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Pang Z, Xu L, Viau C, Lu Y, Salavati R, Basu N, Xia J. MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics. Nat Commun 2024; 15:3675. [PMID: 38693118 PMCID: PMC11063062 DOI: 10.1038/s41467-024-48009-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/18/2024] [Indexed: 05/03/2024] Open
Abstract
The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.
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Affiliation(s)
- Zhiqiang Pang
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Xu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Charles Viau
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Yao Lu
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada
| | - Reza Salavati
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Niladri Basu
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
- Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
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2
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Fakhlaei R, Selamat J, Abdull Razis AF, Sukor R, Ahmad S, Khatib A, Zou X. Development of a zebrafish model for toxicity evaluation of adulterated Apis mellifera honey. CHEMOSPHERE 2024; 356:141736. [PMID: 38554873 DOI: 10.1016/j.chemosphere.2024.141736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/09/2024] [Accepted: 03/15/2024] [Indexed: 04/02/2024]
Abstract
Since ancient times, honey has been used for medical purposes and the treatment of various disorders. As a high-quality food product, the honey industry is prone to fraud and adulteration. Moreover, limited experimental studies have investigated the impact of adulterated honey consumption using zebrafish as the animal model. The aims of this study were: (1) to calculate the lethal concentration (LC50) of acid-adulterated Apis mellifera honey on embryos, (2) to investigate the effect of pure and acid-adulterated A. mellifera honey on hatching rate (%) and heart rate of zebrafish (embryos and larvae), (3) to elucidate toxicology of selected adulterated honey based on lethal dose (LD50) using adult zebrafish and (4) to screen the metabolites profile of adulterated honey from blood serum of adult zebrafish. The result indicated the LC50 of 31.10 ± 1.63 (mg/ml) for pure A. mellifera honey, while acetic acid demonstrates the lowest LC50 (4.98 ± 0.06 mg/ml) among acid adulterants with the highest mortality rate at 96 hpf. The treatment of zebrafish embryos with adulterated A. mellifera honey significantly (p ≤ 0.05) increased the hatching rate (%) and decreased the heartbeat rate. Acute, prolong-acute, and sub-acute toxicology tests on adult zebrafish were conducted at a concentration of 7% w/w of acid adulterants. Furthermore, the blood serum metabolite profile of adulterated-honey-treated zebrafish was screened by LC-MS/MS analysis and three endogenous metabolites have been revealed: (1) Xanthotoxol or 8-Hydroxypsoralen, (2) 16-Oxoandrostenediol, and (3) 3,5-Dicaffeoyl-4-succinoylquinic acid. These results prove that employed honey adulterants cause mortality that contributes to higher toxicity. Moreover, this study introduces the zebrafish toxicity test as a new promising standard technique for the potential toxicity assessment of acid-adulterated honey in this study and hazardous food adulterants for future studies.
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Affiliation(s)
- Rafieh Fakhlaei
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013, Zhenjiang, Jiangsu, China; Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Jinap Selamat
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
| | - Ahmad Faizal Abdull Razis
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia; Natural Medicines and Products Research Laboratory, Universiti Putra Malaysia, Serdang, 43400, Selangor, Malaysia
| | - Rashidah Sukor
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia; Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Syahida Ahmad
- Department of Biochemistry, Faculty of Biotechnology & Biomolecular Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
| | - Alfi Khatib
- Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, 25200, Kuantan, Pahang, Malaysia
| | - Xiaobo Zou
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Rd, 212013, Zhenjiang, Jiangsu, China
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3
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Wevers D, Ramautar R, Clark C, Hankemeier T, Ali A. Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics. Electrophoresis 2023; 44:2000-2024. [PMID: 37667867 DOI: 10.1002/elps.202300105] [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] [Received: 05/11/2023] [Revised: 08/08/2023] [Accepted: 08/19/2023] [Indexed: 09/06/2023]
Abstract
Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.
