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Knox C, Wilson M, Klinger C, Franklin M, Oler E, Wilson A, Pon A, Cox J, Chin NE, Strawbridge S, Garcia-Patino M, Kruger R, Sivakumaran A, Sanford S, Doshi R, Khetarpal N, Fatokun O, Doucet D, Zubkowski A, Rayat D, Jackson H, Harford K, Anjum A, Zakir M, Wang F, Tian S, Lee B, Liigand J, Peters H, Wang RQ, Nguyen T, So D, Sharp M, da Silva R, Gabriel C, Scantlebury J, Jasinski M, Ackerman D, Jewison T, Sajed T, Gautam V, Wishart D. DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res 2024; 52:D1265-D1275. [PMID: 37953279 PMCID: PMC10767804 DOI: 10.1093/nar/gkad976] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 10/09/2023] [Accepted: 10/16/2023] [Indexed: 11/14/2023] Open
Abstract
First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.
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Affiliation(s)
- Craig Knox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mike Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Christen M Klinger
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Mark Franklin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Alex Wilson
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Allison Pon
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Jordan Cox
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Na Eun (Lucy) Chin
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Seth A Strawbridge
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marysol Garcia-Patino
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Ray Kruger
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Aadhavya Sivakumaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Selena Sanford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Rahil Doshi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nitya Khetarpal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Omolola Fatokun
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Daphnee Doucet
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ashley Zubkowski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Yahya Rayat
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Hayley Jackson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karxena Harford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Afia Anjum
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mahi Zakir
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Fei Wang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jaanus Liigand
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ruo Qi (Rachel) Wang
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tue Nguyen
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Denise So
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Matthew Sharp
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Rodolfo da Silva
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Cyrella Gabriel
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Joshua Scantlebury
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Marissa Jasinski
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - David Ackerman
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Timothy Jewison
- OMx Personal Health Analytics, Inc., 700–10130 103 St NW, Edmonton, AB T5J 1B9, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H1, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 1C9, Canada
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2
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Rout M, Lipfert M, Lee BL, Berjanskii M, Assempour N, Fresno RV, Cayuela AS, Dong Y, Johnson M, Shahin H, Gautam V, Sajed T, Oler E, Peters H, Mandal R, Wishart DS. MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:681-704. [PMID: 37265034 DOI: 10.1002/mrc.5371] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/12/2023] [Accepted: 05/17/2023] [Indexed: 06/03/2023]
Abstract
Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.
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Affiliation(s)
- Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Nazanin Assempour
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rosa Vazquez Fresno
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Arnau Serra Cayuela
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Ying Dong
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Mathew Johnson
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Honeya Shahin
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Tanvir Sajed
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Rupasri Mandal
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
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3
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Yanshole VV, Melnikov AD, Yanshole LV, Zelentsova EA, Snytnikova OA, Osik NA, Fomenko MV, Savina ED, Kalinina AV, Sharshov KA, Dubovitskiy NA, Kobtsev MS, Zaikovskii AA, Mariasina SS, Tsentalovich YP. Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data. Metabolites 2023; 13:1088. [PMID: 37887413 PMCID: PMC10609207 DOI: 10.3390/metabo13101088] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/07/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023] Open
Abstract
The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.
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Affiliation(s)
- Vadim V. Yanshole
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Arsenty D. Melnikov
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Lyudmila V. Yanshole
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Ekaterina A. Zelentsova
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Olga A. Snytnikova
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Nataliya A. Osik
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Maxim V. Fomenko
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
- Department of Physics, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia
| | - Ekaterina D. Savina
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Anastasia V. Kalinina
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
| | - Kirill A. Sharshov
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Federal Research Center of Fundamental and Translational Medicine, Timakova Str. 2, Novosibirsk 630117, Russia; (K.A.S.); (N.A.D.)
| | - Nikita A. Dubovitskiy
- Laboratory of Molecular Epidemiology and Biodiversity of Viruses, Federal Research Center of Fundamental and Translational Medicine, Timakova Str. 2, Novosibirsk 630117, Russia; (K.A.S.); (N.A.D.)
| | - Mikhail S. Kobtsev
- Department of Information Technologies, Novosibirsk State University, Pirogova Str. 1, Novosibirsk 630090, Russia;
| | - Anatolii A. Zaikovskii
- Department of Mathematics and Computer Science, Saint Petersburg State University, 14th Line V. O. 29, Saint Petersburg 199178, Russia;
| | - Sofia S. Mariasina
- Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia;
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow 119991, Russia
- RUDN University, Miklukho-Maklaya Str. 6, Moscow 117198, Russia
| | - Yuri P. Tsentalovich
- Laboratory of Proteomics and Metabolomics, International Tomography Center SB RAS, Institutskaya Str. 3a, Novosibirsk 630090, Russia; (A.D.M.); (L.V.Y.); (E.A.Z.); (O.A.S.); (N.A.O.); (M.V.F.); (E.D.S.); (A.V.K.); (Y.P.T.)
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4
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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5
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Wishart DS, Rout M, Lee BL, Berjanskii M, LeVatte M, Lipfert M. Practical Aspects of NMR-Based Metabolomics. Handb Exp Pharmacol 2023; 277:1-41. [PMID: 36271165 DOI: 10.1007/164_2022_613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for >40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada.
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB, Canada.
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB, Canada.
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Marcia LeVatte
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Matthias Lipfert
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
- Reference Standard Management & NMR QC, Lonza Group AG, Visp, Switzerland
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6
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Rauh D, Blankenburg C, Fischer TG, Jung N, Kuhn S, Schatzschneider U, Schulze T, Neumann S. Data format standards in analytical chemistry. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-3101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Research data is an essential part of research and almost every publication in chemistry. The data itself can be valuable for reuse if sustainably deposited, annotated and archived. Thus, it is important to publish data following the FAIR principles, to make it findable, accessible, interoperable and reusable not only for humans but also in machine-readable form. This also improves transparency and reproducibility of research findings and fosters analytical work with scientific data to generate new insights, being only accessible with manifold and diverse datasets. Research data requires complete and informative metadata and use of open data formats to obtain interoperable data. Generic data formats like AnIML and JCAMP-DX have been used for many applications. Special formats for some analytical methods are already accepted, like mzML for mass spectrometry or nmrML and NMReDATA for NMR spectroscopy data. Other methods still lack common standards for data. Only a joint effort of chemists, instrument and software vendors, publishers and infrastructure maintainers can make sure that the analytical data will be of value in the future. In this review, we describe existing data formats in analytical chemistry and introduce guidelines for the development and use of standardized and open data formats.
