RERconverge is a set of software written in R that estimates the correlation between relative evolutionary rates of genes and the evolution of a convergent binary or continuous trait across a phylogeny.
Please refer to the Install page for detailed instructions to install RERconverge from scratch. For more information on running RERconverge, please see the full documentation and R vignettes for a nice step-by-step tutorial.
library(devtools)
install_github("nclark-lab/RERconverge")
To run an analysis you will need: 1) a trees file: a tab-delimited files with gene names and Newick format trees for each gene. Tree topologies must be the same for all genes, and at least one tree must contain all species in the dataset. We provide trees files for several clades here.
3) information about phenotypes for species included in the dataset.
For a binary trait analysis, this can either be in the form of:
For a continuous trait analysis, this should be:
Running RERconverge will produce the following outputs: 1) an object containing, for each gene, the correlation between its relative evolutionary rate and the trait of interest, along with the estimated p-value and FDR 2) an object containing, for each gene, its relative evolutionary rate for each branch of the phylogeny, which can be used in the included visualization scripts (e.g., to illustrate the difference in relative evolutionary rate between foreground and background branches)
See also the list of contributors who participated in this project.
RERconverge can be cited as follows:
Kowalczyk A, Meyer WK, Partha R, Mao W, Clark NL, Chikina M. RERconverge: an R package for associating evolutionary rates with convergent traits. Bioinformatics. 2019;35(22): 4815–4817, https://doi.org/10.1093/bioinformatics/btz468
Partha R, Kowalczyk A, Clark N, Chikina M. Robust methods for detecting convergent shifts in evolutionary rates. Mol Biol Evol. 2019;36(8): 1817–1830. https://doi.org/10.1093/molbev/msz107
The following are the first demonstrations of analyses using the methods in RERconverge:
Chikina M, Robinson JD, Clark NL. Hundreds of Genes Experienced Convergent Shifts in Selective Pressure in Marine Mammals. Mol Biol Evol. 2016;33: 2182–92. doi:10.1093/molbev/msw112
Partha R, Chauhan B, Ferreira Z, Robinson J, Lathrop K, Nischal K, et al. Subterranean mammals show convergent regression in ocular genes and enhancers, along with adaptation to tunneling. eLife 2017;6:e25884. https://doi.org/10.7554/eLife.25884
This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details
Law CW, Chen Y, Shi W, Smyth GK. voom: Precision weights unlock linear model analysis tools for RNA-seq read
counts. Genome Biol. 2014;15: R29. doi:10.1186/gb-2014-15-2-r29
* Projection operations are drawn from the following paper:
Sato T, Yamanishi Y, Kanehisa M, Toh H. The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan. sato@kuicr.kyoto-u.ac.jp; 2005;21: 3482–3489. doi:10.1093/bioinformatics/bti564
* Thanks to [PurpleBooth](https://github.com/PurpleBooth) for this template.