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Affiliation(s)
- Dirk Wevers
- Wageningen University and Research, Wageningen, The Netherlands
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Rawi Ramautar
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Charlie Clark
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Thomas Hankemeier
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
| | - Ahmed Ali
- Metabolomics and Analytics Centre, Leiden Academic Centre for Drug Research, Leiden, The Netherlands
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4
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Lee IC, Tumanov S, Wong JW, Stocker R, Ho JW. Integrative processing of untargeted metabolomic and lipidomic data using MultiABLER. iScience 2023; 26:106881. [PMID: 37260745 PMCID: PMC10227420 DOI: 10.1016/j.isci.2023.106881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 03/13/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
Mass spectrometry (MS)-based untargeted metabolomic and lipidomic approaches are being used increasingly in biomedical research. The adoption and integration of these data are critical to the overall multi-omic toolkit. Recently, a sample extraction method called Multi-ABLE has been developed, which enables concurrent generation of proteomic and untargeted metabolomic and lipidomic data from a small amount of tissue. The proteomics field has a well-established set of software for processing of acquired data; however, there is a lack of a unified, off-the-shelf, ready-to-use bioinformatics pipeline that can take advantage of and prepare concurrently generated metabolomic and lipidomic data for joint downstream analyses. Here we present an R pipeline called MultiABLER as a unified and simple upstream processing and analysis pipeline for both metabolomics and lipidomics datasets acquired using liquid chromatography-tandem mass spectrometry. The code is available via an open-source license at https://github.com/holab-hku/MultiABLER.
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Affiliation(s)
- Ian C.H. Lee
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
| | - Sergey Tumanov
- Heart Research Institute, 7 Eliza Street, Newtown, NSW 2042, Australia
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
| | - Jason W.H. Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Centre for PanorOmic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Roland Stocker
- Heart Research Institute, 7 Eliza Street, Newtown, NSW 2042, Australia
- School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia
| | - Joshua W.K. Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D4H), Hong Kong Science Park, Hong Kong SAR, China
- Centre for PanorOmic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Su QZ, Vera P, Nerín C. Combination of Structure Databases, In Silico Fragmentation, and MS/MS Libraries for Untargeted Screening of Non-Volatile Migrants from Recycled High-Density Polyethylene Milk Bottles. Anal Chem 2023. [PMID: 37262310 DOI: 10.1021/acs.analchem.2c05389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Chemical contamination is one of the major obstacles for mechanical recycling of plastics. In this article, we built and open-sourced an in-house MS/MS library containing more than 500 plastic-related chemicals and developed mspcompiler, an R package, for the compilation of various libraries. We then proposed a workflow to process untargeted screening data acquired by liquid chromatography high-resolution mass spectrometry. These tools were subsequently employed to data originating from recycled high-density polyethylene (rHDPE) obtained from milk bottles. A total of 83 compounds were identified, with 66 easily annotated by making use of our in-house MS/MS libraries and the mspcompiler R package. In silico fragmentation combined with data obtained from gas chromatography-mass spectrometry and lists of chemicals related to plastics were used to identify those remaining unknown. A pseudo-multiple reaction monitoring method was also applied to sensitively target and screen the identified chemicals in the samples. Quantification results demonstrated that a good sorting of postconsumer materials and a better recycling technology may be necessary for food contact applications. Removal or reduction of non-volatile substances, such as octocrylene and 2-ethylhexyl-4-methoxycinnamate, is still challenging but vital for the safe use of rHDPE as food contact materials.
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Affiliation(s)
- Qi-Zhi Su
- Department of Analytical Chemistry, GUIA Group, I3A, EINA, University of Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
- National Reference Laboratory for Food Contact Material (Guangdong), Guangzhou Customs Technology Center, Guangzhou 510075, China
| | - Paula Vera
- Department of Analytical Chemistry, GUIA Group, I3A, EINA, University of Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
| | - Cristina Nerín
- Department of Analytical Chemistry, GUIA Group, I3A, EINA, University of Zaragoza, María de Luna 3, 50018 Zaragoza, Spain
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Du X, Dastmalchi F, Ye H, Garrett TJ, Diller MA, Liu M, Hogan WR, Brochhausen M, Lemas DJ. Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software. Metabolomics 2023; 19:11. [PMID: 36745241 DOI: 10.1007/s11306-023-01974-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/20/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.