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Affiliation(s)
- David Rauh
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Claudia Blankenburg
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Tillmann G. Fischer
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
| | - Nicole Jung
- Karlsruhe Institute of Technology, Institute for Chemical and Biological Systems (IBCS-FMS) , Hermann von Helmholtz Platz 1 , 76344 Eggenstein-Leopolshafen , Germany
| | - Stefan Kuhn
- School of Computer Science and Informatics , De Montfort University , Leicester , UK
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie , Julius-Maximilians-Universität Würzburg , Am Hubland , D-97074 Würzburg , Germany
| | - Tobias Schulze
- Department of Effect-Directed Analysis , Helmholtz Centre for Environmental Research – UFZ , Permoserstr. 15, 04318 Leipzig , Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data , Weinberg 3 , 06120 Halle , Germany
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7
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Paulhe N, Canlet C, Damont A, Peyriga L, Durand S, Deborde C, Alves S, Bernillon S, Berton T, Bir R, Bouville A, Cahoreau E, Centeno D, Costantino R, Debrauwer L, Delabrière A, Duperier C, Emery S, Flandin A, Hohenester U, Jacob D, Joly C, Jousse C, Lagree M, Lamari N, Lefebvre M, Lopez-Piffet C, Lyan B, Maucourt M, Migne C, Olivier MF, Rathahao-Paris E, Petriacq P, Pinelli J, Roch L, Roger P, Roques S, Tabet JC, Tremblay-Franco M, Traïkia M, Warnet A, Zhendre V, Rolin D, Jourdan F, Thévenot E, Moing A, Jamin E, Fenaille F, Junot C, Pujos-Guillot E, Giacomoni F. PeakForest: a multi-platform digital infrastructure for interoperable metabolite spectral data and metadata management. Metabolomics 2022; 18:40. [PMID: 35699774 PMCID: PMC9197906 DOI: 10.1007/s11306-022-01899-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 05/22/2022] [Indexed: 01/02/2023]
Abstract
INTRODUCTION Accuracy of feature annotation and metabolite identification in biological samples is a key element in metabolomics research. However, the annotation process is often hampered by the lack of spectral reference data in experimental conditions, as well as logistical difficulties in the spectral data management and exchange of annotations between laboratories. OBJECTIVES To design an open-source infrastructure allowing hosting both nuclear magnetic resonance (NMR) and mass spectra (MS), with an ergonomic Web interface and Web services to support metabolite annotation and laboratory data management. METHODS We developed the PeakForest infrastructure, an open-source Java tool with automatic programming interfaces that can be deployed locally to organize spectral data for metabolome annotation in laboratories. Standardized operating procedures and formats were included to ensure data quality and interoperability, in line with international recommendations and FAIR principles. RESULTS PeakForest is able to capture and store experimental spectral MS and NMR metadata as well as collect and display signal annotations. This modular system provides a structured database with inbuilt tools to curate information, browse and reuse spectral information in data treatment. PeakForest offers data formalization and centralization at the laboratory level, facilitating shared spectral data across laboratories and integration into public databases. CONCLUSION PeakForest is a comprehensive resource which addresses a technical bottleneck, namely large-scale spectral data annotation and metabolite identification for metabolomics laboratories with multiple instruments. PeakForest databases can be used in conjunction with bespoke data analysis pipelines in the Galaxy environment, offering the opportunity to meet the evolving needs of metabolomics research. Developed and tested by the French metabolomics community, PeakForest is freely-available at https://github.com/peakforest .
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Affiliation(s)
- Nils Paulhe
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cécile Canlet
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Annelaure Damont
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Lindsay Peyriga
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077, Toulouse, France
| | - Stéphanie Durand
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Catherine Deborde
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Sandra Alves
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Stephane Bernillon
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Thierry Berton
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Raphael Bir
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Alyssa Bouville
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Edern Cahoreau
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics & Fluxomics (ANR-11-INBS-0010), 31077, Toulouse, France
| | - Delphine Centeno
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Robin Costantino
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Laurent Debrauwer
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Alexis Delabrière
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Christophe Duperier
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Sylvain Emery
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Amelie Flandin
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Ulli Hohenester
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Daniel Jacob
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Charlotte Joly
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Cyril Jousse
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marie Lagree
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Nadia Lamari
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Marie Lefebvre
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Claire Lopez-Piffet
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Bernard Lyan
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Mickael Maucourt
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Carole Migne
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Marie-Francoise Olivier
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Estelle Rathahao-Paris
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Pierre Petriacq
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Julie Pinelli
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Léa Roch
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Pierrick Roger
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Simon Roques
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Jean-Claude Tabet
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Marie Tremblay-Franco
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Mounir Traïkia
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Anna Warnet
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Vanessa Zhendre
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Dominique Rolin
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Fabien Jourdan
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - Etienne Thévenot
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Annick Moing
- Université de Bordeaux, INRAE, Biologie du Fruit et Pathologie, UMR 1332, Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 71 av E. Bourlaux, 33140, Villenave d'Ornon, France
| | - Emilien Jamin
- Toxalim (Research Center in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, MetaboHUB, 31300, Toulouse, France
| | - François Fenaille
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Christophe Junot
- Département Médicaments et Technologies pour la Santé (DMTS), Université Paris-Saclay, CEA, INRAE, MetaboHUB, 91191, Gif sur Yvette, France
| | - Estelle Pujos-Guillot
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France
| | - Franck Giacomoni
- Université Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du Métabolisme, MetaboHUB Clermont, Clermont-Ferrand, France.
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8
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Hanson RM, Jeannerat D, Archibald M, Bruno IJ, Chalk SJ, Davies AN, Lancashire RJ, Lang J, Rzepa HS. IUPAC specification for the FAIR management of spectroscopic data in chemistry (IUPAC FAIRSpec) – guiding principles. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-2009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
A set of guiding principles for the development of a standard for FAIR management of spectroscopic data are outlined and discussed. The principles form the basis for future recommendations of IUPAC Project 2019-031-1-024 specifying a detailed data model and metadata schema for describing the contents of an “IUPAC FAIRData Collection” and the organization of digital objects within that collection. Foremost among the recommendations will be a specification for an “IUPAC FAIRData Finding Aid” that describes the collection in such a way as to optimize the findability, accessibility, interoperability, and reusability of its contents. Results of an analysis of data provided by an American Chemical Society Publications pilot study are discussed in relation to potential workflows that might be used in implementing the “IUPAC FAIRSpec” standard based on these principles.
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Affiliation(s)
- Robert M. Hanson
- Department of Chemistry , St Olaf College , Northfield , MN , USA
| | | | | | - Ian J. Bruno
- Cambridge Crystallographic Data Centre , 12 Union Road , Cambridge CB2 1EZ , UK
| | - Stuart J. Chalk
- Department of Chemistry , University of North Florida , Jacksonville , FL , USA
| | - Antony N. Davies
- SERC, Sustainable Environment Research Centre, Faculty of Computing, Engineering and Science , University of South Wales , Newport , UK
| | - Robert J. Lancashire
- Department of Chemistry , The University of the West Indies , Mona Campus , Kingston 7 , Jamaica
| | - Jeffrey Lang
- American Chemical Society Publications Division , Washington , DC , USA
| | - Henry S. Rzepa
- Department of Chemistry , Molecular Sciences Research Hub, Imperial College London , White City Campus, Wood Lane , London W12 OBZ , UK
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9
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Strömert P, Hunold J, Castro A, Neumann S, Koepler O. Ontologies4Chem: the landscape of ontologies in chemistry. PURE APPL CHEM 2022. [DOI: 10.1515/pac-2021-2007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.
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Affiliation(s)
- Philip Strömert
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - Johannes Hunold
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - André Castro
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry , Weinberg 3 , 06120 Halle , Germany
| | - Oliver Koepler
- TIB – Leibniz Information Centre for Science and Technology , Welfengarten 1 B, 30167 Hannover , Germany
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10
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Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, Dizon R, Sayeeda Z, Tian S, Lee B, Berjanskii M, Mah R, Yamamoto M, Jovel J, Torres-Calzada C, Hiebert-Giesbrecht M, Lui V, Varshavi D, Varshavi D, Allen D, Arndt D, Khetarpal N, Sivakumaran A, Harford K, Sanford S, Yee K, Cao X, Budinski Z, Liigand J, Zhang L, Zheng J, Mandal R, Karu N, Dambrova M, Schiöth H, Greiner R, Gautam V. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Res 2022; 50:D622-D631. [PMID: 34986597 PMCID: PMC8728138 DOI: 10.1093/nar/gkab1062] [Citation(s) in RCA: 699] [Impact Index Per Article: 349.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/13/2021] [Accepted: 10/19/2021] [Indexed: 01/23/2023] Open
Abstract
The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - AnChi Guo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Fei Wang
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Afia Anjum
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Raynard Dizon
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zinat Sayeeda
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Robert Mah
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mai Yamamoto
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Juan Jovel
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | | | - Vicki W Lui
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorna Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dana Allen
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - David Arndt
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nitya Khetarpal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Aadhavya Sivakumaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Karxena Harford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Selena Sanford
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Kristen Yee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Xuan Cao
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zachary Budinski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jaanus Liigand
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Lun Zhang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jiamin Zheng
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Rupasri Mandal
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Naama Karu
- Leiden Academic Centre for Drug Research LACDR/Analytical Biosciences, Leiden University, Leiden, Netherlands
| | - Maija Dambrova
- Laboratory of Pharmaceutical Pharmacology, Latvian Institute of Organic Synthesis, Riga, Latvia
| | - Helgi B Schiöth
- Section of Functional Pharmacology, Department of Neuroscience, Uppsala University, Uppsala, Sweden
- Institute for Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Russell Greiner
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
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11
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Rzepa HS, Kuhn S. A data-oriented approach to making new molecules as a student experiment: artificial intelligence-enabling FAIR publication of NMR data for organic esters. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2022; 60:93-103. [PMID: 34106480 DOI: 10.1002/mrc.5186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/23/2021] [Accepted: 06/06/2021] [Indexed: 06/12/2023]
Abstract
The lack of machine-readable data is a major obstacle in the application of nuclear magnetic resonance (NMR) in artificial intelligence (AI). As a way to overcome this, a procedure for capturing primary NMR spectroscopic instrumental data annotated with rich metadata and publication in a Findable, Accessible, Interoperable and Reusable (FAIR) data repository is described as part of an undergraduate student laboratory experiment in a chemistry department. This couples the techniques of chemical synthesis of a never before made organic ester with illustration of modern data management practices and serves to raise student awareness of how FAIR data might improve research quality and replicability. Searches of the registered metadata are shown, which enable actionable finding and accessing of such data. The potential for re-use of the data in AI applications is discussed.
