myTAI
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)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.
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.
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
Install myTAIv2:
install.packages("myTAI", dependencies = TRUE)To install the old version of myTAI, and access the old vignettes, do:
devtools::install_github("drostlab/myTAI@v1.0")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
- Evolutionary trends in the emergence of skeletal cell types
A Damatac, II , S Koska , K K Ullrich , T Domazet-Lošo , A Klimovich , M Kaucká… - Evolution Letters, 2025
- Phylostratigraphic analysis revealed that ancient ohnologue PtoWRKY53 innovated a vascular transcription regulatory network in Populus
W Huang, M Quan, W Qi, L Xiao, Y Fang, J Zhou… - New Phytologist, 2025
- Pra-GE-ATLAS: Empowering Pinus radiata stress and breeding research through a multi-omics database
V Roces, MJ Cañal, JL Mateo, L Valledor… - Journal of Integrative Plant Biology, 2025
- Developmental phylotranscriptomics in grapevine suggests an ancestral role of somatic embryogenesis
S Koska, D Leljak-Levanic, N Malenica, K Bigovic Villi… - Communications Biology, 2025
- Proteomic analyses reveal the key role of gene co-option in the evolution of the scaly-foot snail scleritome
WC Wong, YH Kwan, X He, C Chen, S Xiang, Y Xiao… - Communications Biology, 2025
- Genome assembly of Stewartia sinensis reveals origin and evolution of orphan genes in Theaceae
L Cheng, Q Han, Y Hao, Z Qiao, M Li, D Liu… - Communications Biology, 2025
- A transcriptomic hourglass in brown algae
JS Lotharukpong, M Zheng, R Luthringer, D Liesner, H-G Drost, SM Coelho - Nature, 2024
- Genome assemblies of 11 bamboo species highlight diversification induced by dynamic subgenome dominance
PF Ma, YL Liu, C Guo, G Jin, ZH Guo, L Mao, YZ Yang… - Nature Genetics, 2024
- Hemichordate cis-regulatory genomics and the gene expression dynamics of deuterostomes
A Pérez-Posada, CY Lin, TP Fan, CY Lin, YC Chen… - Nature Ecology & Evolution, 2024
- Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota
D Bars-Cortina, E Ramon, B Rius-Sansalvador… - BMC genomics, 2024
- Heat stress reprograms herbivory-induced defense responses in potato plants
J Zhong, J Zhang, Y Zhang, Y Ge, W He, C Liang… - BMC Plant Biology, 2024
- The transcriptomic signature of adaptations associated with perfume collection in orchid bees
K Darragh, SR Ramírez - Journal of Evolutionary Biology, 2024
- Proteomic dynamics revealed sex‐biased responses to combined heat‐drought stress in Marchantia
S Guerrero, V Roces, L García‐Campa, L Valledor… - Journal of Integrative Plant Biology, 2024
- Evolution of gene networks underlying adaptation to drought stress in the wild tomato Solanum chilense
K Wei, S Sharifova, X Zhao, N Sinha, H Nakayama… - Molecular Ecology, 2024
- Conserved and specific gene expression patterns in the embryonic development of tardigrades
C Li, Z Yang, X Xu, L Meng, S Liu, D Yang - Evolution & Development, 2024
- The functions and factors governing fungal communities and diversity in agricultural waters: insights into the ecosystem services aquatic mycobiota provide
P Pham, Y Shi, I Khan, M Sumarah, J Renaud… - Frontiers in Microbiology, 2024
- An evolutionary timeline of the oxytocin signaling pathway
AM Sartorius, J Rokicki, S Birkeland, F Bettella, C Barth… - Communications Biology, 2024
- The Evolution of Foraging Webs is Associated with Young Genes in Araneoidea Spiders
A Jia, T Yang, W Hu, S Ma, Z Zhang, Y Wang - Available at SSRN 4383994
- Multiplexed transcriptomic analyses of the plant embryonic hourglass
H Wu, R Zhang, KJ Niklas, MJ Scanlon - BioRxiv, 2024
- Brachiopod genome unveils the evolution of the BMP–Chordin network in bilaterian body patterning
TD Lewin, K Shimizu, IJY Liao, ME Chen, K Endo… - BioRxiv, 2024
- The angiosperm seed life cycle follows a developmental reverse hourglass
AA Sami, L Bentsink, MAS Artur - BioRxiv, 2024
- Evolutionary trends in the emergence of skeletal cell types
A Damatac, S Koska, KK Ullrich, T Domazet-Loso… - BioRxiv, 2024
Transcriptome age of individual cell types in Caenorhabditis elegans F Ma, C Zheng - Proceedings of the National Academy of Sciences, 2023
Single-cell atlases of two lophotrochozoan larvae highlight their complex evolutionary histories
L Piovani, DJ Leite, LA Yañez Guerra, F Simpson… - Science Advances, 2023
