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

BACKGROUND: Plants 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. DESCRIPTION: Here, 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. CONCLUSION: Using this database, we created a web application ( http://pgl.gnu.ac.kr/hormoneDB/ ) 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.

Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

BACKGROUND: The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certification of the genomic background of their plant materials would be important for the reliability of seed market and stable yield as well as for quarantine procedure. However, the analysis of NGS data is still difficult for non-computational researchers or breeders to verify their samples because majority of current softwares for NGS analysis require users to access unfamiliar Linux environment. MAIN BODY: Here, we developed a web-application, "Soybean-VCF2Genomes", http://pgl.gnu.ac.kr/soy_vcf2genome/ to map single sample variant call format (VCF) file against known soybean germplasm collection for identification of the closest soybean accession. Based on principal component analysis (PCA), we simplified genotype matrix for lowering computational burden while maintaining accurate clustering. With our web-application, users can simply upload single sample VCF file created by more than 10x resequencing strategy to find the closest samples along with linkage dendrogram of the reference genotype matrix. CONCLUSION: The information of the closest soybean cultivar will allow breeders to estimate relative germplasmic position of their query sample to determine soybean breeding strategies. Moreover, our VCF2Genomes scheme can be extended to other plant species where the whole genome sequences of core collection are publicly available.

RiceProteomeDB (RPDB): a user-friendly database for proteomics data storage, retrieval, and analysis.

Rice, feeding a significant portion of the world, poses unique proteomic challenges critical to agricultural research and global food security. The complexity of the rice proteome, influenced by various genetic and environmental factors, demands specialized analytical approaches for effective study. The central challenges in rice proteomics lie in developing custom methods suited to the unique aspects of rice biology. These include data preprocessing, method selection, and result validation, all of which are essential for advancing rice research. Our aim is to decode these proteomic intricacies to facilitate breakthroughs in strain improvement, disease resistance, and yield optimization, all vital for combating global food insecurity. To achieve this, we have created the RiceProteomeDB (RPDB), a React + Django database, offering a streamlined and comprehensive platform for the analysis of rice proteomics data. RiceProteomeDB (RPDB) simplifies proteomics data management and analysis. It offers features for data organization, preprocessing, method selection, result validation, and data sharing. Researchers can access processed rice proteomics data, conduct analyses, and explore experimental conditions. The user-friendly web interface enhances navigation and interaction. RPDB fosters collaboration by enabling data sharing and proper acknowledgment of sources, contributing to proteomics research and knowledge dissemination. Availability and implementation: Web application: http://riceproteome.plantprofile.net/ . The web application's source code, user's manual, and sample data: https://github.com/dongu7610/Riceproteome .

SoyDBean: a database for SNPs reconciliation by multiple versions of soybean reference genomes.

Due to the development of sequence technology and decreased cost, many whole genome sequences have been obtained. As a result, extensive genetic variations have been discovered from many populations and germplasms to understand the genetic diversity of soybean (Glycine max [L.] Merr.). However, assessing the quality of variation is essential because the published variants were collected using different bioinformatic methods and parameters. Furthermore, despite the enhanced genome contiguity and more efficient filling of "N" stretches in the new reference genome, there remains a dearth of endeavors to verify the caliber of variations present in it. The primary goal of this research was to discern a dependable set of SNPs that can withstand reconciliation across multiple reference genomes. Additionally, the investigation aimed to reconfirm the variations through the utilization of numerous whole genome sequencing data obtained from publicly available databases. Based on the result, we created datasets that comprised the thoroughly verified SNP coordinates between the reference assemblies. The resulting "SoyDBean" database is now publicly accessible through the following URL: http://soydbean.plantprofile.net/ .

The Semi-Supervised Strategy of Machine Learning on the Gene Family Diversity to Unravel Resveratrol Synthesis.

Resveratrol is a phytochemical with medicinal benefits, being well-known for its presence in wine. Plants develop resveratrol in response to stresses such as pathogen infection, UV radiation, and other mechanical stress. The recent publications of genomic sequences of resveratrol-producing plants such as grape, peanut, and eucalyptus can expand our molecular understanding of resveratrol synthesis. Based on a gene family count matrix of Viridiplantae members, we uncovered important gene families that are common in resveratrol-producing plants. These gene families could be prospective candidates for improving the efficiency of synthetic biotechnology-based artificial resveratrol manufacturing.

Biofilm and Spore Formation of Clostridium perfringens and Its Resistance to Disinfectant and Oxidative Stress.

Clostridium perfringens is a major human pathogen that causes gastroenteritis via enterotoxin production and has the ability to form spores and biofilms for environmental persistence and disease transmission. This study aimed to compare the disinfectant and environmental resistance properties of C. perfringens vegetative cells and spores in planktonic and sessile conditions, and to examine the nucleotide polymorphisms and transcription under sessile conditions in C. perfringens strains isolated from meat. The sporulation rate of sessile C. perfringens TYJAM-D-66 (cpe+) was approximately 19% at day 5, while those of CMM-C-80 (cpe-) and SDE-B-202 (cpe+) were only 0.26% and 0.67%, respectively, at day 7. When exposed to aerobic conditions for 36 h, TYJAM-D-66, CMM-C-80, and SDE-B-202 vegetative cells showed 1.70 log, 5.36 log, and 5.67 log reductions, respectively. After treatment with sodium hypochlorite, the survival rates of TYJAM-D-66 vegetative cells (53.6%) and spores (82.3%) in biofilms were higher than those of planktonic cells (9.23%). Biofilm- and spore-related genes showed different expression within TYJAM-D-66 (-4.66~113.5), CMM-C-80 (-3.02~2.49), and SDE-B-202 (-5.07~2.73). Our results indicate the resistance of sessile cells and spores of C. perfringens upon exposure to stress conditions after biofilm formation.

Genome of the world's smallest flowering plant, Wolffia australiana, helps explain its specialized physiology and unique morphology.

Watermeal, Wolffia australiana, is the smallest known flowering monocot and is rich in protein. Despite its great potential as a biotech crop, basic research on Wolffia is in its infancy. Here, we generated the reference genome of a species of watermeal, W. australiana, and identified the genome-wide features that may contribute to its atypical anatomy and physiology, including the absence of roots, adaxial stomata development, and anaerobic life as a turion. In addition, we found evidence of extensive genome rearrangements that may underpin the specialized aquatic lifestyle of watermeal. Analysis of the gene inventory of this intriguing species helps explain the distinct characteristics of W. australiana and its unique evolutionary trajectory.