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Affiliation(s)
- Henry S Rzepa
- Department of Chemistry, Molecular Sciences Research Hub, Imperial College London, London, UK
| | - Stefan Kuhn
- School of Computer Science and Informatics, De Montfort University, Leicester, UK
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12
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Castelli FA, Rosati G, Moguet C, Fuentes C, Marrugo-Ramírez J, Lefebvre T, Volland H, Merkoçi A, Simon S, Fenaille F, Junot C. Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests. Anal Bioanal Chem 2022; 414:759-789. [PMID: 34432105 PMCID: PMC8386160 DOI: 10.1007/s00216-021-03586-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/30/2022]
Abstract
Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.
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Affiliation(s)
- Florence Anne Castelli
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- MetaboHUB, Gif-sur-Yvette, France
| | - Giulio Rosati
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Christian Moguet
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - Celia Fuentes
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Jose Marrugo-Ramírez
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Thibaud Lefebvre
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- Centre de Recherche sur l'Inflammation/CRI, Université de Paris, Inserm, Paris, France
- CRMR Porphyrie, Hôpital Louis Mourier, AP-HP Nord - Université de Paris, Colombes, France
| | - Hervé Volland
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - Arben Merkoçi
- Institut Català de Nanociència i Nanotecnologia (ICN2), Edifici ICN2 Campus UAB, 08193 Bellaterra, Barcelona, Spain
| | - Stéphanie Simon
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
| | - François Fenaille
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France
- MetaboHUB, Gif-sur-Yvette, France
| | - Christophe Junot
- Université Paris-Saclay, CEA, INRAE, Département Médicaments et Technologies pour la Santé (MTS), Gif-sur-Yvette cedex, 91191, France.
- MetaboHUB, Gif-sur-Yvette, France.
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13
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Optimization of metabolomic data processing using NOREVA. Nat Protoc 2022; 17:129-151. [PMID: 34952956 DOI: 10.1038/s41596-021-00636-9] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022]
Abstract
A typical output of a metabolomic experiment is a peak table corresponding to the intensity of measured signals. Peak table processing, an essential procedure in metabolomics, is characterized by its study dependency and combinatorial diversity. While various methods and tools have been developed to facilitate metabolomic data processing, it is challenging to determine which processing workflow will give good performance for a specific metabolomic study. NOREVA, an out-of-the-box protocol, was therefore developed to meet this challenge. First, the peak table is subjected to many processing workflows that consist of three to five defined calculations in combinatorially determined sequences. Second, the results of each workflow are judged against objective performance criteria. Third, various benchmarks are analyzed to highlight the uniqueness of this newly developed protocol in (1) evaluating the processing performance based on multiple criteria, (2) optimizing data processing by scanning thousands of workflows, and (3) allowing data processing for time-course and multiclass metabolomics. This protocol is implemented in an R package for convenient accessibility and to protect users' data privacy. Preliminary experience in R language would facilitate the usage of this protocol, and the execution time may vary from several minutes to a couple of hours depending on the size of the analyzed data.
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14
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Wishart DS, Sayeeda Z, Budinski Z, Guo A, Lee BL, Berjanskii M, Rout M, Peters H, Dizon R, Mah R, Torres-Calzada C, Hiebert-Giesbrecht M, Varshavi D, Varshavi D, Oler E, Allen D, Cao X, Gautam V, Maras A, Poynton EF, Tavangar P, Yang V, van Santen JA, Ghosh R, Sarma S, Knutson E, Sullivan V, Jystad AM, Renslow R, Sumner LW, Linington RG, Cort JR. NP-MRD: the Natural Products Magnetic Resonance Database. Nucleic Acids Res 2021; 50:D665-D677. [PMID: 34791429 PMCID: PMC8728158 DOI: 10.1093/nar/gkab1052] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 11/15/2022] Open
Abstract
The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.,Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada.,Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Zinat Sayeeda
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Zachary Budinski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - AnChi Guo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Brian L Lee
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Mark Berjanskii
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Manoj Rout
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Harrison Peters
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Raynard Dizon
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Robert Mah
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | | | | | - Dorna Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dorsa Varshavi
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Eponine Oler
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Dana Allen
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Xuan Cao
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Andrew Maras
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Ella F Poynton
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Pegah Tavangar
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Vera Yang
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | | | - Rajarshi Ghosh
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Saurav Sarma
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Eleanor Knutson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Victoria Sullivan
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Amy M Jystad
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Ryan Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Lloyd W Sumner
- Department of Biochemistry, University of Missouri, Columbia, MO 65211, USA.,MU Metabolomics Center, University of Missouri, Columbia, MO 65211, USA.,Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - John R Cort
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
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15
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Beniddir MA, Kang KB, Genta-Jouve G, Huber F, Rogers S, van der Hooft JJJ. Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches. Nat Prod Rep 2021; 38:1967-1993. [PMID: 34821250 PMCID: PMC8597898 DOI: 10.1039/d1np00023c] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Indexed: 12/13/2022]
Abstract
Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.
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Affiliation(s)
- Mehdi A Beniddir
- Université Paris-Saclay, CNRS, BioCIS, 5 rue J.-B Clément, 92290 Châtenay-Malabry, France
| | - Kyo Bin Kang
- Research Institute of Pharmaceutical Sciences, College of Pharmacy, Sookmyung Women's University, Seoul 04310, Republic of Korea
| | - Grégory Genta-Jouve
- Laboratoire de Chimie-Toxicologie Analytique et Cellulaire (C-TAC), UMR CNRS 8038, CiTCoM, Université de Paris, 4, Avenue de l'Observatoire, 75006, Paris, France
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 275 Route de Montabo, 97334 Cayenne, French Guiana, France
| | - Florian Huber
- Netherlands eScience Center, 1098 XG Amsterdam, The Netherlands
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK
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16
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Hammer AS, Leonov AI, Bell NL, Cronin L. Chemputation and the Standardization of Chemical Informatics. JACS AU 2021; 1:1572-1587. [PMID: 34723260 PMCID: PMC8549037 DOI: 10.1021/jacsau.1c00303] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Indexed: 05/11/2023]
Abstract
The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.
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17
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Johnson D, Batista D, Cochrane K, Davey RP, Etuk A, Gonzalez-Beltran A, Haug K, Izzo M, Larralde M, Lawson TN, Minotto A, Moreno P, Nainala VC, O'Donovan C, Pireddu L, Roger P, Shaw F, Steinbeck C, Weber RJM, Sansone SA, Rocca-Serra P. ISA API: An open platform for interoperable life science experimental metadata. Gigascience 2021; 10:giab060. [PMID: 34528664 PMCID: PMC8444265 DOI: 10.1093/gigascience/giab060] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 03/19/2021] [Accepted: 08/23/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open source community specifications and software tools for enabling discovery, exchange, and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab-a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, the JSON serialization ISA-JSON was developed. RESULTS In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python object classes. We describe the ISA API feature set, early adopters, and its growing user community. CONCLUSIONS The ISA API provides users with rich programmatic metadata-handling functionality to support automation, a common interface, and an interoperable medium between the 2 ISA formats, as well as with other life science data formats required for depositing data in public databases.
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Affiliation(s)
- David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
- Department of Informatics and Media, Uppsala University, Box 513, 75120 Uppsala, Sweden
| | - Dominique Batista
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Keeva Cochrane
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Robert P Davey
- Earlham Institute, Data infrastructure and algorithms, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Anthony Etuk
- Earlham Institute, Data infrastructure and algorithms, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
- Science and Technology Facilities Council, Scientific Computing Department, Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, UK
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
- Genome Research Limited, Wellcome Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Saffron Walden, CB10 1RQ, UK
| | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Martin Larralde
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Thomas N Lawson
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Alice Minotto
- Earlham Institute, Data infrastructure and algorithms, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Venkata Chandrasekhar Nainala
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Luca Pireddu
- Distributed Computing Group, CRS4: Center for Advanced Studies, Research & Development in Sardinia, Pula 09050, Italy
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Felix Shaw
- Earlham Institute, Data infrastructure and algorithms, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, Oxford, OX1 3QG, UK
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18
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Kikuchi J, Yamada S. The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science. RSC Adv 2021; 11:30426-30447. [PMID: 35480260 PMCID: PMC9041152 DOI: 10.1039/d1ra03008f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.
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Affiliation(s)
- Jun Kikuchi
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Graduate School of Bioagricultural Sciences, Nagoya University Furo-cho, Chikusa-ku Nagoya 464-8601 Japan
- Graduate School of Medical Life Science, Yokohama City University 1-7-29 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
| | - Shunji Yamada
- Environmental Metabolic Analysis Research Team, RIKEN Center for Sustainable Resource Science 1-7-22 Suehiro-cho, Tsurumi-ku Yokohama 230-0045 Japan
- Prediction Science Laboratory, RIKEN Cluster for Pioneering Research 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
- Data Assimilation Research Team, RIKEN Center for Computational Science 7-1-26 Minatojima-minami-machi, Chuo-ku Kobe 650-0047 Japan
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Kuhn S, Wieske LHE, Trevorrow P, Schober D, Schlörer NE, Nuzillard JM, Kessler P, Junker J, Herráez A, Farès C, Erdélyi M, Jeannerat D. NMReDATA: Tools and applications. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:792-803. [PMID: 33729627 DOI: 10.1002/mrc.5146] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
The nuclear magnetic resonance extracted data (NMReDATA) format has been proposed as a way to store, exchange, and disseminate nuclear magnetic resonance (NMR) data and physical and chemical metadata of chemical compounds. In this paper, we report on analytical workflows that take advantage of the uniform and standardized NMReDATA format. We also give access to a repository of sample data, which can serve for validating software packages that encode or decode files in NMReDATA format.
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Affiliation(s)
- Stefan Kuhn
- School of Computer Science and Informatics, De Montfort University, Leicester, UK
| | | | | | - Daniel Schober
- Ontology Development, MatterWaveSemantics, Südharz, Germany
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Halle (Saale), Germany
| | - Nils E Schlörer
- Department of Chemistry, University of Cologne, Köln, Germany
| | | | | | - Jochen Junker
- Center for Technological Development in Public Health, Fundação Oswaldo Cruz - CDTS, Rio de Janeiro - RJ, Brazil
| | - Angel Herráez
- Department of Systems Biology, Universidad de Alcalá, Alcalá de Henares, Spain
| | - Christophe Farès
- Abteilung NMR, Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr, Germany
| | - Mate Erdélyi
- Department of Chemistry - BMC, Uppsala Universitet, Uppsala, Sweden
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20
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Kupče Ē, Frydman L, Webb AG, Yong JRJ, Claridge TDW. Parallel nuclear magnetic resonance spectroscopy. ACTA ACUST UNITED AC 2021. [DOI: 10.1038/s43586-021-00024-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Automatic 1D 1H NMR Metabolite Quantification for Bioreactor Monitoring. Metabolites 2021; 11:metabo11030157. [PMID: 33803350 PMCID: PMC8001003 DOI: 10.3390/metabo11030157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/24/2021] [Accepted: 03/05/2021] [Indexed: 12/23/2022] Open
Abstract
High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.
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22
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Nie H, Luo P, Fu P. Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data. DATA INTELLIGENCE 2021. [DOI: 10.1162/dint_a_00088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.
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Affiliation(s)
- Hua Nie
- Peking University Library, Beijing 100871, China
| | | | - Ping Fu
- Central Washington University Library, 400 E. University Way, Ellensburg WA 98926, USA
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23
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Koskela H, Cavalcante SFDA, Ahmed S, Vanninen P. Quantum mechanical reference spectrum simulation for precursors and degradation products of chemicals relevant to the Chemical Weapons Convention. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2021; 59:117-137. [PMID: 32865833 DOI: 10.1002/mrc.5090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 08/19/2020] [Accepted: 08/24/2020] [Indexed: 06/11/2023]
Abstract
A selection of acidic, alkaline and neutral degradation products relevant to the Chemical Weapons Convention was studied in wide range of pH conditions to determine their spin systems as well as spectral parameters. The pH dependence of chemical shifts and J couplings was parameterized using Henderson-Hasselbalch-based functions using dichloromethane as additional shift reference in TSP-d4 referenced spectra. The resulting parameters allowed calculation of precise chemical shifts and J coupling constants in arbitrary pH conditions. The validity of the obtained spin system definitions and parameters as a source of quantum mechanically simulated reference data in chemical verification analysis is demonstrated.
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Affiliation(s)
- Harri Koskela
- VERIFIN, Department of Chemistry, University of Helsinki, Helsinki, Finland
| | - Samir F de A Cavalcante
- Brazilian Army Institute of CBRN Defense (IDQBRN), Rio de Janeiro, Brazil
- Walter Mors Institute of Research on Natural Products (IPPN), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Samim Ahmed
- Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
| | - Paula Vanninen
- VERIFIN, Department of Chemistry, University of Helsinki, Helsinki, Finland
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24
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Steinbeck C, Koepler O, Bach F, Herres-Pawlis S, Jung N, Liermann J, Neumann S, Razum M, Baldauf C, Biedermann F, Bocklitz T, Boehm F, Broda F, Czodrowski P, Engel T, Hicks M, Kast S, Kettner C, Koch W, Lanza G, Link A, Mata R, Nagel W, Porzel A, Schlörer N, Schulze T, Weinig HG, Wenzel W, Wessjohann L, Wulle S. NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany. RESEARCH IDEAS AND OUTCOMES 2020. [DOI: 10.3897/rio.6.e55852] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation.
This overarching goal is achieved by working towards a number of key objectives:
Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories.
Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack.
Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula.
Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers.
Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI.
Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.
