluster. The GO BP
 term enrichment scores and p-values are recorded in columns 2-7.
 Figure6_Mammalian_FtERC_clusters: Full list of GO Biological processes
 enrichmnent terms for the clusters shown in Figure 6. Each tab represents
 the enriched GO Biological process terms for a given cluster. The GO BP
 term enrichment scores and p-values are recorded in columns 2-7.
 SuppFile4_ERCdataZscoreHistoneChaperonesCoreAll.xlsx: ERC scores between
 the histone chaperone proteins displayed in Figure 7. The first and fourth
 columns represent the manually curated annotation group as either a number
 (column 1) or the histone-chaperone protein it is associated with (column
 4). The second column shows the gene name. The third column shows the ERC
 value between the genes listed in column 2 and column 4. Columns 5 and 6
 show the Z-score of the ERC value for a given pair. ####
 cytoscape\_networks: cys files for cytoscape networks in Figures 5-7
 yeast_clusters.cys: Cytoscape networks for top 0.1% of yeast scores as
 shown in Figures 5, S4 mammal_clusters: Cytoscape networks for top 0.1% of
 mammal scores as shown in Figures 6, S5
 EloEvoHistoneChaperonesCore3ManualAnnotationNoERC.cys: Cytoscape network
 for the histone chaperone network displayed in Figure 7 ## Code/Software
 [Software to run ERC2.0 can be found on
 GitHub](https://github.com/nclark-lab/erc)