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Affiliation(s)
- Xinsong Du
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Farhad Dastmalchi
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Hao Ye
- Health Science Center Libraries, University of Florida, Florida, USA
| | - Timothy J Garrett
- Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Florida, USA
| | - Matthew A Diller
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA
| | - Mathias Brochhausen
- Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, USA
| | - Dominick J Lemas
- Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.
- Department of Obstetrics and Gynecology, University of Florida College of Medicine, Florida, Gainesville, United States.
- Center for Perinatal Outcomes Research, University of Florida College of Medicine, Gainesville, United States.
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7
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Gendoo DMA. Overview of Bioinformatics Software and Databases for Metabolic Engineering. Methods Mol Biol 2023; 2553:265-274. [PMID: 36227548 DOI: 10.1007/978-1-0716-2617-7_13] [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
The explosion of the "omics" era has introduced a growing number of sets and tools that facilitate molecular interrogation of the metabolome. These include various bioinformatics and pharmacogenomics resources that can be utilized independently or collectively to facilitate metabolic engineering across disease, clinical oncology, and understanding of molecular changes across larger systems. This review provides starting points for accessing publicly available data and computational tools that support assessment of metabolic profiles and metabolic regulation, providing both a depth-and-breadth approach toward understanding the metabolome. We focus in particular on pathway databases and tools, which provide in-depth analysis of metabolic pathways, which is at the heart of metabolic engineering.
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Affiliation(s)
- Deena M A Gendoo
- Centre for Computational Biology, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom.
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8
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Llambrich M, Brezmes J, Cumeras R. The untargeted urine volatilome for biomedical applications: methodology and volatilome database. Biol Proced Online 2022; 24:20. [PMID: 36456991 PMCID: PMC9714113 DOI: 10.1186/s12575-022-00184-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published. Current achievements and limitations on each process step are discussed and compared. 34 studies were found retrieving all information from the urine treatment to the final results obtained. In this report, we provide the first specific urine volatilome database, consisting of 841 compounds from 80 different chemical classes.
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Affiliation(s)
- Maria Llambrich
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
| | - Jesús Brezmes
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
| | - Raquel Cumeras
- Department of Electrical Electronic Engineering and Automation, Universitat Rovira I Virgili, 43007 Tarragona, Spain
- Department of Nutrition and Metabolism, Metabolomics Interdisciplinary Group, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
- Oncology Department, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204, Reus, Spain
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9
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Bhosle A, Wang Y, Franzosa EA, Huttenhower C. Progress and opportunities in microbial community metabolomics. Curr Opin Microbiol 2022; 70:102195. [PMID: 36063685 DOI: 10.1016/j.mib.2022.102195] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 01/25/2023]
Abstract
The metabolome lies at the interface of host-microbiome crosstalk. Previous work has established links between chemically diverse microbial metabolites and a myriad of host physiological processes and diseases. Coupled with scalable and cost-effective technologies, metabolomics is thus gaining popularity as a tool for characterization of microbial communities, particularly when combined with metagenomics as a window into microbiome function. A systematic interrogation of microbial community metabolomes can uncover key microbial compounds, metabolic capabilities of the microbiome, and also provide critical mechanistic insights into microbiome-linked host phenotypes. In this review, we discuss methods and accompanying resources that have been developed for these purposes. The accomplishments of these methods demonstrate that metabolomes can be used to functionally characterize microbial communities, and that microbial properties can be used to identify and investigate chemical compounds.