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25
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Emami Khoonsari P, Moreno P, Bergmann S, Burman J, Capuccini M, Carone M, Cascante M, de Atauri P, Foguet C, Gonzalez-Beltran AN, Hankemeier T, Haug K, He S, Herman S, Johnson D, Kale N, Larsson A, Neumann S, Peters K, Pireddu L, Rocca-Serra P, Roger P, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Schober D, Selivanov V, Thévenot EA, van Vliet M, Zanetti G, Steinbeck C, Kultima K, Spjuth O. Interoperable and scalable data analysis with microservices: applications in metabolomics. Bioinformatics 2019; 35:3752-3760. [PMID: 30851093 PMCID: PMC6761976 DOI: 10.1093/bioinformatics/btz160] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 02/25/2019] [Accepted: 03/08/2019] [Indexed: 12/20/2022] Open
Abstract
MOTIVATION Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. RESULTS We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. AVAILABILITY AND IMPLEMENTATION The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Payam Emami Khoonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joachim Burman
- Department of Neuroscience, Uppsala University, Uppsala, Sweden
| | - Marco Capuccini
- Department of Information Technology, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Matteo Carone
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | | | - Thomas Hankemeier
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Sijin He
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Stephanie Herman
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Steffen Neumann
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
| | - Kristian Peters
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Luca Pireddu
- CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, France
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Noureddin Sadawi
- Faculty of Medicine, Department of Surgery & Cancer, Imperial College London, London, UK
| | - Reza M Salek
- International Agency for Research on Cancer, 69372 Lyon CEDEX 08, France
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Daniel Schober
- Department of Stress and Developmental Biology, Leibniz Institute of Plant Biochemistry, Halle, Germany
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, and Institute of Biomedicine (IBUB), Faculty of Biology, Universitat de Barcelona (IBUB), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) and Metabolomics Node at INB-Bioinfarmatics Platform, Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems' Intelligence, MetaboHUB, Gif-sur-Yvette, France
| | - Michael van Vliet
- Division of Analytical Biosciences, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Gianluigi Zanetti
- CRS4: Center for Advanced Studies, Research and Development in Sardinia, Distributed Computing Group, Pula, Italy
| | - Christoph Steinbeck
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Jena, Germany
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, Uppsala, Sweden
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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26
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Wishart DS. NMR metabolomics: A look ahead. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 306:155-161. [PMID: 31377153 DOI: 10.1016/j.jmr.2019.07.013] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 04/13/2019] [Accepted: 07/08/2019] [Indexed: 05/24/2023]
Abstract
NMR has been used to perform metabolic studies, metabolic profiling and metabolomics in biofluids and tissues for more than 40 years. This close connection between metabolic measurements and NMR has flourished because of NMR's many unique strengths for characterizing the chemical composition of complex mixtures. However, a number of other technologies, including mass spectrometry, have appeared in the past few years that are encroaching on NMR's dominance in metabolomics and metabolic studies. In this brief review, some of the current strengths and existing limitations of NMR-based metabolomics are highlighted. Additionally, a number of recent advances in NMR hardware, methodology and software are also described and these advancements are used to speculate about where NMR-based metabolomics is going, what needs to be done to make it more popular and how it will evolve in the next 5-10 years.
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Affiliation(s)
- David S Wishart
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada; Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada.
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27
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Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 2019; 46:D1074-D1082. [PMID: 29126136 PMCID: PMC5753335 DOI: 10.1093/nar/gkx1037] [Citation(s) in RCA: 4540] [Impact Index Per Article: 908.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 11/03/2017] [Indexed: 12/11/2022] Open
Abstract
DrugBank (www.drugbank.ca) is a web-enabled database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets. First described in 2006, DrugBank has continued to evolve over the past 12 years in response to marked improvements to web standards and changing needs for drug research and development. This year's update, DrugBank 5.0, represents the most significant upgrade to the database in more than 10 years. In many cases, existing data content has grown by 100% or more over the last update. For instance, the total number of investigational drugs in the database has grown by almost 300%, the number of drug-drug interactions has grown by nearly 600% and the number of SNP-associated drug effects has grown more than 3000%. Significant improvements have been made to the quantity, quality and consistency of drug indications, drug binding data as well as drug-drug and drug-food interactions. A great deal of brand new data have also been added to DrugBank 5.0. This includes information on the influence of hundreds of drugs on metabolite levels (pharmacometabolomics), gene expression levels (pharmacotranscriptomics) and protein expression levels (pharmacoprotoemics). New data have also been added on the status of hundreds of new drug clinical trials and existing drug repurposing trials. Many other important improvements in the content, interface and performance of the DrugBank website have been made and these should greatly enhance its ease of use, utility and potential applications in many areas of pharmacological research, pharmaceutical science and drug education.
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Affiliation(s)
- David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.,Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2N8, Canada.,Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Yannick D Feunang
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - An C Guo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Elvis J Lo
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ana Marcu
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Jason R Grant
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Tanvir Sajed
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Daniel Johnson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Carin Li
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Zinat Sayeeda
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nazanin Assempour
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Ithayavani Iynkkaran
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Yifeng Liu
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Adam Maciejewski
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
| | - Nicola Gale
- OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Alex Wilson
- OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Lucy Chin
- OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Ryan Cummings
- OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Diana Le
- OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Allison Pon
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Craig Knox
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
| | - Michael Wilson
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada.,OMx Personal Health Analytics, Inc., 301-10359 104 St NW, Edmonton, AB T5J 1B9, Canada
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28
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Emwas AH, Roy R, McKay RT, Tenori L, Saccenti E, Gowda GAN, Raftery D, Alahmari F, Jaremko L, Jaremko M, Wishart DS. NMR Spectroscopy for Metabolomics Research. Metabolites 2019; 9:E123. [PMID: 31252628 PMCID: PMC6680826 DOI: 10.3390/metabo9070123] [Citation(s) in RCA: 504] [Impact Index Per Article: 100.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 06/14/2019] [Accepted: 06/18/2019] [Indexed: 12/14/2022] Open
Abstract
Over the past two decades, nuclear magnetic resonance (NMR) has emerged as one of the three principal analytical techniques used in metabolomics (the other two being gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled with single-stage mass spectrometry (LC-MS)). The relative ease of sample preparation, the ability to quantify metabolite levels, the high level of experimental reproducibility, and the inherently nondestructive nature of NMR spectroscopy have made it the preferred platform for long-term or large-scale clinical metabolomic studies. These advantages, however, are often outweighed by the fact that most other analytical techniques, including both LC-MS and GC-MS, are inherently more sensitive than NMR, with lower limits of detection typically being 10 to 100 times better. This review is intended to introduce readers to the field of NMR-based metabolomics and to highlight both the advantages and disadvantages of NMR spectroscopy for metabolomic studies. It will also explore some of the unique strengths of NMR-based metabolomics, particularly with regard to isotope selection/detection, mixture deconvolution via 2D spectroscopy, automation, and the ability to noninvasively analyze native tissue specimens. Finally, this review will highlight a number of emerging NMR techniques and technologies that are being used to strengthen its utility and overcome its inherent limitations in metabolomic applications.
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Affiliation(s)
- Abdul-Hamid Emwas
- Core Labs, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Raja Roy
- Centre of Biomedical Research, Formerly, Centre of Biomedical Magnetic Resonance, Sanjay Gandhi Post-Graduate Institute of Medical Sciences Campus, Uttar Pradesh 226014, India
| | - Ryan T McKay
- Department of Chemistry, University of Alberta, Edmonton, AB T6G 2W2, Canada
| | - Leonardo Tenori
- Department of Experimental and Clinical Medicine, University of Florence, Largo Brambilla 3, 50134 Florence, Italy
| | - Edoardo Saccenti
- Laboratory of Systems and Synthetic Biology Wageningen University & Research, Stippeneng 4, 6708 WE Wageningen, The Netherlands
| | - G A Nagana Gowda
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, 850 Republican St., Seattle, WA 98109, USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue, Seattle, WA 98109, USA
| | - Fatimah Alahmari
- Department of NanoMedicine Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman bin Faisal University, Dammam 31441, Saudi Arabia
| | - Lukasz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Mariusz Jaremko
- Division of Biological and Environmental Sciences and Engineering (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
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29
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Hoffmann N, Rein J, Sachsenberg T, Hartler J, Haug K, Mayer G, Alka O, Dayalan S, Pearce JTM, Rocca-Serra P, Qi D, Eisenacher M, Perez-Riverol Y, Vizcaíno JA, Salek RM, Neumann S, Jones AR. mzTab-M: A Data Standard for Sharing Quantitative Results in Mass Spectrometry Metabolomics. Anal Chem 2019; 91:3302-3310. [PMID: 30688441 PMCID: PMC6660005 DOI: 10.1021/acs.analchem.8b04310] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 01/28/2019] [Indexed: 12/29/2022]
Abstract
Mass spectrometry (MS) is one of the primary techniques used for large-scale analysis of small molecules in metabolomics studies. To date, there has been little data format standardization in this field, as different software packages export results in different formats represented in XML or plain text, making data sharing, database deposition, and reanalysis highly challenging. Working within the consortia of the Metabolomics Standards Initiative, Proteomics Standards Initiative, and the Metabolomics Society, we have created mzTab-M to act as a common output format from analytical approaches using MS on small molecules. The format has been developed over several years, with input from a wide range of stakeholders. mzTab-M is a simple tab-separated text format, but importantly, the structure is highly standardized through the design of a detailed specification document, tightly coupled to validation software, and a mandatory controlled vocabulary of terms to populate it. The format is able to represent final quantification values from analyses, as well as the evidence trail in terms of features measured directly from MS (e.g., LC-MS, GC-MS, DIMS, etc.) and different types of approaches used to identify molecules. mzTab-M allows for ambiguity in the identification of molecules to be communicated clearly to readers of the files (both people and software). There are several implementations of the format available, and we anticipate widespread adoption in the field.
