English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

myTAI: evolutionary transcriptomics with R

MPS-Authors
There are no MPG-Authors in the publication available
External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Drost, H.-G., Gabel, A., Liu, J., Quint, M., & Grosse, I. (2018). myTAI: evolutionary transcriptomics with R. Bioinformatics, 34(9), 1589-1590. doi:10.1093/bioinformatics/btx835.


Cite as: https://hdl.handle.net/21.11116/0000-000C-B7EA-E
Abstract
Motivation: Next Generation Sequencing (NGS) technologies generate a large amount of high quality transcriptome datasets enabling the investigation of molecular processes on a genomic and metagenomic scale. These transcriptomics studies aim to quantify and compare the molecular phenotypes of the biological processes at hand. Despite the vast increase of available transcriptome datasets, little is known about the evolutionary conservation of those characterized transcriptomes.
Results: The myTAI package implements exploratory analysis functions to infer transcriptome conservation patterns in any transcriptome dataset. Comprehensive documentation of myTAI functions and tutorial vignettes provide step-by-step instructions on how to use the package in an exploratory and computationally reproducible manner.
Availability and implementation: The open source myTAI package is available at https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html.