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Database: web application for visualization of the cumulated RNAseq data against the salicylic acid (SA) and methyl jasmonate (MeJA) treatment of Arabidopsis thaliana

  • Woo, Dong U1
  • Jeon, Ho Hwi1
  • Park, Halim1
  • Park, Jin Hwa1
  • Lee, Yejin1
  • Kang, Yang Jae1, 2
  • 1 Division of Bio & Medical Big data department (BK4 Program) at Gyeongsang National University, Jinju, Republic of Korea , Jinju (South Korea)
  • 2 Division of Life Science Department at Gyeongsang National University, Jinju, Republic of Korea , Jinju (South Korea)
Published Article
BMC Plant Biology
Springer (Biomed Central Ltd.)
Publication Date
Oct 02, 2020
DOI: 10.1186/s12870-020-02659-y
Springer Nature


BackgroundPlants have adapted to survive under adverse conditions or exploit favorable conditions in response to their environment as sessile creatures. In a way of plant adaptation, plant hormones have been evolved to efficiently use limited resources. Plant hormones including auxin, jasmonic acid, salicylic acid, and ethylene have been studied to reveal their role in plant adaptation against their environment by phenotypic observation with experimental design such as mutation on hormone receptors and treatment / non-treatment of plant hormones along with other environmental conditions.With the development of Next Generation Sequencing (NGS) technology, it became possible to score the total gene expression of the sampled plants and estimate the degree of effect of plant hormones in gene expression. This allowed us to infer the signaling pathway through plant hormones, which greatly stimulated the study of functional genomics using mutants. Due to the continued development of NGS technology and analytical techniques, many plant hormone-related studies have produced and accumulated NGS-based data, especially RNAseq data have been stored in the sequence read archive represented by NCBI, EBI, and DDBJ.DescriptionHere, hormone treatment RNAseq data of Arabidopsis (Col0), wild-type genotype, were collected with mock, SA, and MeJA treatments. The genes affected by hormones were identified through a machine learning approach. The degree of expression of the affected gene was quantified, visualized in boxplot using d3 (data-driven-document), and the database was built by Django.ConclusionUsing this database, we created a web application ( that lists hormone-related or hormone-affected genes and visualizes the boxplot of the gene expression of selected genes. This web application eventually aids the functional genomics researchers who want to gather the cases of the gene responses by the hormones.

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