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Affiliation(s)
- Nils Hoffmann
- Leibniz-Institut
für Analytische Wissenschaften-ISAS-e.V., Otto-Hahn-Straße 6b, 44227 Dortmund, Germany
| | - Joel Rein
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, United Kingdom
| | - Timo Sachsenberg
- Applied Bioinformatics
Group, Center for Bioinformatics, University
of Tübingen, Sand
14, 72076 Tübingen, Germany
| | - Jürgen Hartler
- Institute of Computational Biotechnology, Graz University of Technology, Petersgasse 14, 8010 Graz, Austria
- Center
for Explorative Lipidomics, BioTechMed-Graz, 8010 Graz, Austria
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Gerhard Mayer
- Medizinisches Proteom Center (MPC), Ruhr-Universität
Bochum, Universitätsstraße
150, D-44801 Bochum, Germany
| | - Oliver Alka
- Applied Bioinformatics
Group, Center for Bioinformatics, University
of Tübingen, Sand
14, 72076 Tübingen, Germany
| | - Saravanan Dayalan
- Metabolomics Australia, The University
of Melbourne, Parkville, Victoria 3010, Australia
| | - Jake T. M. Pearce
- MRC-NIHR National Phenome Centre, Department of Surgery & Cancer, Imperial College London, London SW7 2AZ, United Kingdom
| | - Philippe Rocca-Serra
- University of Oxford, e-Research Centre, 7 Keble Road, Oxford OX1
3QG, United Kingdom
| | - Da Qi
- BGI-Shenzhen, Shenzhen, 518083, People’s Republic of China
- Institute
of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Martin Eisenacher
- Medizinisches Proteom Center (MPC), Ruhr-Universität
Bochum, Universitätsstraße
150, D-44801 Bochum, Germany
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Reza M. Salek
- International Agency for Research on Cancer, 150 cours Albert Thomas, 69008 Lyon, France
| | - Steffen Neumann
- Department
of Stress and Developmental Biology, Leibniz
Institute of Plant Biochemistry, 06120 Halle, Germany
- German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig Deutscher, Platz
5e, 04103 Leipzig, Germany
| | - Andrew R. Jones
- Institute
of Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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30
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Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone SA, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck C. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 2019; 8:giy149. [PMID: 30535405 PMCID: PMC6377398 DOI: 10.1093/gigascience/giy149] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 10/19/2018] [Accepted: 11/20/2018] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.
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Affiliation(s)
- Kristian Peters
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - James Bradbury
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Capuccini
- Division of Scientific Computing, Department of Information Technology, Uppsala University, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Pedro de Atauri
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Timothy M D Ebbels
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Robert Glen
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB21EW, United Kingdom
| | - Alejandra Gonzalez-Beltran
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Ulrich L Günther
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Evangelos Handakas
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Thomas Hankemeier
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Kenneth Haug
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Stephanie Herman
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | | | - Massimiliano Izzo
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Daniel Jacob
- INRA, University of Bordeaux, Plateforme Métabolome Bordeaux-MetaboHUB, 33140 Villenave d'Ornon, France
| | - David Johnson
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
- Department of Informatics and Media, Uppsala University, Box 513, 751 20 Uppsala, Sweden
| | - Fabien Jourdan
- INRA - French National Institute for Agricultural Research, UMR1331, Toxalim, Research Centre in Food Toxicology, Toulouse, France
| | - Namrata Kale
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Ibrahim Karaman
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, United Kingdom
| | - Bita Khalili
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Payam Emami Khonsari
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Kim Kultima
- Department of Medical Sciences, Clinical Chemistry, Uppsala University, 751 85 Uppsala, Sweden
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Anders Larsson
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Pablo Moreno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103 Leipzig, Germany
| | - Jon Ander Novella
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
- National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Claire O'Donovan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Jake T M Pearce
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Alina Peluso
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | | | | | - Michelle A C Reed
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Philippe Rocca-Serra
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Pierrick Roger
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Antonio Rosato
- Magnetic Resonance Center (CERM) and Department of Chemistry, University of Florence and CIRMMP, 50019 Sesto Fiorentino, Florence, Italy
| | - Rico Rueedi
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Christoph Ruttkies
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Noureddin Sadawi
- Department of Computer Science, College of Engineering, Design and Physical Sciences, Brunel University, London, UK
- Department of Surgery & Cancer, Imperial College London, South Kensington, London, SW7 2AZ, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Susanna-Assunta Sansone
- Oxford e-Research Centre, Department of Engineering Science, University of Oxford, 7 Keble Road, OX1 3QG, Oxford, United Kingdom
| | - Vitaly Selivanov
- Department of Biochemistry and Molecular Biomedicine, Universitat de Barcelona; Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Instituto de Salud Carlos III (ISCIII), Spain
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden
| | - Daniel Schober
- Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany
| | - Etienne A Thévenot
- CEA, LIST, Laboratory for Data Analysis and Systems’ Intelligence, MetaboHUB, Gif-Sur-Yvette F-91191, France
| | - Mattia Tomasoni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Merlijn van Rijswijk
- Netherlands Metabolomics Center, Leiden, 2333 CC, Netherlands
- ELIXIR-NL, Dutch Techcentre for Life Sciences, Utrecht, 3503 RM, Netherlands
| | - Michael van Vliet
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, 2333 CC, The Netherlands
| | - Mark R Viant
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
- Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | | | - Christoph Steinbeck
- Cheminformatics and Computational Metabolomics, Institute for Analytical Chemistry, Lessingstr. 8, 07743 Jena, Germany
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Tabatabaei Anaraki M, Bermel W, Dutta Majumdar R, Soong R, Simpson M, Monnette M, Simpson AJ. 1D "Spikelet" Projections from Heteronuclear 2D NMR Data-Permitting 1D Chemometrics While Preserving 2D Dispersion. Metabolites 2019; 9:metabo9010016. [PMID: 30654443 PMCID: PMC6358932 DOI: 10.3390/metabo9010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/08/2019] [Accepted: 01/09/2019] [Indexed: 12/19/2022] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this application note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This application note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers.
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Affiliation(s)
- Maryam Tabatabaei Anaraki
- Environmental NMR Center, Department of Physical and Environmental Sciences, University of Toronto Scarborough, Military Trial, Toronto, ON 1265, Canada.
| | - Wolfgang Bermel
- Bruker BioSpin GmbH, Silberstreifen 4, 76287 Rheinstetten, Germany.
| | | | - Ronald Soong
- Environmental NMR Center, Department of Physical and Environmental Sciences, University of Toronto Scarborough, Military Trial, Toronto, ON 1265, Canada.
| | - Myrna Simpson
- Environmental NMR Center, Department of Physical and Environmental Sciences, University of Toronto Scarborough, Military Trial, Toronto, ON 1265, Canada.
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M1C 1A4, Canada.
| | | | - André J Simpson
- Environmental NMR Center, Department of Physical and Environmental Sciences, University of Toronto Scarborough, Military Trial, Toronto, ON 1265, Canada.
- Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON M1C 1A4, Canada.
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McAlpine JB, Chen SN, Kutateladze A, MacMillan JB, Appendino G, Barison A, Beniddir MA, Biavatti MW, Bluml S, Boufridi A, Butler MS, Capon RJ, Choi YH, Coppage D, Crews P, Crimmins MT, Csete M, Dewapriya P, Egan JM, Garson MJ, Genta-Jouve G, Gerwick WH, Gross H, Harper MK, Hermanto P, Hook JM, Hunter L, Jeannerat D, Ji NY, Johnson TA, Kingston DGI, Koshino H, Lee HW, Lewin G, Li J, Linington RG, Liu M, McPhail KL, Molinski TF, Moore BS, Nam JW, Neupane RP, Niemitz M, Nuzillard JM, Oberlies NH, Ocampos FMM, Pan G, Quinn RJ, Reddy DS, Renault JH, Rivera-Chávez J, Robien W, Saunders CM, Schmidt TJ, Seger C, Shen B, Steinbeck C, Stuppner H, Sturm S, Taglialatela-Scafati O, Tantillo DJ, Verpoorte R, Wang BG, Williams CM, Williams PG, Wist J, Yue JM, Zhang C, Xu Z, Simmler C, Lankin DC, Bisson J, Pauli GF. The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research. Nat Prod Rep 2019; 36:35-107. [PMID: 30003207 PMCID: PMC6350634 DOI: 10.1039/c7np00064b] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Indexed: 12/20/2022]
Abstract
Covering: up to 2018With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.
