We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

The Human Metabolome Database (HMDB) (www.hmdb.ca) is a resource dedicated to providing scientists with the most current and comprehensive coverage of the human metabolome. Since its first release in 2007, the HMDB has been used to facilitate research for nearly 1000 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 3.0) has been significantly expanded and enhanced over the 2009 release (version 2.0). In particular, the number of annotated metabolite entries has grown from 6500 to more than 40 000 (a 600% increase). This enormous expansion is a result of the inclusion of both ‘detected’ metabolites (those with measured concentrations or experimental confirmation of their existence) and ‘expected’ metabolites (those for which biochemical pathways are known or human intake/exposure is frequent but the compound has yet to be detected in the body). The latest release also has greatly increased the number of metabolites with biofluid or tissue concentration data, the number of compounds with reference spectra and the number of data fields per entry. In addition to this expansion in data quantity, new database visualization tools and new data content have been added or enhanced. These include better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps. This article describes these enhancements to the HMDB, which was previously featured in the 2009 NAR Database Issue. (Note to referees, HMDB 3.0 will go live on 18 September 2012.).

MetaboAnalyst (www.metaboanalyst.ca) is a web server designed to permit comprehensive metabolomic data analysis, visualization and interpretation. It supports a wide range of complex statistical calculations and high quality graphical rendering functions that require significant computational resources. First introduced in 2009, MetaboAnalyst has experienced more than a 50X growth in user traffic (>50 000 jobs processed each month). In order to keep up with the rapidly increasing computational demands and a growing number of requests to support translational and systems biology applications, we performed a substantial rewrite and major feature upgrade of the server. The result is MetaboAnalyst 3.0. By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, we have been able substantially improve its performance, capacity and user interactivity. Three new modules have also been added including: (i) a module for biomarker analysis based on the calculation of receiver operating characteristic curves; (ii) a module for sample size estimation and power analysis for improved planning of metabolomics studies and (iii) a module to support integrative pathway analysis for both genes and metabolites. In addition, popular features found in existing modules have been significantly enhanced by upgrading the graphical output, expanding the compound libraries and by adding support for more diverse organisms.

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

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.

MetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Data uploading, processing, and normalization Basic Protocol 2: Identification of significant variables Basic Protocol 3: Multivariate exploratory data analysis Basic Protocol 4: Functional interpretation of metabolomic data Basic Protocol 5: Biomarker analysis based on receiver operating characteristic (ROC) curves Basic Protocol 6: Time‐series and two‐factor data analysis Basic Protocol 7: Sample size estimation and power analysis Basic Protocol 8: Joint pathway analysis Basic Protocol 9: MS peaks to pathway activities Basic Protocol 10: Biomarker meta‐analysis Basic Protocol 11: Knowledge‐based network exploration of multi‐omics data Basic Protocol 12: MetaboAnalystR introduction

Metabolomics is a newly emerging field of ‘omics’ research that is concerned with characterizing large numbers of metabolites using NMR, chromatography and mass spectrometry. It is frequently used in biomarker identification and the metabolic profiling of cells, tissues or organisms. The data processing challenges in metabolomics are quite unique and often require specialized (or expensive) data analysis software and a detailed knowledge of cheminformatics, bioinformatics and statistics. In an effort to simplify metabolomic data analysis while at the same time improving user accessibility, we have developed a freely accessible, easy-to-use web server for metabolomic data analysis called MetaboAnalyst. Fundamentally, MetaboAnalyst is a web-based metabolomic data processing tool not unlike many of today's web-based microarray analysis packages. It accepts a variety of input data (NMR peak lists, binned spectra, MS peak lists, compound/concentration data) in a wide variety of formats. It also offers a number of options for metabolomic data processing, data normalization, multivariate statistical analysis, graphing, metabolite identification and pathway mapping. In particular, MetaboAnalyst supports such techniques as: fold change analysis, t-tests, PCA, PLS-DA, hierarchical clustering and a number of more sophisticated statistical or machine learning methods. It also employs a large library of reference spectra to facilitate compound identification from most kinds of input spectra. MetaboAnalyst guides users through a step-by-step analysis pipeline using a variety of menus, information hyperlinks and check boxes. Upon completion, the server generates a detailed report describing each method used, embedded with graphical and tabular outputs. MetaboAnalyst is capable of handling most kinds of metabolomic data and was designed to perform most of the common kinds of metabolomic data analyses. MetaboAnalyst is accessible at http://www.metaboanalyst.ca

Continuing improvements in analytical technology along with an increased interest in performing comprehensive, quantitative metabolic profiling, is leading to increased interest pressures within the metabolomics community to develop centralized metabolite reference resources for certain clinically important biofluids, such as cerebrospinal fluid, urine and blood. As part of an ongoing effort to systematically characterize the human metabolome through the Human Metabolome Project, we have undertaken the task of characterizing the human serum metabolome. In doing so, we have combined targeted and non-targeted NMR, GC-MS and LC-MS methods with computer-aided literature mining to identify and quantify a comprehensive, if not absolutely complete, set of metabolites commonly detected and quantified (with today's technology) in the human serum metabolome. Our use of multiple metabolomics platforms and technologies allowed us to substantially enhance the level of metabolome coverage while critically assessing the relative strengths and weaknesses of these platforms or technologies. Tables containing the complete set of 4229 confirmed and highly probable human serum compounds, their concentrations, related literature references and links to their known disease associations are freely available at http://www.serummetabolome.ca.