XStar: Cross Study Transcriptomic Analysis Resource

Conference Details: 

XIV Encontro Nacional de Biologia Evolutiva, 11 October 2018, Lisbon (url)

The poster is based on a paper titled "On taming the effect of transcript level intra-condition count variation during differential expression analysis: a story of dogs, foxes and wolves" that is available here. 

Presented by Diana Lobo.

Abstract

The recent expansion of RNA-Seq technology has yielded data that possess potential for providing unprecedented insight into the understanding of transcriptome evolution, function and development. The evolution of software that is essential to analysis has occurred more slowly. In a typical experiment, expression profiles based in read counts, are compared in order to identify differentially expressed genes. This is complicated by sources of variation that include sequencing errors and biological variation unassociated with the condition of interest. Current tools work based on read data from samples being allocated to one of two conditions while minimizing all other background variation and compute each genes likelihood of being differentially expressed. This process is susceptible to background noise, which is amplified at an inter-study level. We present a method for filtering out genes that display high intra condition variability based on pairwise distance profiles calculated for each gene across all samples and subsequently perform an inter-study differential expression analysis. We apply the method to datasets containing reads from brain tissues of dogs (n=10), wolves (n=6) and two strains of silver foxes (aggressive and tamed) in order to identify genes involved in domestication. By removing 5% of intra condition variation we were able to exclude 24% of previously ambiguous differentially expressed genes. Using this filtered set, we identified two genes (COL11A2 and ITGA8) that display high potential to be involved in domestication. We provide an open source, user friendly tool, to perform our analysis on any inter study transcriptomic data and feel that this will benefit the research community greatly in comparing data at an inter study level. 

(The conference abstract book is available from the download section along with a PDF of the poster)

Related software to this project are:
1. CStone 
2. CSReadGen
3. CView
4. ChimSim 
5. TVScript <

General details of the supporting projects are available here (wolf hybridization) and here (removal of chimeras).