- oggmap: a Python package to extract gene ages per orthogroup and link them with single-cell RNA data
KK Ullrich, NE Glytnasi - Bioinformatics, 2023
- Discovery of putative long non-coding RNAs expressed in the eyes of Astyanax mexicanus (Actinopterygii: Characidae)
I Batista da Silva, D Aciole Barbosa, KF Kavalco… - Scientific Reports, 2023
- An ancient split of germline and somatic stem cell lineages in Hydra
C Nishimiya-Fujisawa, H Petersen, TC-T Koubková-Yu, C Noda, S Shigenobu, J Bageritz, T Fujisawa, O Simakov, S Kobayashi, TW Holstein - BioRxiv, 2023
- Oxytocin receptor expression patterns in the human brain across development
J Rokicki, T Kaufmann, A-MG de Lange, D van der Meer, S Bahrami, AM Sartorius, UK Haukvik, NE Steen, E Schwarz, DJ Stein, T Nærland, OA Andreassen, LT Westlye, DS Quintana - Neuropsychopharmacology, 2022
The Phylotranscriptomic Hourglass Pattern in Fungi: An Updated Model Y Xie, HS Kwan, PL Chan, WJ Wu, J Chiou, J Chang BioRxiv, 2022
Embryo-Like Features in Developing Bacillus subtilis Biofilms M Futo, L Opašić, S Koska, N Čorak, T Široki, V Ravikumar, A Thorsell, M Lenuzzi, D Kifer, M Domazet-Lošo, K Vlahoviček, I Mijakovic, T Domazet-Lošo - Molecular Biology and Evolution, 2021
New Genes Interacted With Recent Whole-Genome Duplicates in the Fast Stem Growth of Bamboos G Jin, P-F Ma, X Wu, L Gu, M Long, C Zhang, DZ Li - Molecular Biology and Evolution, 2021
Evolutionary transcriptomics of metazoan biphasic life cycle supports a single intercalation origin of metazoan larvae
J Wang, L Zhang, S Lian, Z Qin, X Zhu, X Dai, Z Huang et al. - Nature Ecology & Evolution, 2020
- Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis
Y Draceni, S Pechmann - Proceedings of the National Academy of Sciences, 2019
- Reconstructing the transcriptional ontogeny of maize and sorghum supports an inverse hourglass model of inflorescence development
S Leiboff, S Hake - Current Biology, 2019
- The Transcriptional Landscape of Polyploid Wheats and their Diploid Ancestors during Embryogenesis and Grain Development
D Xiang, TD Quilichini, Z Liu, P Gao, Y Pan et al. - The Plant Cell, 2019
- Pervasive convergent evolution and extreme phenotypes define chaperone requirements of protein homeostasis
Y Draceni, S Pechmann - Proceedings of the National Academy of Sciences, 2019
- A unicellular relative of animals generates a layer of polarized cells by actomyosin-dependent cellularization
O Dudin, A Ondracka, X Grau-Bové, AAB Haraldsen et al. - eLife, 2019
- Gene Expression Does Not Support the Developmental Hourglass Model in Three Animals with Spiralian Development
L Wu, KE Ferger, JD Lambert - Molecular Biology and Evolution, 2019
- Phylostratr: a framework for phylostratigraphy
Z Arendsee, J Li, U Singh, A Seetharam et al. - Bioinformatics, 2019
- Algorithms for synteny-based phylostratigraphy and gene origin classification
Z Arendsee - 2019
- Elucidating the endogenous synovial fluid proteome and peptidome of inflammatory arthritis using label-free mass spectrometry
SM Mahendran, EC Keystone, RJ Krawetz et al. - Clinical proteomics, 2019
- Environmental DNA reveals landscape mosaic of wetland plant communities
ME Shackleton, GN Rees, G Watson et al. - Global Ecology and Conservation, 2019
- Developmental constraints on genome evolution in four bilaterian model species
J Liu, M Robinson-Rechavi - Genome Biology and Evolution, 2018
- Mapping selection within Drosophila melanogaster embryo's anatomy
I Salvador-Martínez et al. - Molecular Biology and Evolution, 2017
- Distribution and diversity of enzymes for polysaccharide degradation in fungi
R Berlemont - Scientific reports, 2017
- The origins and evolutionary history of human non-coding RNA regulatory networks
M Sherafatian, SJ Mowla - Journal of bioinformatics and computational biology, 2017
- High expression of new genes in trochophore enlightening the ontogeny and evolution of trochozoans
F Xu, T Domazet-Lošo, D Fan, TL Dunwell, L Li et al. - Scientific reports, 2016
- Evidence for active maintenance of phylotranscriptomic hourglass patterns in animal and plant embryogenesis
HG Drost, A Gabel, I Grosse, M Quint - Molecular Biology and Evolution, 2015
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:
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
bulk with replicates
single cell
From expression matrix
Or directly from a seurat object:
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: