Biology subjects

Kang, J. E.

Publications and source records attributed to Kang, J. E..

5 recordsLinked to original sources

bioRxiv2023

AraMultiOmics: a tool for generating omics features for downstream statistical analysis to infer the molecular basis of symbiosis among non-host plant Arabidopsis thaliana, host plant, and arbuscular mycorrhizal fungi

Arbuscular mycorrhizal fungi (AMF) are symbiotic microorganisms that colonize plant roots, promoting plant growth and improving soil quality. A large number of studies have focused on investigating the communication between host-plants and AMF. Recent investigation in molecular evolution related to arbuscular mycorrhizal symbiosis in Arabidopsis thaliana (A. thaliana) have enabled scientists to perform comparative multi-omics analyses between A. thaliana and host-plants. Although there is a vast amount of omics data available for A. thaliana, most AM-related information comes from differentially expressed genes (DEG) identified in transcriptome studies. To address this gap, we developed AraMultiOmics, a useful tool for integrative analysis of multi-omics data of A. thaliana. It consists of 10 modules: 1) epigenetic regulations in protein-nucleic acid interaction (PNI), 2) DNA structure and metal binding profile, 3) transcription factor (TF) binding profiles, 4) protein domain-domain interaction (DDI), 5) profiling of interactions of protein-metal and of protein-ligand with complex structures (PLP) based on alignment of similar protein structures, 6) carbohydrate-lipid-protein interaction (CLP)- analysis of lipidome-proteome-glycoscience, 7) metabolic pathway analysis, 8) multiple omics association study, 9) GO/PO analysis, and 10) Medicago COG information. These analyses are conducted in comparison with the COG of Medicago truncatula (M. truncatula). To facilitate the inference of AM-driven changes and of AM derived molecules during AM symbiosis, the program provides a convenient means to generate datasets with important features that can be conjoined with various downstream statistical methods. We have included demonstrations on how to create comparative datasets, and the program codes are freely available for download at www.artfoundation.kr.

bioinformatics↗

bioRxiv2023

A natural cyclic peptide valinomycin enhances plant innate immunity

Various natural compounds as alternative agents for the chemical management of plant diseases have long been proposed. Valinomycin, a Streptomyces-derived cyclic peptide, acts as an antifungal agent against several plant pathogenic fungi, including Botrytis cinerea. Here, we report the novel function of valinomycin, plant immune-boosting. Valinomycin potentiates pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) in Arabidopsis, leading to enhanced resistance against bacterial speck disease locally and systemically. Moreover, this plant immune-boosting activity of valinomycin is associated with plant hormonal signaling. Thus, we propose that valinomycin harbors potential as a biocontrol agent suppressing complex pathogen infections, such as bacteria and fungi.

plant biology↗

bioRxiv2021

GUIdeStaR (G-quadruplex, Uorf, IRES, Small RNA, Repeats), bioinformatics tool for gene characterization- case study: development of attribute selection and classification methods based on GuideStar for studying human transcription factor genes mediated by SARS-COV-2 small ncRNA

GUIdEStaR integrates existing databases of various types of G-quadruplex, upstream Open Reading Frame (uORF), Internal Ribosome Entry Site (IRES), methylation to RNA and histone protein, small RNA, and repeats. GUIdEStaR consists of approx. 40,000 genes and 320,000 transcripts. An mRNA transcript is divided into 5 regions (5UTR, 3UTR, exon, intron, and biological region) where each region contains presence-absence data of 169 different types of elements. Recently, artificial intelligence (AI) based analysis of sequencing data has been gaining popularity in the area of bioinformatics. GUIdEStaR generates datasets that can be used as inputs to AI methods. At the GUIdEStaR homepage, users submit gene symbols by clicking a "Send" button, and shortly result files in CSV format are available for download at the result website. Users have an option to send the result files to their email addresses. Additionally, the entire database and the example Java codes are also freely available for download. Here, we demonstrate the database usage with three neural network classification studies-1) small RNA study for classifying transcription factor (TF) genes into either one of TF mediated by small RNA originated from SARS-CoV-2 or by human microRNA (miRNA), 2) cell membrane receptor study for classifying receptor genes as either with virus interaction or without one, and 3) nonsense mediated mRNA decay (NMD) study for classifying cell membrane and nuclear receptors as either NMD target or non-target. GUIdEStaR is available for access to the easy-to-use web-based database at www.guidestar.kr and for download at https://sourceforge.net/projects/guidestar.

bioinformatics↗

bioRxiv2020

SeSaMe PS Function: Functional Analysis of the Whole Metagenome Sequencing Data of the Arbuscular Mycorrhizal Fungi

In this article, we introduce a novel bioinformatics program- SeSaMe PS Function (Spore associated Symbiotic Microbes Position Specific Function) - for position-specific functional analysis of short sequences derived from metagenome sequencing data of the arbuscular mycorrhizal fungi. The unique advantage of the program lies in databases created based on genus-specific sequence properties derived from protein secondary structure, namely amino acid usages, codon usages, and codon contexts of three codon DNA 9-mers. SeSaMe PS Function searches a query sequence against reference sequence database, identifies three codon DNA 9-mers with structural roles, and dynamically creates the comparative dataset of 54 microbial genera based on their codon usage biases. The program applies correlation Principal Component Analysis in conjunction with K-means clustering method to the comparative dataset. Three codon DNA 9-mers clustered as sole member or with only a few members are often structurally and functionally distinctive sites that provide useful insights into important molecular interactions. The program provides a versatile means for studying functions of short sequences from metagenome sequencing and has a wide spectrum of applications.

bioinformatics↗

bioRxiv2020

SeSaMe: Metagenome Sequence Classification of Arbuscular Mycorrhizal Fungi Associated Microorganisms

Arbuscular mycorrhizal fungi (AMF) are plant root symbionts that play key roles in plant growth and soil fertility. They are obligate biotrophic fungi that form coenocytic multinucleated hyphae and spores. Numerous studies have shown that diverse microorganisms live on the surface and inside their mycelia, resulting in a metagenome when whole genome sequencing (WGS) data are obtained from sequencing AMF cultivated in vivo. The metagenome contains not only the AMF sequences, but also those from associated microorganisms. In this article, we introduce a novel bioinformatics program, SeSaMe, designed for taxonomic classification of short sequences obtained by next-generation DNA sequencing. A genus-specific usage bias database was created based on amino acid usage and codon usage of three consecutive codon DNA 9-mers encoding for an amino acid trimer in a protein secondary structure. The program distinguishes between coding sequence (CDS) and non-CDS, and classifies a query sequence into a genus group out of 54 genera used as reference. The average correct prediction percentages of the CDS and the non-CDS test sets at the genus level were 71% and 50% for bacteria, 65% and 73% for fungi (excluding AMF), and 49% and 72% for AMF (Rhizophagus irregularis), respectively. The program provides a means for estimating not only taxonomic diversity and abundance but also the gene reservoir of the reference taxonomic groups associated with AMF. Therefore, the program enables users to study the symbiotic roles of associated microorganisms. SeSaMe can be applicable to other microorganisms as well as soil metagenomes. It is freely available at www.journal.com and www.fungalsesame.org.

bioinformatics↗