Perform Evolutionary Transcriptomics with R

To put aside for a moment the matter-of-factness of an exact scientist, I will confess that I frequently have the feeling in my experimental work of holding a dialogue with someone who is considerably brighter than me.

- Hans Spemann

Development is the major process establishing complex life on earth. Hence, studying the evolution of developmental processes allows us to understand the key machanisms that control and constraint the evolution and diversification of complex organisms on this planet. To study the evolution of developmental processes an evolutionary transcriptomics approach (= phylotranscriptomics) has been proposed aiming to quantify the evolutionary conservation of developmental transcriptomes (Drost et al., 2015 Mol. Biol. Evol. ; Drost et al., 2016 Mol. Biol. Evol.).

The myTAI package allows users to capture evolutionary information that is hidden in transcriptomes using an evolutionary transcriptomics approach.

This evolutionary transcriptomics approach (= phylotranscriptomics) defines the concept of combining genetic sequence conservation information with gene expression levels to quantify transcriptome conservation throughout biological processes (Domazet-Loso and Tautz, 2010 Nature ; Quint, Drost et al., 2012 Nature ; Drost et al., 2015 Mol. Biol. Evol. ; Drost et al., 2016 Mol. Biol. Evol.).

This subfield of Evolutionary Developmental Biology aims to determine and investigate stages or periods of evolutionary conservation in biological processes of extant species. However, although motivated by and applied to developmental processes, the myTAI package is implemented to quantify transcriptome conservation in any transcriptome experiment of interest and therefore aims to provide a standard approach to investigate the evolution of biological processes in the context of transcriptome conservation.

In particular, myTAI provides an easy to use and optimized software framework to perform phylostrancriptomic analyses for any annotated organism and developmental process of interest. Additionally, customized visualization functions implemented in myTAI allow users to generate publication quality plots for their own phylotranscriptomics research.

The following tutorials will provide use cases and detailed explainations of how to quantify transcriptome conservation with myTAI and how to interpret the results generated with this software tool.


These tutorials introduce users to myTAI:


The current status of the package as well as a detailed history of the functionality of each version of myTAI can be found in the NEWS section.


Users can download myTAI from CRAN :

# install myTAI 0.4.0 from CRAN
install.packages("myTAI", dependencies = TRUE)

Package Dependencies

# to perform differential gene expression analyses with myTAI
# please install the edgeR package
# install edgeR

Getting started with myTAI

Users can also read the tutorials within (RStudio) :

# source the myTAI package

# look for all tutorials (vignettes) available in the myTAI package
# this will open your web browser

# or as single tutorials

# open tutorial: Introduction to Phylotranscriptomics and myTAI
 vignette("Introduction", package = "myTAI")

# open tutorial: Intermediate Concepts of Phylotranscriptomics
 vignette("Intermediate", package = "myTAI")

# open tutorial: Advanced Concepts of Phylotranscriptomics
 vignette("Advanced", package = "myTAI")

# open tutorial: Age Enrichment Analyses
 vignette("Enrichment", package = "myTAI")
# open tutorial: Gene Expression Analysis with myTAI
 vignette("Expression", package = "myTAI")
 # open tutorial: Taxonomic Information Retrieval with myTAI
 vignette("Taxonomy", package = "myTAI")

In the myTAI framework users can find:

Phylotranscriptomics Measures:

Visualization and Analytics Tools:

A Statistical Framework and Test Statistics:

All functions also include visual analytics tools to quantify the goodness of test statistics.

Differential Gene Expression Analysis

Taxonomic Information Retrieval

Minor Functions for Better Usibility and Additional Analyses

Developer Version of myTAI

The developer version of myTAI might include more functionality than the stable version on CRAN. Hence users can download the current developer version of myTAI by typing:

# The developer version can be installed directly from github:

# install.packages("devtools")

# install developer version of myTAI
install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE)

# On Windows, this won't work - see ?build_github_devtools
# install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE)

# When working with Windows, first you need to install the
# R package: rtools ->
# or consult:

# Afterwards you can install devtools -> install.packages("devtools")
# and then you can run:

devtools::install_github("HajkD/myTAI", build_vignettes = TRUE, dependencies = TRUE)

# and then call it from the library
library("myTAI", lib.loc = "C:/Program Files/R/R-3.1.1/library")


Domazet-Lošo T. and Tautz D. A phylogenetically based transcriptome age index mirrors ontogenetic divergence patterns. Nature (2010) 468: 815-8.

Quint M, Drost HG, et al. A transcriptomic hourglass in plant embryogenesis. Nature (2012) 490: 98-101.

Drost HG, Gabel A, Grosse I, Quint M. Evidence for Active Maintenance of Phylotranscriptomic Hourglass Patterns in Animal and Plant Embryogenesis. Mol. Biol. Evol. (2015) 32 (5): 1221-1231.

Drost HG, Bellstädt J, Ó'Maoiléidigh DS, Silva AT, Gabel A, Weinholdt C, Ryan PT, Dekkers BJW, Bentsink L, Hilhorst H, Ligterink W, Wellmer F, Grosse I, and Quint M. Post-embryonic hourglass patterns mark ontogenetic transitions in plant development. Mol. Biol. Evol. (2016) doi:10.1093/molbev/msw039

Discussions and Bug Reports

I would be very happy to learn more about potential improvements of the concepts and functions provided in this package.

Furthermore, in case you find some bugs or need additional (more flexible) functionality of parts of this package, please let me know:


I would like to thank several individuals for making this project possible.

First I would like to thank Ivo Grosse and Marcel Quint for providing me a place and the environment to be able to work on fascinating topics of Evo-Devo research and for the fruitful discussions that led to projects like this one.

Furthermore, I would like to thank Alexander Gabel and Jan Grau for valuable discussions on how to improve some methodological concepts of some analyses present in this package.

I would also like to thank Master Students: Sarah Scharfenberg, Anne Hoffmann, and Sebastian Wussow who worked intensively with this package and helped me to improve the usability and logic of the package environment.