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Affiliation(s)
- James B McAlpine
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
| | - Shao-Nong Chen
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
| | - Andrei Kutateladze
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO 80210, USA
| | - John B MacMillan
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA
| | - Giovanni Appendino
- Dipartimento di Scienze Chimiche, Alimentari, Farmaceutiche e Farmacologiche, Universita` del Piemonte Orientale, Via Bovio 6, 28100 Novara, Italy
| | | | - Mehdi A Beniddir
- Équipe "Pharmacognosie-Chimie des Substances Naturelles" BioCIS, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 5 rue J.-B. Clément, 92290 Châtenay-Malabry, France
| | - Maique W Biavatti
- Department of Pharmaceutical Sciences, Federal University of Santa Catarina, Florianópolis, Brazil
| | - Stefan Bluml
- University of Southern California, Keck School of Medicine, Los Angeles, CA 90089, USA
| | - Asmaa Boufridi
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - Mark S Butler
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
| | - Robert J Capon
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
| | - Young H Choi
- Division of Pharmacognosy, Section Metabolomics, Institute of Biology, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - David Coppage
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA
| | - Phillip Crews
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA
| | - Michael T Crimmins
- Kenan and Caudill Laboratories of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Marie Csete
- University of Southern California, Huntington Medical Research Institutes, 99 N. El Molino Ave., Pasadena, CA 91101, USA
| | - Pradeep Dewapriya
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4072, Australia
| | - Joseph M Egan
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Mary J Garson
- School of Chemistry and Molecular Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Grégory Genta-Jouve
- C-TAC, UMR 8638 CNRS, Faculté de Pharmacie de Paris, Paris-Descartes University, Sorbonne, Paris Cité, 4, Aveue de l'Observatoire, 75006 Paris, France
| | - William H Gerwick
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA 92093, USA and Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, La Jolla, CA 92093, USA
| | - Harald Gross
- Pharmaceutical Institute, Department of Pharmaceutical Biology, Eberhard Karls University of Tübingen, Auf der Morgenstelle 8, 72076 Tübingen, Germany
| | - Mary Kay Harper
- Department of Medicinal Chemistry, University of Utah, Salt Lake City, UT 84112, USA
| | - Precilia Hermanto
- NMR Facility, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - James M Hook
- NMR Facility, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - Luke Hunter
- NMR Facility, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW 2052, Australia
| | - Damien Jeannerat
- University of Geneva, Department of Organic Chemistry, 30 quai E. Ansermet, CH 1211 Geneva 4, Switzerland
| | - Nai-Yun Ji
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Chunhui Road 17, Yantai 264003, People's Republic of China
| | - Tyler A Johnson
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA
| | - David G I Kingston
- Department of Chemistry, M/C 0212, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
| | - Hiroyuki Koshino
- RIKEN Center for Sustainable Resource Science, Wako, Saitama, 351-0198, Japan
| | - Hsiau-Wei Lee
- Department of Chemistry and Biochemistry, University of California, Santa Cruz, CA 95064, USA
| | - Guy Lewin
- Équipe "Pharmacognosie-Chimie des Substances Naturelles" BioCIS, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 5 rue J.-B. Clément, 92290 Châtenay-Malabry, France
| | - Jie Li
- Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, La Jolla, CA 92093, USA
| | - Roger G Linington
- Department of Chemistry, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Miaomiao Liu
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - Kerry L McPhail
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR 97331, USA
| | - Tadeusz F Molinski
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Bradley S Moore
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, La Jolla, San Diego, CA 92093, USA and Center for Marine Biotechnology and Biomedicine, Scripps Institution of Oceanography, La Jolla, CA 92093, USA
| | - Joo-Won Nam
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Ram P Neupane
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Matthias Niemitz
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Jean-Marc Nuzillard
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Nicholas H Oberlies
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | | | - Guohui Pan
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Ronald J Quinn
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, QLD 4111, Australia
| | - D Sai Reddy
- Department of Chemistry and Biochemistry, University of Denver, Denver, CO 80210, USA
| | - Jean-Hugues Renault
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - José Rivera-Chávez
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Wolfgang Robien
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Carla M Saunders
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Thomas J Schmidt
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Christoph Seger
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Ben Shen
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Christoph Steinbeck
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Hermann Stuppner
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Sonja Sturm
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Orazio Taglialatela-Scafati
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Dean J Tantillo
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Robert Verpoorte
- Division of Pharmacognosy, Section Metabolomics, Institute of Biology, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands
| | - Bin-Gui Wang
- Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Chunhui Road 17, Yantai 264003, People's Republic of China and Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Craig M Williams
- School of Chemistry and Molecular Sciences, University of Queensland, St. Lucia, QLD 4072, Australia
| | - Philip G Williams
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Julien Wist
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Jian-Min Yue
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Chen Zhang
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Zhengren Xu
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. , and
| | - Charlotte Simmler
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
| | - David C Lankin
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
| | - Jonathan Bisson
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
| | - Guido F Pauli
- Center for Natural Product Technologies (CENAPT), Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 S. Wood St., Chicago, IL 60612, USA. ,
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Allard PM, Bisson J, Azzollini A, Pauli GF, Cordell GA, Wolfender JL. Pharmacognosy in the digital era: shifting to contextualized metabolomics. Curr Opin Biotechnol 2018; 54:57-64. [PMID: 29499476 PMCID: PMC6110999 DOI: 10.1016/j.copbio.2018.02.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Revised: 01/26/2018] [Accepted: 02/13/2018] [Indexed: 01/01/2023]
Abstract
Humans have co-evolved alongside numerous other organisms, some having a profound effect on health and nutrition. As the earliest pharmaceutical subject, pharmacognosy has evolved into a meta-discipline devoted to natural biomedical agents and their functional properties. While the acquisition of expanding data volumes is ongoing, contextualization is lagging. Thus, we assert that the establishment of an integrated and open databases ecosystem will nurture the discipline. After proposing an epistemological framework of knowledge acquisition in pharmacognosy, this study focuses on recent computational and analytical approaches. It then elaborates on the flux of research data, where good practices could foster the implementation of more integrated systems, which will in turn help shaping the future of pharmacognosy and determine its constitutional societal relevance.
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Affiliation(s)
- Pierre-Marie Allard
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland.
| | - Jonathan Bisson
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Antonio Azzollini
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland
| | - Guido F Pauli
- Center for Natural Product Technologies, Program for Collaborative Research in the Pharmaceutical Sciences (PCRPS), and Department of Medicinal Chemistry and Pharmacognosy, College of Pharmacy, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Geoffrey A Cordell
- Natural Products Inc., Evanston, IL 60203, United States; Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, FL 32610, United States
| | - Jean-Luc Wolfender
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Rue Michel-Servet 1, CH-1211 Geneva 4, Switzerland
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Pupier M, Nuzillard JM, Wist J, Schlörer NE, Kuhn S, Erdelyi M, Steinbeck C, Williams AJ, Butts C, Claridge TD, Mikhova B, Robien W, Dashti H, Eghbalnia HR, Farès C, Adam C, Kessler P, Moriaud F, Elyashberg M, Argyropoulos D, Pérez M, Giraudeau P, Gil RR, Trevorrow P, Jeannerat D. NMReDATA, a standard to report the NMR assignment and parameters of organic compounds. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2018; 56:703-715. [PMID: 29656574 PMCID: PMC6226248 DOI: 10.1002/mrc.4737] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 02/22/2018] [Accepted: 03/25/2018] [Indexed: 05/29/2023]
Abstract
Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules. A new format is therefore introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the proposed chemical structure. These NMR parameters, which we shall call NMReDATA (for nuclear magnetic resonance extracted data), include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times, and diffusion rates. The file format is an extension of the existing Structure Data Format, which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, allowing automatic verification of published results, and for assisting the constitution of highly needed open-source structural databases.
