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Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC–MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca . Graphical Abstract From raw data to statistical and functional insights using MetaboAnalyst 5.0.
The performance of a diagnostic test in the case of a binary predictor can be evaluated using the measures of sensitivity and specificity. However, in many instances, we encounter predictors that are measured on a continuous or ordinal scale. In such cases, it is desirable to assess performance of a diagnostic test over the range of possible cutpoints for the predictor variable. This is achieved by a receiver operating characteristic (ROC) curve that includes all the possible decision thresholds from a diagnostic test result. In this brief report, we discuss the salient features of the ROC curve, as well as discuss and interpret the area under the ROC curve, and its utility in comparing two different tests or predictor variables of interest.
Plant secondary metabolites are unique sources for pharmaceuticals, food additives, flavors, and industrially important biochemicals. Accumulation of such metabolites often occurs in plants subjected to stresses including various elicitors or signal molecules. Secondary metabolites play a major role in the adaptation of plants to the environment and in overcoming stress conditions. Environmental factors viz. temperature, humidity, light intensity, the supply of water, minerals, and CO2 influence the growth of a plant and secondary metabolite production. Drought, high salinity, and freezing temperatures are environmental conditions that cause adverse effects on the growth of plants and the productivity of crops. Plant cell culture technologies have been effective tools for both studying and producing plant secondary metabolites under in vitro conditions and for plant improvement. This brief review summarizes the influence of different abiotic factors include salt, drought, light, heavy metals, frost etc. on secondary metabolites in plants. The focus of the present review is the influence of abiotic factors on secondary metabolite production and some of important plant pharmaceuticals. Also, we describe the results of in vitro cultures and production of some important secondary metabolites obtained in our laboratory.
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