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Jun Peng

Bio: Jun Peng is an academic researcher from University of Alberta. The author has contributed to research in topics: Metabolome & Metabolomics. The author has an hindex of 8, co-authored 9 publications receiving 3073 citations. Previous affiliations of Jun Peng include National Institute for Nanotechnology.

Papers
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Journal ArticleDOI
TL;DR: The most recent release of HMDB has been significantly expanded and enhanced over the previous release, with the number of fully annotated metabolite entries growing from 2180 to more than 6800, a 300% increase.
Abstract: 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.

1,748 citations

Journal ArticleDOI
16 Feb 2011-PLOS ONE
TL;DR: This work has 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.
Abstract: 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.

1,423 citations

Journal ArticleDOI
TL;DR: The UMS method was developed and applied for a urine metabolomics study of bladder cancer and showed a clear separation between the bladder cancer group and the control group from the discovery samples, which was confirmed by the verification samples.
Abstract: Large-scale metabolomics study requires a quantitative method to generate metabolome data over an extended period with high technical reproducibility We report a universal metabolome-standard (UMS) method, in conjunction with chemical isotope labeling liquid chromatography–mass spectrometry (LC–MS), to provide long-term analytical reproducibility and facilitate metabolome comparison among different data sets In this method, UMS of a specific type of sample labeled by an isotope reagent is prepared a priori The UMS is spiked into any individual samples labeled by another form of the isotope reagent in a metabolomics study The resultant mixture is analyzed by LC–MS to provide relative quantification of the individual sample metabolome to UMS UMS is independent of a study undertaking as well as the time of analysis and useful for profiling the same type of samples in multiple studies In this work, the UMS method was developed and applied for a urine metabolomics study of bladder cancer UMS of human ur

88 citations

Journal ArticleDOI
TL;DR: An enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC-MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material is reported, illustrating the utility of this isotope labeled LC-MS method for biomarker discovery using mouse urine metabolomics.
Abstract: Because of a limited volume of urine that can be collected from a mouse, it is very difficult to apply the common strategy of using multiple analytical techniques to analyze the metabolites to increase the metabolome coverage for mouse urine metabolomics. We report an enabling method based on differential isotope labeling liquid chromatography mass spectrometry (LC–MS) for relative quantification of over 950 putative metabolites using 20 μL of urine as the starting material. The workflow involves aliquoting 10 μL of an individual urine sample for 12C-dansylation labeling that target amines and phenols. Another 10 μL of aliquot was taken from each sample to generate a pooled sample that was subjected to 13C-dansylation labeling. The 12C-labeled individual sample was mixed with an equal volume of the 13C-labeled pooled sample. The mixture was then analyzed by LC–MS to generate information on metabolite concentration differences among different individual samples. The interday repeatability for the LC–MS run...

48 citations

Journal ArticleDOI
TL;DR: An improved method based on the use of liquid-liquid extraction to selectively extract the organic acids, followed by using differential isotope p-dimethylaminophenacyl (DmPA) labeling of the acid metabolites to generate a very comprehensive profile of the organic acid sub-metabolome is reported.

44 citations


Cited by
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Journal ArticleDOI
TL;DR: A new generation of a popular open-source data processing toolbox, MZmine 2 is introduced, suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.
Abstract: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/ . The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

2,884 citations

Journal ArticleDOI
TL;DR: New database visualization tools and new data content have been added or enhanced to the HMDB, which includes better spectral viewing tools, more powerful chemical substructure searches, an improved chemical taxonomy and better, more interactive pathway maps.
Abstract: 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.).

2,656 citations

Journal ArticleDOI
TL;DR: This year's update to the HMDB, HMDB 4.0, represents the most significant upgrade to the database in its history and should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.
Abstract: The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

2,608 citations

Journal ArticleDOI
TL;DR: By completely re-implementing the MetaboAnalyst suite using the latest web framework technologies, the server has been able to substantially improve its performance, capacity and user interactivity.
Abstract: 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.

2,404 citations

Journal ArticleDOI
TL;DR: The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets and is freely available at zinc.docking.org.
Abstract: ZINC is a free public resource for ligand discovery. The database contains over twenty million commercially available molecules in biologically relevant representations that may be downloaded in popular ready-to-dock formats and subsets. The Web site also enables searches by structure, biological activity, physical property, vendor, catalog number, name, and CAS number. Small custom subsets may be created, edited, shared, docked, downloaded, and conveyed to a vendor for purchase. The database is maintained and curated for a high purchasing success rate and is freely available at zinc.docking.org.

2,144 citations