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Affiliation(s)
- Amrisha Bhosle
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Ya Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Eric A Franzosa
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Curtis Huttenhower
- Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Harvard Chan Microbiome in Public Health Center, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
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10
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Quick tips for re-using metabolomics data. Nat Cell Biol 2022; 24:1560-1562. [PMID: 36280705 DOI: 10.1038/s41556-022-01019-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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11
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Han X, Liang L. metabolomicsR: a streamlined workflow to analyze metabolomic data in R. BIOINFORMATICS ADVANCES 2022; 2:vbac067. [PMID: 36177485 PMCID: PMC9512519 DOI: 10.1093/bioadv/vbac067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 01/27/2023]
Abstract
Summary metabolomicsR is a streamlined, flexible and user-friendly R package to preprocess, analyze and visualize metabolomic data. metabolomicsR includes comprehensive functionalities for sample and metabolite quality control, outlier detection, missing value imputation, dimensional reduction, batch effect normalization, data integration, regression, metabolite annotation and visualization of data and results. In this application note, we demonstrate the step-by-step use of the main functions from this package. Availability and implementation The metabolomicsR package is available via CRAN and GitHub (https://github.com/XikunHan/metabolomicsR/). A step-by-step online tutorial is available at https://xikunhan.github.io/metabolomicsR/docs/articles/Introduction.html. Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Xikun Han
- To whom correspondence should be addressed. or
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12
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Salzer L, Witting M, Schmitt-Kopplin P. MobilityTransformR: an R package for effective mobility transformation of CE-MS data. Bioinformatics 2022; 38:4044-4045. [PMID: 35781328 DOI: 10.1093/bioinformatics/btac441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/28/2022] [Accepted: 06/30/2022] [Indexed: 12/24/2022] Open
Abstract
SUMMARY We present MobilityTransformR, an R/Bioconductor package for the effective mobility scaling of capillary zone electrophoresis-mass spectrometry (CE-MS) data. It uses functionality from different R packages that are frequently used for data processing and analysis in MS-based metabolomics workflows, allowing the subsequent use of reproducible transformed CE-MS data in existing workflows. AVAILABILITY AND IMPLEMENTATION MobilityTransformR is implemented in R (Version >= 4.2) and can be downloaded directly from the Bioconductor database (https://bioconductor.org/packages/MobilityTransformR) or GitHub (https://github.com/LiesaSalzer/MobilityTransformR). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Liesa Salzer
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, D-85764 Neuherberg, Germany
| | - Michael Witting
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, D-85764 Neuherberg, Germany
| | - Philippe Schmitt-Kopplin
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, D-85764 Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany
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13
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Pang Z, Zhou G, Ewald J, Chang L, Hacariz O, Basu N, Xia J. Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat Protoc 2022; 17:1735-1761. [PMID: 35715522 DOI: 10.1038/s41596-022-00710-w] [Citation(s) in RCA: 527] [Impact Index Per Article: 263.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 05/18/2022] [Indexed: 01/01/2023]
Abstract
Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has become a workhorse in global metabolomics studies with growing applications across biomedical and environmental sciences. However, outstanding bioinformatics challenges in terms of data processing, statistical analysis and functional interpretation remain critical barriers to the wider adoption of this technology. To help the user community overcome these barriers, we have made major updates to the well-established MetaboAnalyst platform ( www.metaboanalyst.ca ). This protocol extends the previous 2011 Nature Protocol by providing stepwise instructions on how to use MetaboAnalyst 5.0 to: optimize parameters for LC-HRMS spectra processing; obtain functional insights from peak list data; integrate metabolomics data with transcriptomics data or combine multiple metabolomics datasets; conduct exploratory statistical analysis with complex metadata. Parameter optimization may take ~2 h to complete depending on the server load, and the remaining three stages may be executed in ~60 min.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jessica Ewald
- Department of Natural Resources Sciences, McGill University, Montreal, Quebec, Canada
| | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Orcun Hacariz
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Niladri Basu
- Department of Natural Resources Sciences, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
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14
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Hoffmann N, Mayer G, Has C, Kopczynski D, Al Machot F, Schwudke D, Ahrends R, Marcus K, Eisenacher M, Turewicz M. A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics. Metabolites 2022; 12:metabo12070584. [PMID: 35888710 PMCID: PMC9319858 DOI: 10.3390/metabo12070584] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/17/2022] [Accepted: 06/19/2022] [Indexed: 12/13/2022] Open
Abstract
Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography−mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.
