myTAI – Evolutionary Transcriptomics with R

Posted by: RNA-Seq Blog in Analysis Pipelines January 10, 2018 1,983 Views

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.

Researchers from the University of Cambridge have developed the myTAI package to implement 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.

Workflow describing the input and output of the myTAI package

The myTAI package takes phylogenetic information such as phylogenetic trees (see Dunn, 2013), genomic phylostratography based gene age inference (see Domazet-Loso et al., 2007); Capra et al., 2013; Liebeskind et al., 2016), by dNdS estimation of orthologous genes (see Quint, Drost et al., 2012 and Drost et al., 2015), or phylogenetic reconciliation (see Doyon et al, 2011) and a RNA-Seq or Microarray based transcriptome dataset as input. Internally, myTAI then combines the phylogenetic data and the transcriptome data an provides numerous functions to perform evolutionary trancriptomics analyses. Here, we examplify the output of the functions PlotSignature(), PlotRE() and PlotCategoryExpr().

Availability – The open source myTAI package is available at: https://github.com/HajkD/myTAI and https://cran.r-project.org/web/packages/myTAI/index.html

Drost H, Gabel A, Liu J, Quint M, Grosse I. (2017) myTAI: Evolutionary Transcriptomics with R. Bioinformatics [Epub ahead of print]. [article]

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myTAI – Evolutionary Transcriptomics with R

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