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Bioinformatics. 2018 May 1;34(9):1589-1590. doi: 10.1093/bioinformatics/btx835.

myTAI: evolutionary transcriptomics with R.

Author information

1
Sainsbury Laboratory Cambridge, University of Cambridge, Cambridge CB2 1LR, UK.
2
Institute of Computer Science, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.
3
Université de Lausanne, Département d'Ecologie et d'Evolution, Quartier Sorge, 1015 Lausanne, Switzerland.
4
Institute of Agricultural and Nutritional Sciences, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany.
5
German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany.

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.

Contact:

hgd23@cam.ac.uk.

Supplementary information:

Supplementary data are available at Bioinformatics online.

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