myTAI

Paper link install with bioconda Visitors R-CMD-check GitHub release

Evolutionary Transcriptomics with R

library(myTAI)
# obtain an example phylo-expression object
data("example_phyex_set")
# plot away!
myTAI::plot_signature(example_phyex_set)  
myTAI::plot_contribution(example_phyex_set)
myTAI::plot_gene_space(example_phyex_set)
plot_signature function output plot_contribution function output plot_gene_space function output

Detailed documentation provided here

Package summary

Using myTAI, any existing or newly generated transcriptome dataset can be combined with evolutionary information (find details here) to retrieve novel insights about the evolutionary conservation of the transcriptome at hand.

For the purpose of performing large scale evolutionary transcriptomics studies, the myTAI package implements the quantification, statistical assessment, and analytics functionality to allow researchers to study the evolution of biological processes by determining stages or periods of evolutionary conservation or variability in transcriptome data.

We hope that myTAI will become the community standard tool to perform evolutionary transcriptomics studies and we are happy to add required functionality upon request.

Detailed background

In the past years, a variety of studies aimed to uncover the molecular basis of morphological innovation and variation from the evolutionary developmental perspective. These studies often rely on transcriptomic data to establish the molecular patterns driving the complex biological processes underlying phenotypic plasticity.

Although transcriptome information is a useful start to study the molecular mechanisms underlying a biological process of interest (molecular phenotype), they rarely capture how these expression patterns emerged in the first place or to what extent they are possibly constrained, thereby neglecting the evolutionary history and developmental constraints of genes contributing to the overall pool of expressed transcripts.

To overcome this limitation, the myTAI package introduces procedures summarized under the term evolutionary transcriptomics to integrate gene age information into classical gene expression analysis. Gene age inference can be performed with various existing software, but we recommend using GenEra or orthomap, since they address published shortcomings of gene age inference (see detailed discussion here). In addition, users can easily retrieve previously precomputed gene age information via our data package phylomapr.

Evolutionary transcriptomics studies can serve as a first approach to screen in silico for the potential existence of evolutionary and developmental constraints within a biological process of interest. This is achieved by quantifying transcriptome conservation patterns and their underlying gene sets in biological processes. The exploratory analysis functions implemented in myTAI provide users with a standardized, automated and statistically sound framework to detect and analyze patterns of evolutionary constraints in any transcriptome dataset of interest.

Scientific background

Today, phenomena such as morphological mutations, diseases or developmental processes are primarily investigated on the molecular level using transcriptomics approaches. Transcriptomes denote the total number of quantifiable transcripts present at a specific stage in a biological process. In disease or developmental (defect) studies, transcriptomes are usually measured over several time points. In treatment studies aiming to quantify differences in the transcriptome due to biotic stimuli, abiotic stimuli, or diseases usually treatment / disease versus non-treatment / non-disease transcriptomes are compared. In either case, comparing changes in transcriptomes over time or between treatments allows us to identify genes and gene regulatory mechanisms that might be involved in governing the biological process of investigation. Although classic transcriptomics studies are based on an established methodology, little is known about the evolution and conservation mechanisms underlying such transcriptomes. Understanding the evolutionary mechanism that change transcriptomes over time, however, might give us a new perspective on how diseases emerge in the first place or how morphological changes are triggered by changes of developmental transcriptomes.

Evolutionary transcriptomics aims to capture and quantify the evolutionary conservation of genes that contribute to the transcriptome during a specific stage of the biological process of interest. The resulting temporal conservation pattern then enables to detect stages of development or other biological processes that are evolutionarily conserved (Drost et al., 2018). This quantification on the highest level is achieved through transcriptome indices (e.g. Transcriptome Age Index or Transcriptome Divergence Index) which aim to quantify the average evolutionary age Barrera-Redondo et al., 2023 or sequence conservation Drost et al., 2015 of genes that contribute to the transcriptome at a particular stage. In general, evolutionary transcriptomics can be used as a method to quantify the evolutionary conservation of transcriptomes at particular developmental stages and to investigate how transcriptomes underlying biological processes are constrained or channeled due to events in evolutionary history (Dollo’s law) (Drost et al., 2017).

Please note, since myTAI relies on gene age inference and there has been an extensive debate about the best approaches for gene age inference in the last years, please follow my updated discussion about the gene age inference literature. With GenEra, we addressed all previously raised issues and we encourage users to run GenEra when aiming to infer gene ages for further myTAI analyses.

Installation

myTAIv2 is still work in progress. To install the development version, do:

devtools::install_github("drostlab/myTAI")

To install the previous version of myTAI, and access the old vignettes, do:

devtools::install_github("drostlab/myTAI@v1.0")

Soon, users will be able to install myTAI from CRAN:

# install myTAIv2 from CRAN
install.packages("myTAI", dependencies = TRUE)

Citation

Please cite the following paper when using myTAI for your own research. This will allow us to continue working on this software tool and will motivate us to extend its functionality and usability in the next years. Many thanks in advance!

Drost et al. myTAI: evolutionary transcriptomics with R. Bioinformatics 2018, 34 (9), 1589-1590. doi:10.1093

Studies that successfully used myTAI to quantify transcriptome novelty and conservation

NEWS

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.

Tutorials

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

Main: - Getting started - Bring your datasets into myTAI - Statistical testing with myTAI - Transforming dataset for myTAI - Break TAI patterns using gaTAI - Beautiful plots made via myTAI

Advanced: - Gene age inference - Other evolutionary and expression indices

Users can also read the tutorials within (RStudio) :

# source the myTAI package
library(myTAI)

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

Object classes in myTAI

Workflow to load your own dataset:

bulk

PhyEx overview

bulk with replicates

PhyEx replicates overview

single cell

From expression matrix

scPhyEx overview

Or directly from a seurat object:

scPhyEx overview (seurat)

Discussions and Bug Reports

We 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 us know:

https://github.com/drostlab/myTAI/issues