Duane Rinehart

XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data

Recently, interest in untargeted metabolomics has become prevalent in the general scientific community among an increasing number of investigators. The majority of these investigators, however, do not have the bioinformatic expertise that has been required to process metabolomic data by using command-line driven software programs. Here we introduce a novel platform to process untargeted metabolomic data that uses an intuitive graphical interface and does not require installation or technical expertise. This platform, called XCMS Online, is a web-based version of the widely used XCMS software that allows users to easily upload and process liquid chromatography/mass spectrometry data with only a few mouse clicks. XCMS Online provides a solution for the complete untargeted metabolomic workflow including feature detection, retention time correction, alignment, annotation, statistical analysis, and data visualization. Results can be browsed online in an interactive, customizable table showing statistics, chromatograms, and putative METLIN identities for each metabolite. Additionally, all results and images can be downloaded as zip files for offline analysis and publication. XCMS Online is available at https://xcmsonline.scripps.edu.

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.

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. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. 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.

Metabolomic data streaming for biology-dependent data acquisition

1. Jelinek, M. et al. Res. Technol. Manag. 55, 16–6 (2012). 2. code of Federal Regulations, Title 21 c.F.R. § 314.53. 3. Friedman, Y. Nat. Rev. Drug Discov. 9, 835–836 (2010). 4. Light, D.W. Health Aff. 28, w969–w977 (2009). 5. Pharmaceutical Research and Manufacturers of America, Pharmaceutical Industry Profile 2012. http:// www.phrma.org/sites/default/files/159/phrma_industry_profile.pdf (PhRMA; Washington, Dc, April 2012) 6. Locke, R.M. & Wellhausen, R. A Preview of the MIT Production in the Innovation Economy Report. http:// web.mit.edu/pie/news/PIE_Preview.pdf (Massachusetts Institute of Technology, cambridge, MA, 2013). 7. Breznitz, D. & Murphree, M. Run of the Red Queen Government, Innovation, Globalization, and Economic Growth in China (Yale University Press, New Haven cT, 2011) 8. National Science Board. Science and Engineering Indicators 2012. NSB 12–01 (National Science Foundation, Washington, Dc, 2012) 9. World Intellectual Property organization. 2012 World Intellectual Property Indicators (WIPo, Geneva, Switzerland, December 2012) with a strong record of innovation in semiconductors and automobiles, also has very few biopharmaceutical inventors. Overall, the figures show that the locations of biopharmaceutical innovation have remained largely the same since 2000; clearly, most inventors reside in the United States, the legacy biopharmaceutical nations in Europe and in Japan. The near-absence of patent inventorship (12 patent inventors in China and 9 in India versus >4,000 in the United States since 2000) raises questions about the level of pharmaceutical innovation in these emerging economies. What’s more, the low level of R&D investment in these countries— according to the Pharmaceutical Researchers and Manufacturing Association, in 2010 US pharmaceutical R&D investments were

An Interactive Cluster Heat Map to Visualize and Explore Multidimensional Metabolomic Data.

40.7 billion (80.2% of the global total), Chinese investments were

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online

142 million (0.3% of the global total) and Indian investments were

Determining Conserved Metabolic Biomarkers from a Million Database Queries

43.9 million (0.1% of the global total)5—adds further doubt to the innovative capacity of these countries. For these emerging economies to achieve future growth in innovation similar to that seen in developed economies, clearly a larger corps of inventors and much increased R&D investment will be necessary. Another explanation for the low output is that innovation in emerging economies is fundamentally different from the proprietary model of Western biopharmaceutical innovation, which requires patenting and high levels of R&D investment upfront. If innovation in emerging economies really is different, neither inventorship of patents nor R&D investment would be a good proxy for innovation. In this respect, a report from the Massachusetts Institute of Technology (MIT) Taskforce on Innovation and Production, contrasting product development in China and Germany, may be informative6. The task force made the following two observations. First, new business creation in Germany was not done through the start-up model familiar in the United States, but rather occurred “through the transformation of old capabilities and their reapplication, repurposing and commercialization.” Thus, the strength of German firms was in repurposing existing assets. Second, the task force further observed that Chinese firms, by contrast, excelled in scale-up to mass-manufacturing “not because of low-cost labor, but because of their ability to move complex advanced product designs into production and commercialization.” This sentiment is echoed in Run of the Red Queen7, where the authors combined over 200 interviews and industry analysis to conclude that in China process innovation, rather than product innovation, is central to economic growth. Accordingly, one must consider that the contributions of China, and of other countries outside regions that have an already established biopharmaceutical sector, will be in areas beyond inventing new molecules; rather, their strengths may be in developing better tools and methods for research or in developing better methods to refine the patented inventions. China is a world leader in scientific publishing8 and in patent filings9, neither of which seem to be delivering proportional outputs. Chinese papers have low citations rates, both domestically and internationally7, and (as shown here) Chinese inventors appear on few patents covering globally marketed biopharmaceuticals. In other words, China’s current strategy appears to be based on promoting traditional outputs, which do not support its core strengths. With the above factors in mind, governments in countries such as China and India that have a low rate of drug inventorship and a proven ability to reduce costs beyond simply providing low-cost manual labor may be directing their resources inappropriately. Rather than focusing policy and funding on historical

XCMS-MRM and METLIN-MRM: a cloud library and public resource for targeted analysis of small molecules

Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)–mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5–10 min, depending on user experience; data processing typically takes 1–3 h, and data analysis takes ∼30 min.

Exposing the Exposome with Global Metabolomics and Cognitive Computing

MOTIVATION Metabolite databases provide a unique window into metabolome research allowing the most commonly searched biomarkers to be catalogued. Omic scale metabolite profiling, or metabolomics, is finding increased utility in biomarker discovery largely driven by improvements in analytical technologies and the concurrent developments in bioinformatics. However, the successful translation of biomarkers into clinical or biologically relevant indicators is limited. RESULTS With the aim of improving the discovery of translatable metabolite biomarkers, we present search analytics for over one million METLIN metabolite database queries. The most common metabolites found in METLIN were cross-correlated against XCMS Online, the widely used cloud-based data processing and pathway analysis platform. Analysis of the METLIN and XCMS common metabolite data has two primary implications: these metabolites, might indicate a conserved metabolic response to stressors and, this data may be used to gauge the relative uniqueness of potential biomarkers. AVAILABILITY AND IMPLEMENTATION METLIN can be accessed by logging on to: https://metlin.scripps.edu CONTACT siuzdak@scripps.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.