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Gowda, Harsha

Publications and source records attributed to Gowda, Harsha.

Autonomous Metabolomics for Rapid Metabolite Identification in Global Profiling

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. We can analyze large profiling datasets and simultaneously obtain structural identifications, as a result of this unique integration. Furthermore, validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