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Affiliation(s)
- Nils Hoffmann
- Forschungszentrum Jülich GmbH, Institute for Bio- and Geosciences (IBG-5), 52425 Jülich, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
| | - Gerhard Mayer
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Canan Has
- Biological Mass Spectrometry, Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
- University Hospital Carl Gustav Carus, 01307 Dresden, Germany
- CENTOGENE GmbH, 18055 Rostock, Germany
| | - Dominik Kopczynski
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Fadi Al Machot
- Faculty of Science and Technology, Norwegian University for Life Science (NMBU), 1433 Ås, Norway;
| | - Dominik Schwudke
- Bioanalytical Chemistry, Forschungszentrum Borstel, Leibniz Lung Center, 23845 Borstel, Germany;
- Airway Research Center North, German Center for Lung Research (DZL), 23845 Borstel, Germany
- German Center for Infection Research (DZIF), TTU Tuberculosis, 23845 Borstel, Germany
| | - Robert Ahrends
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (D.K.); (R.A.)
| | - Katrin Marcus
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
| | - Martin Eisenacher
- Center for Protein Diagnostics (ProDi), Medical Proteome Analysis, Ruhr University Bochum, 44801 Bochum, Germany; (K.M.); (M.E.)
- Faculty of Medicine, Medizinisches Proteom-Center, Ruhr University Bochum, 44801 Bochum, Germany
| | - Michael Turewicz
- Institute for Clinical Biochemistry and Pathobiochemistry, German Diabetes Center (DDZ), Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, 40225 Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, 85764 Neuherberg, Germany
- Correspondence: (N.H.); (M.T.); Tel.: +49-(0)521-106-86780 (N.H.)
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15
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Rey-Stolle F, Dudzik D, Gonzalez-Riano C, Fernández-García M, Alonso-Herranz V, Rojo D, Barbas C, García A. Low and high resolution gas chromatography-mass spectrometry for untargeted metabolomics: A tutorial. Anal Chim Acta 2022; 1210:339043. [PMID: 35595356 DOI: 10.1016/j.aca.2021.339043] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/04/2021] [Accepted: 09/06/2021] [Indexed: 12/28/2022]
Abstract
GC-MS for untargeted metabolomics is a well-established technique. Small molecules and molecules made volatile by derivatization can be measured and those compounds are key players in main biological pathways. This tutorial provides ready-to-use protocols for GC-MS-based metabolomics, using either the well-known low-resolution approach (GC-Q-MS) with nominal mass or the more recent high-resolution approach (GC-QTOF-MS) with accurate mass, discussing their corresponding strengths and limitations. Analytical procedures are covered for different types of biofluids (plasma/serum, bronchoalveolar lavage, urine, amniotic fluid) tissue samples (brain/hippocampus, optic nerve, lung, kidney, liver, pancreas) and samples obtained from cell cultures (adipocytes, macrophages, Leishmania promastigotes, mitochondria, culture media). Together with the sample preparation and data acquisition, data processing strategies are described specially focused on Agilent equipments, including deconvolution software and database annotation using spectral libraries. Manual curation strategies and quality control are also deemed. Finally, considerations to obtain a semiquantitative value for the metabolites are also described. As a case study, an illustrative example from one of our experiments at CEMBIO Research Centre, is described and findings discussed.
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Affiliation(s)
- Fernanda Rey-Stolle
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Danuta Dudzik
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain; Department of Biopharmaceutics and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, Poland
| | - Carolina Gonzalez-Riano
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Miguel Fernández-García
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Vanesa Alonso-Herranz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - David Rojo
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain
| | - Antonia García
- Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, Universidad San Pablo CEU, CEU Universities. Campus Monteprincipe, Boadilla Del Monte, 28668, Madrid, Spain.
