Moreover, there is an increasing demand for standardization of data reporting for large-scale metabolomics, which will help researchers to cross-reference results from different studies with profound benefits. Within this context, we have undertaken the task of developing a highthroughput metabolomics/bioinformatics protocol for the robust dissection of plant-fungal pathogen interaction using the pathosystem. For the analysis of soybean’s metabolome direct infusion Orbitrap mass spectrometry and gas chromatography-MS analyzers were employed, which exhibit complimentary capabilities for metabolite detection and identification. Soybean is a crop grown on almost 6% of arable land and among the most important plant sources of human food, animal feed protein, and cooking oil, phytoestrogens, and biodiesel. It is the first legume species with a complete sequence, and therefore, a key reference for the development of high-throughput plant metabolomics protocols. Various biotic constraints such as, bacteria, fungi, nematodes, and insects threaten its production by directly reducing seed yield and/or quality. Among them is the soil-borne fungal pathogen R. solani, which causes Rhizoctonia rot of young seedlings and is characterized by post-emergence symptoms of brown AMN107 necrotic lesions observed on roots, hypocotyls and stems. In North America, severe yield losses of up to 48% result from stand reduction in newly planted fields and premature death of diseased plants that produce undersize seeds. Few management options exist since no resistant or tolerant varieties are available. During plant-pathogen interaction a sequence of chemical, molecular, and mechanical events occur, implicated in a distinctive “dialogue” leading to the development of the disease. Apart from recent “omics” studies on the responses of soybean to stresses, no study exists on the regulation of the global soybean metabolome in response to pathogen infection. In the present study, we explore whole-cellular processes at the metabolome level under a pathogen attack and present a comprehensive overview of soybean’s primary and secondary metabolism regulation. This is accomplished by combining multidimensional metabolomics data in an effort to detect specific pathways indispensable for the regulation of metabolism. Key components in the approach are the construction of a standardized soybean library and the use of bioinformatics software for the visualization and analyses of soybean metabolome, which can significantly accelerate the steps of metabolite identification and biological interpretation of results. The protein amino acid pool of seedlings was also substantially affected in two distinct phases, one at 24 h and the second at 48 h post-inoculation.