Arghavan Alisoltani-Dehkordi is a postdoctoral research fellow in the OMICS Research Group, Department of Biotechnology, Faculty of Applied & Computer Sciences, Vaal University of Technology, Vanderbijlpark, South Africa. Arghavan completed her Ph.D. at Shahrekord University. Her research interests lie in the area of Computational Biology, ranging from theory to design analytical pipelines in plant and human sciences. She has developed several workflows to detect functional markers for cancer diagnosis and crop breeding. Her scientific productivity includes several published papers and book chapters. In recent years, she has focused on development of techniques for functional analysis of trancriptome expression data. She has collaborated actively with researchers in several other disciplines of cancer science, microbiology and computer science.
Whole-genome strategies were recently applied for eQTL and marker-assisted plant breeding. However, identifying associated variants is a major obstacle, since the known genetic variants are mostly located within non-coding regions or located at various physical distances from the gene they influence. In addition, the employed linear modeling framework in genome wide association studies (GWAS) often considers only one SNP at a time and ignores the effects of the other genotyped SNPs. Therefore, the progression can be arduous from statistical association obtained through GWAS to inferred association and functional consequences. Furthermore, many of these studies are focused on one type of genomic variations; consequently, the impacts of other involved factors are neglected. Here we suggested a reverse strategy comprising prediction of potential stress related chromosomes/regions based on transcriptome expression data followed by searching for associated variants. Identification of such stress related hot spots assists discovery of new variants as well as simultaneous study of different factors affecting gene expression by limiting assessments to specific chromosome/region. The insight could help breeders to focus on a specific chromosome and/or chromosomal region. Accordingly, we developed a pipeline for prediction of stress-related chromosomes based on calculation of the differentially-expressed genes. As a proof of concept, the developed pipeline was used to predict stress-related chromosomes of peach (Prunnus persica) using public RNA-seq data-sets. Differentially expressed genes after inoculation with Xanthomonas arboricola pv. pruni were applied to calculate the chromosome participation in gene expression. The frequency of the DE genes was calculated for each peach scaffold and was normalized according to the scaffold size. Among 9 scaffolds of peach, scaffolds 3 and 6 were recorded as scaffolds with the highest changes, suggesting the potential pivotal role the two scaffolds could play in response to pathogenicity of Xanthomonas arboricola pv. pruni. Therefore, potential application of the developed pipeline could open new window in future genome association studies to find different types of associated variants with important traits including resistance to various biotic and abiotic stresses.
Keywords: RNA-Seq; scaffold; transcriptome; Xanthomonas arboricola