AraMultiOmics: a tool for generating multi-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

Abstract

AbstractArbuscular 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 multiple modules: protein–nucleic acid interaction (PNI), transcription factor (TF) binding profiles, domain–domain interaction (DDI), protein–ligand profile (PLP) based on alignment of similar protein structures, multiple omics association study, metabolic pathway analysis, GO/PO analysis, and DNA structure and metal binding profiles. These analyses are conducted in comparison with the COG of Medicago truncatula (M. truncatula). With recently developed software and databases, we incorporated predictions of DNA structure, profiling of protein-metal and of protein-ligand with complex structures, and membrane protein orientation to provide researchers with tools for investigating AM-driven changes, such as the effects of increased accessibility of transition metals in PNI and DDI or assessing roles of AM derived molecules in catalyzing volatile organic compound (VOC) reactions. To facilitate the inference of molecular signatures 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 atwww.artfoundation.kr.