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Affiliation(s)
- Marion Pupier
- Department of Organic Chemistry, University of Geneva, 30 Quai E. Ansermet, 1211 Geneva 4, Switzerland
| | - Jean-Marc Nuzillard
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, BP 1039, 51687, Reims Cedex 2, France
| | - Julien Wist
- Chemistry Department, Universidad del Valle, 76001 Cali, Colombia
| | - Nils E. Schlörer
- Department of Chemistry, University of Cologne, Greinstr. 4, 50939 Köln, Germany
| | - Stefan Kuhn
- Department of Chemistry, University of Cologne, Greinstr. 4, 50939 Köln, Germany
| | - Mate Erdelyi
- Department of Chemistry - BMC, Uppsala University, Husargatan 3, 752 37 Uppsala, Sweden
| | - Christoph Steinbeck
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller-University, Lessingstr. 8, 07743 Jena, Germany
| | - Antony J. Williams
- National Center for Computational Toxicology, Environmental Protection Agency, 109 T.W. Alexander Drive, Room D131I, Mail Drop D143-02, Research Triangle Park, NC 27711, USA
| | - Craig Butts
- School of Chemistry, Bristol University, BS8 1TS Bristol, UK
| | - Tim D.W. Claridge
- Department of Chemistry, University of Oxford, Chemistry Research Laboratory, Mansfield Road, Oxford OX1 3TA, UK
| | - Bozhana Mikhova
- Institute of Organic Chemistry with Centre of Phytochemistry, Bulgarian Academy of Sciences, Akad. G. Bonchev Str. Bl.9, Sofia 1113, Bulgaria
| | - Wolfgang Robien
- University of Vienna, Department of Organic Chemistry, Währingerstr. 38, 1090 Vienna, Austria
| | - Hesam Dashti
- Department of Biochemistry, National Magnetic Resonance Facility at Madison (NMRFAM), 433 Babcock Drive, Madison, WI, USA
| | - Hamid R. Eghbalnia
- Department of Biochemistry, National Magnetic Resonance Facility at Madison (NMRFAM), 433 Babcock Drive, Madison, WI, USA
| | - Christophe Farès
- Max-Planck-Institut für Kohlenforschung, Abteilung NMR, Kaiser-Wilhelm-Platz 1, 45470 Mülheim an der Ruhr, Germany
| | - Christian Adam
- Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Pavel Kessler
- Bruker BioSpin GmbH, Silberstreifen, 76287 Rheinstetten, Germany
| | - Fabrice Moriaud
- Bruker BioSpin AG, Industriestrasse 26, 8117 Fällanden, Switzerland
| | - Mikhail Elyashberg
- Moscow Department, Advanced Chemistry Development, 6 Akademik Bakulev Street, Moscow 117513, Russian Federation
| | - Dimitris Argyropoulos
- Advanced Chemistry Development, Inc. (ACD/Labs), Venture House, Arlington Square, Downshire Way, Bracknell, Berkshire RG12 1WA, UK
| | - Manuel Pérez
- Mestrelab Research, S.L., Feliciano Barrera 9B - Bajo, ES-15706 Santiago de Compostela, Spain
| | - Patrick Giraudeau
- EBSI Team, Chimie et Interdisciplinarité: Synthèse, Analyse, Modélisation (CEISAM) CNRS, UMR 6230, Université de Nantes, 92208, 2 rue de la Houssinière, BP 44322 Nantes, France
- Institut Universitaire de France, 1 rue Descartes, 75005 Paris Cedex 05, France
| | - Roberto R. Gil
- Department of Chemistry, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA
| | | | - Damien Jeannerat
- Department of Organic Chemistry, University of Geneva, 30 Quai E. Ansermet, 1211 Geneva 4, Switzerland
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Misra BB, Langefeld CD, Olivier M, Cox LA. Integrated Omics: Tools, Advances, and Future Approaches. J Mol Endocrinol 2018; 62:JME-18-0055. [PMID: 30006342 DOI: 10.1530/jme-18-0055] [Citation(s) in RCA: 206] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Revised: 07/02/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022]
Abstract
With the rapid adoption of high-throughput omic approaches to analyze biological samples such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can generate tera- to peta-byte sized data files on a daily basis. These data file sizes, together with differences in nomenclature among these data types, make the integration of these multi-dimensional omics data into biologically meaningful context challenging. Variously named as integrated omics, multi-omics, poly-omics, trans-omics, pan-omics, or shortened to just 'omics', the challenges include differences in data cleaning, normalization, biomolecule identification, data dimensionality reduction, biological contextualization, statistical validation, data storage and handling, sharing, and data archiving. The ultimate goal is towards the holistic realization of a 'systems biology' understanding of the biological question in hand. Commonly used approaches in these efforts are currently limited by the 3 i's - integration, interpretation, and insights. Post integration, these very large datasets aim to yield unprecedented views of cellular systems at exquisite resolution for transformative insights into processes, events, and diseases through various computational and informatics frameworks. With the continued reduction in costs and processing time for sample analyses, and increasing types of omics datasets generated such as glycomics, lipidomics, microbiomics, and phenomics, an increasing number of scientists in this interdisciplinary domain of bioinformatics face these challenges. We discuss recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
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Affiliation(s)
- Biswapriya B Misra
- B Misra, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Carl D Langefeld
- C Langefeld, Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Michael Olivier
- M Olivier, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
| | - Laura A Cox
- L Cox, Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, United States
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Rattray NJW, Deziel NC, Wallach JD, Khan SA, Vasiliou V, Ioannidis JPA, Johnson CH. Beyond genomics: understanding exposotypes through metabolomics. Hum Genomics 2018; 12:4. [PMID: 29373992 PMCID: PMC5787293 DOI: 10.1186/s40246-018-0134-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 01/11/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact. MAIN TEXT Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics. CONCLUSIONS Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.
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Affiliation(s)
- Nicholas J. W. Rattray
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT USA
| | - Nicole C. Deziel
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT USA
| | - Joshua D. Wallach
- Collaboration for Research Integrity and Transparency (CRIT), Yale Law School, New Haven, CT USA
- Center for Outcomes Research and Evaluation (CORE), Yale-New Haven Health System, New Haven, CT USA
| | - Sajid A. Khan
- Department of Surgery, Section of Surgical Oncology, Yale University School of Medicine, New Haven, CT USA
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT USA
| | - Vasilis Vasiliou
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT USA
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT USA
| | - John P. A. Ioannidis
- Stanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA USA
- Department of Health Research and Policy, Stanford University, Stanford, CA USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA USA
- Department of Statistics, Stanford University, Stanford, CA USA
- Meta-Research Innovation Center at Stanford, Stanford University, Stanford, CA USA
| | - Caroline H. Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven, CT USA
- Yale Cancer Center, Yale University School of Medicine, New Haven, CT USA
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Misra BB. New tools and resources in metabolomics: 2016-2017. Electrophoresis 2018; 39:909-923. [PMID: 29292835 DOI: 10.1002/elps.201700441] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2017] [Revised: 12/17/2017] [Accepted: 12/18/2017] [Indexed: 01/07/2023]
Abstract
Rapid advances in mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based platforms for metabolomics have led to an upsurge of data every single year. Newer high-throughput platforms, hyphenated technologies, miniaturization, and tool kits in data acquisition efforts in metabolomics have led to additional challenges in metabolomics data pre-processing, analysis, interpretation, and integration. Thanks to the informatics, statistics, and computational community, new resources continue to develop for metabolomics researchers. The purpose of this review is to provide a summary of the metabolomics tools, software, and databases that were developed or improved during 2016-2017, thus, enabling readers, developers, and researchers access to a succinct but thorough list of resources for further improvisation, implementation, and application in due course of time.
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Affiliation(s)
- Biswapriya B Misra
- Department of Internal Medicine, Section of Molecular Medicine, Medical Center Boulevard, Winston-Salem, NC, USA
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