Author(s): Natalie Cooper
This repository contains all the code and some data used in the [paper](TO ADD link).
To cite the paper:
Marcello Mezzasalma, Jeffrey W. Streicher, Fabio M. Guarino, Marc E.H. Jones, Simon P. Loader, Gaetano Odierna, and Natalie Cooper 2023. Microchromosome fusions underpin convergent evolution of chameleon karyotypes. Evolution. In press.
To cite this repo:
Natalie Cooper. 2023. GitHub: nhcooper123/chameleon-chromosomes: code for the paper. Zenodo. DOI: 10.5281/zenodo.7846270.
All raw and cleaned data are available from the NHM Data Portal.
For reproducibility purposes, if you want to rerun the data wrangling steps in script analyses\01-data-wrangling, download CHROMO_STATES_2020-10-16.csv and place it into a raw-data/ folder along with the dataset of Meiri 2018.
The cleaned datasets, along rest of the ancillary data required to run all analyses and produce all figures is within the data folder.
- Bayesian-tree-species.tre - tree needed for the main analyses in the paper
- chromosome-data-species.csv - data needed for the main analyses in the paper
- Bayesian-tree.tre - tree for the all OTUs analyses in the supplementary materials
- chromosome-data.csv - data needed for the all OTUs analyses in the supplementary materials
- chromevol-simulations-outputs-best.csv - outputs from the "best" model of chromosome evolution used in the main text. The equivalent for the all OTUs analyses is in the folder
data/OTUs. - null-simulations-outputs.csv - outputs from the null simulations using the best model of chromosome evolution used in the main text. The equivalent for the all OTUs analyses is in the folder
data/OTUs.
If you use the cleaned data please cite as follows:
Marcello Mezzasalma, Jeffrey W. Streicher, Fabio M. Guarino, Marc E.H. Jones, Simon P. Loader, Gaetano Odierna, and Natalie Cooper 2022. CHROMREP [Data set]. Natural History Museum. https://doi.org/10.5519/rmaopg7d.
The life-history data all comes from Meiri 2018 so please cite this paper if you use it.
Meiri, S. Traits of lizards of the world: Variation around a successful evolutionary design. Global Ecol Biogeogr. 2018; 27: 1168– 1172
The majority of analyses run in R, but we also used ChromEvol, a stand alone package available from http://chromevol.tau.ac.il/. The associated paper for the package is:
Lior Glick, Itay Mayrose, ChromEvol: Assessing the Pattern of Chromosome Number Evolution and the Inference of Polyploidy along a Phylogeny, Molecular Biology and Evolution, Volume 31, Issue 7, July 2014, Pages 1914–1922, https://doi.org/10.1093/molbev/msu122
To run these analyses you will need to download ChromEvol and save it in the chromEvol/ folder. This unzips into a folder called chromEvol_source/ that contains the package as an executable called chromEvol. I have not uploaded this to the repo because it's not my package.
ChromEvol analyses require a dataset, a tree and a control file. These are within the chromEvol/ folder preceded by data_for_chromevol or tree_for_chroevol or with the word control somewhere in their name respectively. Several versions exist for the different sensitivity analyses. These analyses save their outputs into the chromEvol/results folder, in folders named based on the names of the control files. Full details of how to build control files is in the ChromEvol manual at http://chromevol.tau.ac.il/. We provide code for creating appropriate datasets and trees in analyses/02-
Running chromEvol analyses is simple. Just open a Terminal in the chromEvol folder and run:
chromEvol_source/chromEvol name-of-control-file
You can run all analyses from our paper using the shell script: run_all_models.sh.
The chromEvol/ folder also includes a folder called root_freq. This has the three different root frequency definitions used in the different ChromEvol analyses (see text).
Finally the OTUs folder within chromEvol/ contain the same things but for analyses using all OTUs (found in the supplementary materials of paper).
The majority of analyses run in R, but we also used ChromoSSE, which runs within RevBayes. The associated paper for ChromoSSE is:
Freyman W.A., Höhna S. 2018. Cladogenetic and anagenetic models of chromosome number evolution: a Bayesian model averaging approach. Systematic Biology. 67:1995–215.
And for RevBayes:
Höhna, Landis, Heath, Boussau, Lartillot, Moore, Huelsenbeck, Ronquist. 2016. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Systematic Biology, 65:726-736.
To run these analyses you will need to download RevBayes. This unzips into a folder called rb that contains the package as an executable called chromEvol. I have not uploaded this to the repo because it's not my package.
ChromoSSE analyses require a dataset, a tree and a control file. These are within the chromEvol/ folder preceded by data_for_chromevol or tree_for_chroevol or with the word control somewhere in their name respectively. Several versions exist for the different sensitivity analyses. These analyses save their outputs into the chromEvol/results folder, in folders named based on the names of the control files. Full details of how to build control files is in the ChromEvol manual at http://chromevol.tau.ac.il/. We provide code for creating appropriate datasets and trees in analyses/02-
To run the ChromoSSE analyses, open rb and a Terminal window should open. Next set the working directory so RevBayes knows where to look for the data i.e. setwd("YOUR_FOLDER/chameleon-chromosomes/ChromoSSE"). Decide on the number of iterations and then run the code to set this: iterations = 10000. Then run your RevBayes control file (files ending in .Rev) using source, e.g.
source("ChromoSSE_exclude_n18.Rev")
The ChromoSSE/ folder includes RevBayes control files to replicate all models in the chromEvol model set.
