This XCMS metabolomic proposal is highly responsive to this funding opportunity for the extended development, hardening and dissemination of technologies in biomedical computing, informatics and big data science. A recent report in the journal Nature predicts that metabolomics will be one of the top areas of research to impact the treatment and diagnosis of human disease over the next ten years1, and mass spectrometry-based metabolomics specifically has emerged as the most widely used platform to elucidate novel biomarkers, perform diagnostic testing, and identify biochemical mechanisms of disease. The XCMS Metabolomic Data Technology proposed research will develop the existing cloud-based XCMS Online ( technology for metabolic analyses to meet the needs of the over 4500 current users, allowing it to be expandable well beyond the current user base which is growing daily. The major challenge in performing metabolomic analyses is the bioinformatic processing of data, which has been addressed by the Siuzdak lab's software and database called XCMS and METLIN. These resources are the most widely used bioinformatic technologies in the field and the developments presented here are crucial to hardening and disseminating this resource for the metabolomic community. We will develop XCMS Online for its users by facilitating large scale data uploads, rapid data processing, long- term data storage and terabyte scale data analysis, including a new suite of statistical analysis approaches, visualization technologies and cloud-based data sharing for the metabolomic community. We will also develop a novel data streaming approach to allow for biological dependent data acquisition. A key development will include the analysis of stable isotopes by creating a bioinformatic solution to process metabolomic data acquired from targeted and untargeted analysis of samples that have been isotopically enriched. These data have a tremendous amount of information about cellular metabolism and will help facilitate the hardening and dissemination of the XCMS Online platform.

Public Health Relevance

The XCMS Metabolomic Data Technology proposed research will develop and harden the existing cloud- based XCMS Online ( technology to meet the needs of the over 4500 current users, allowing it to be expandable for large scale Big Data analyses.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Biodata Management and Analysis Study Section (BDMA)
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Ravichandran, Veerasamy
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Scripps Research Institute
La Jolla
United States
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Forsberg, Erica; Fang, Mingliang; Siuzdak, Gary (2017) Staying Alive: Measuring Intact Viable Microbes with Electrospray Ionization Mass Spectrometry. J Am Soc Mass Spectrom 28:14-20
Ivanisevic, Julijana; Stauch, Kelly L; Petrascheck, Michael et al. (2016) Metabolic drift in the aging brain. Aging (Albany NY) 8:1000-20
Johnson, Caroline H; Ivanisevic, Julijana; Siuzdak, Gary (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17:451-9
Johnson, Caroline H; Spilker, Mary E; Goetz, Laura et al. (2016) Metabolite and Microbiome Interplay in Cancer Immunotherapy. Cancer Res 76:6146-6152
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Fang, Mingliang; Ivanisevic, Julijana; Benton, H Paul et al. (2015) Thermal Degradation of Small Molecules: A Global Metabolomic Investigation. Anal Chem 87:10935-41
Kurczy, Michael E; Ivanisevic, Julijana; Johnson, Caroline H et al. (2015) Determining conserved metabolic biomarkers from a million database queries. Bioinformatics 31:3721-4
Ivanisevic, Julijana; Siuzdak, Gary (2015) The Role of Metabolomics in Brain Metabolism Research. J Neuroimmune Pharmacol 10:391-5
Johnson, Caroline H; Patti, Gary J; Courade, Jean-Philippe et al. (2015) Alterations in Spinal Cord Metabolism during Treatment of Neuropathic Pain. J Neuroimmune Pharmacol 10:396-401

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