The mRNA and miRNA transcriptomic landscape of Panax ginseng under the high ambient temperature.

BACKGROUND: Ginseng is a popular traditional herbal medicine in north-eastern Asia. It has been used for human health for over thousands of years. With the rise in global temperature, the production of Korean ginseng (Panax ginseng C.A.Meyer) in Korea have migrated from mid to northern parts of the Korean peninsula to escape from the various higher temperature related stresses. Under the high ambient temperature, vegetative growth was accelerated, which resulted in early flowering. This precocious phase change led to yield loss. Despite of its importance as a traditional medicine, biological mechanisms of ginseng has not been well studied and even the genome sequence of ginseng is yet to be determined due to its complex genome structure. Thus, it is challenging to investigate the molecular biology mechanisms at the transcript level. RESULTS: To investigate how ginseng responds to the high ambient temperature environment, we performed high throughput RNA sequencing and implemented a bioinformatics pipeline for the integrated analysis of small-RNA and mRNA-seq data without a reference genome. By performing reverse transcriptase (RT) PCR and sanger sequencing of transcripts that were assembled using our pipeline, we validated that their sequences were expressed in our samples. Furthermore, to investigate the interaction between genes and non-coding small RNAs and their regulation status under the high ambient temperature, we identified potential gene regulatory miRNAs. As a result, 100,672 contigs with significant expression level were identified and 6 known, 214 conserved and 60 potential novel miRNAs were predicted to be expressed under the high ambient temperature. CONCLUSION: Collectively, we have found that development, flowering and temperature responsive genes were induced under high ambient temperature, whereas photosynthesis related genes were repressed. Functional miRNAs were down-regulated under the high ambient temperature. Among them are miR156 and miR396 that target flowering (SPL6/9) and growth regulating genes (GRF) respectively.

BioVLAB-mCpG-SNP-EXPRESS: A system for multi-level and multi-perspective analysis and exploration of DNA methylation, sequence variation (SNPs), and gene expression from multi-omics data.

Measuring gene expression, DNA sequence variation, and DNA methylation status is routinely done using high throughput sequencing technologies. To analyze such multi-omics data and explore relationships, reliable bioinformatics systems are much needed. Existing systems are either for exploring curated data or for processing omics data in the form of a library such as R. Thus scientists have much difficulty in investigating relationships among gene expression, DNA sequence variation, and DNA methylation using multi-omics data. In this study, we report a system called BioVLAB-mCpG-SNP-EXPRESS for the integrated analysis of DNA methylation, sequence variation (SNPs), and gene expression for distinguishing cellular phenotypes at the pairwise and multiple phenotype levels. The system can be deployed on either the Amazon cloud or a publicly available high-performance computing node, and the data analysis and exploration of the analysis result can be conveniently done using a web-based interface. In order to alleviate analysis complexity, all the process are fully automated, and graphical workflow system is integrated to represent real-time analysis progression. The BioVLAB-mCpG-SNP-EXPRESS system works in three stages. First, it processes and analyzes multi-omics data as input in the form of the raw data, i.e., FastQ files. Second, various integrated analyses such as methylation vs. gene expression and mutation vs. methylation are performed. Finally, the analysis result can be explored in a number of ways through a web interface for the multi-level, multi-perspective exploration. Multi-level interpretation can be done by either gene, gene set, pathway or network level and multi-perspective exploration can be explored from either gene expression, DNA methylation, sequence variation, or their relationship perspective. The utility of the system is demonstrated by performing analysis of phenotypically distinct 30 breast cancer cell line data set. BioVLAB-mCpG-SNP-EXPRESS is available at http://biohealth.snu.ac.kr/software/biovlab_mcpg_snp_express/.