These analyses take a long time to run! One model is approximately two weeks of run time.
The analysis code is divided into .Rmd files that run the analyses and plot the figures for each section of the paper/supplementary materials.
Note that several of these scripts use an R package written by N.Cusimano. You can download this here https://www.en.sysbot.bio.lmu.de/people/employees/cusimano/use_r/. The easiest way to use this is to download the package and save the ChromEvol.R file. You can then install these functions in R using source. In my repo I kept this within the chromEvol folder so used the code source("chromEvol/ChromEvol.R") to install the functions.
- 00-fix-tip-labels-bayesian-tree.R changes the tip labels of the Bayesian tree to match the dataset.
- 01-data-wrangling.Rmd wrangles the data into a clean format for analyses.
- 02A-data-for-chromosome-evolution-models_main.Rmd creates data and tree files for the ChromEvol analyses.
- 02B-data-for-chromosome-evolution-models_OTUs.Rmd as above but for all OTUs analyses.
- 03A-visualising-data-on-phylogenies_main.Rmd visualises chromosome properties onto a phylogeny.
- 03B-visualising-data-on-phylogenies_OTUs.Rmd as above but for all OTUs analyses.
- 04A-chromosome-evolution-models.Rmd extract the outputs from the ChromEvol models, but only for the two main models of interest (constant rates and linear rates).
- 04A-supp-chromosome-evolution-models-all_main.Rmd extract the outputs from the ChromEvol models for all models.
- 04B-chromosome-evolution-models_OTUs.Rmd as 4A above but for all OTUs analyses.
- 04B-supp-chromosome-evolution-models-all_OTUs.Rmd as 4A-supp above but for all OTUs analyses.
- 05A-micro-macro-correlates-analyses_main.Rmd GLMs of haploid numbers of chromosomes against number of micro- and macro-chromosomes, numbers of micro- vs numbers of micro-chromosomes, ITS versus haploid numbers of chromosomes, and number of micro- and macro-chromosomes. Plus figures.
- 05B-micro-macro-correlates-analyses_OTUs.Rmd as above but for all OTUs analyses.
- 06A-run-or-collate-simulations_main.Rmd collates simulations outputs from ChromEvol and runs null simulations of haploid chromosome numbers.
- 06B-run-or-collate-simulations_OTUs.Rmd as above but for all OTUs analyses.
- 06C-figures-simulation-models_main.Rmd creates figures using simulations.
- 06D-figures-simulation-models_OTUs.Rmd as above but for all OTUs analyses.
- 07A-taxon-species-pairs_main.Rmd creates plots of species chromosome number distance versus phylogenetic distance.
- 07B-taxon-species-pairs_OTUs.Rmd as above but for all OTUs analyses.
- 08A-ecological-correlates-figures.Rmd creates figures of haploid chromosome number and a range of ecological, life-history and geographical variable for each species. FYI there are no all OTUs equivalents of these analyses as data are only available at the species level.
- 08B-ecological-correlates-analyses.Rmd MCMCglmm models of haploid chromosome number and a range of ecological, life-history and geographical variable for each species. FYI there are no all OTUs equivalents of these analyses as data are only available at the species level.
- 09A-morphology-chromosome-no_main.Rmd correlations of different aspects of chromosome morphology with haploid number of chromosomes.
- 09B-morphology-chromosome-no_OTUs.Rmd as above but for all OTUs analyses.
- 10A-data-for-chromoSSE-models_main.Rmd Extracts data for the ChromoSSE models in RevBayes.
- 11A-ChromoSSE-assess-convergence-get-parameters_main.Rmd. Checks the outputs of the ChromoSSE models for convergence (using traceplots and ESS).
- 12A-ChromoSEE-plotting.R. Plots results from ChromoSSE models to create Figure 7 in the main text.
- 13A-ChromoSEE-comparisons.R. Plots correlated evolution plots to create supplemental figure.
/outputscontains the figures and tables/manuscriptcontains the manuscript materials in LaTeX format
For reproducibility purposes, here is the output of devtools::session_info() used to perform the analyses in the publication.
─ Session info ─────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.0 (2022-04-22)
os macOS Big Sur 11.4
system x86_64, darwin17.0
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Dublin
date 2023-04-19
rstudio 2023.03.0+386 Cherry Blossom (desktop)
pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown)
─ Packages ─────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
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[1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
To rerun all the code with packages as they existed on CRAN at time of our analyses we recommend using the checkpoint package, and running this code prior to the analysis:
checkpoint("2023-04-05")