Abstract
The evolution of RNA-Seq technologies yielded datasets that are of immense scientific value. Commonly, such data is generated within differential expression studies, where datasets derived from individual samples are grouped into conditions, and gene expression patterns quantified. The number of archived datasets is increasing and revisiting many at an inter-study level provides an in-depth view into transcriptome evolution. The biggest hurdle is in dealing with variation of read counts at an individual transcript level between common conditions. We present a tool, TVScript, that quantifies intra-condition variation, and subsequently, removes reference-based transcripts that are associated with high levels of this. TVScript is demonstrated at inter and intra-study levels, using data from brain samples of dogs, wolves and foxes (aggressive and tame), where a marked improvement in the distribution of the gene-wise dispersion estimates, the metric utilized by the majority of differential expression tools, lowered the number of outliers detected. We provide support for seven candidate genes with potential for being involved with selection for tameness, and that appear to play a crucial role in canine domestication. We also identify several genes previously identified as being differentially expressed, but that possessed high intra-condition variation, weakening their relevance. TVScript is available at: https://sourceforge.net/projects/tvscript/.
Differential expression of individual transcript variants of PD-1 and PD-L2 genes on Th-1/Th-2 status is guaranteed for prognosis prediction in PCNSL
