Travis-CI Build Status

Performing Phylotranscriptomics with R

The myTAI package allows users to capture evolutionary signals in developmental transcriptomes using a phylotranscriptomic approach.

Phylotranscriptomics defines the concept of combining genetic sequence information with gene expression levels to capture evolutionary signals during development (Domazet-Loso and Tautz, 2010; Quint et al., 2012; Drost et al., 2015).

This subfield of Evolutionary Developmental Biology aims to investigate stages or periods of evolutionary conservation or constraint in developmental processes of extant species.

myTAI provides easy to use and optimized functions to perform phylostrancriptomic analyses for any annotated organism and developmental process of interest. Additionally, a customized visualization framework implemented in myTAI allows users to generate publication quality plots of their custom phylotranscriptomic analyses.


These tutorials introduce users to myTAI:

Users can also read the tutorials within (RStudio) :


Users can download myTAI from CRAN :

# install myTAI 0.2.1 from CRAN
                 repos        = "",
                 dependencies = TRUE,
                 type         = "source")

Package Dependencies

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

Getting started with myTAI

# 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:

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

Quint M. 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.

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.