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16
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Moco S. Studying Metabolism by NMR-Based Metabolomics. Front Mol Biosci 2022; 9:882487. [PMID: 35573745 PMCID: PMC9094115 DOI: 10.3389/fmolb.2022.882487] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 03/24/2022] [Indexed: 12/12/2022] Open
Abstract
During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.
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17
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Galvão Ferrarini M, Ziska I, Andrade R, Julien-Laferrière A, Duchemin L, César RM, Mary A, Vinga S, Sagot MF. Totoro: Identifying Active Reactions During the Transient State for Metabolic Perturbations. Front Genet 2022; 13:815476. [PMID: 35281848 PMCID: PMC8905348 DOI: 10.3389/fgene.2022.815476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/16/2022] Open
Abstract
Motivation: The increasing availability of metabolomic data and their analysis are improving the understanding of cellular mechanisms and how biological systems respond to different perturbations. Currently, there is a need for novel computational methods that facilitate the analysis and integration of increasing volume of available data. Results: In this paper, we present Totoro a new constraint-based approach that integrates quantitative non-targeted metabolomic data of two different metabolic states into genome-wide metabolic models and predicts reactions that were most likely active during the transient state. We applied Totoro to real data of three different growth experiments (pulses of glucose, pyruvate, succinate) from Escherichia coli and we were able to predict known active pathways and gather new insights on the different metabolisms related to each substrate. We used both the E. coli core and the iJO1366 models to demonstrate that our approach is applicable to both smaller and larger networks. Availability:Totoro is an open source method (available at https://gitlab.inria.fr/erable/totoro) suitable for any organism with an available metabolic model. It is implemented in C++ and depends on IBM CPLEX which is freely available for academic purposes.
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Affiliation(s)
- Mariana Galvão Ferrarini
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Univ Lyon, INRAE, INSA-Lyon, BF2I, UMR 203, Villeurbanne, France
| | - Irene Ziska
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Ricardo Andrade
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,Institute of Mathematics and Statistics (IME), University of São Paulo, São Paulo, Brazil
| | | | - Louis Duchemin
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France
| | | | - Arnaud Mary
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
| | - Susana Vinga
- INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Marie-France Sagot
- Laboratoire de Biométrie et Biologie Évolutive, UMR 5558, CNRS, Université de Lyon, Université Lyon 1, Villeurbanne, France.,INRIA Grenoble Rhône-Alpes, Villeurbanne, France
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18
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Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol 2022; 20:143-160. [PMID: 34552265 PMCID: PMC9578303 DOI: 10.1038/s41579-021-00621-9] [Citation(s) in RCA: 143] [Impact Index Per Article: 71.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 02/08/2023]
Abstract
Microbiotas are a malleable part of ecosystems, including the human ecosystem. Microorganisms affect not only the chemistry of their specific niche, such as the human gut, but also the chemistry of distant environments, such as other parts of the body. Mass spectrometry-based metabolomics is one of the key technologies to detect and identify the small molecules produced by the human microbiota, and to understand the functional role of these microbial metabolites. This Review provides a foundational introduction to common forms of untargeted mass spectrometry and the types of data that can be obtained in the context of microbiome analysis. Data analysis remains an obstacle; therefore, the emphasis is placed on data analysis approaches and integrative analysis, including the integration of microbiome sequencing data.
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Affiliation(s)
- Anelize Bauermeister
- Institute of Biomedical Science, Universidade de São Paulo, São Paulo, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Helena Mannochio-Russo
- Department of Biochemistry and Organic Chemistry, Institute of Chemistry, São Paulo State University, Araraquara, SP, Brazil,Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | | | - Alan K. Jarmusch
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA
| | - Pieter C. Dorrestein
- Collaborative Mass Spectrometry Innovation Center, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, CA, USA.,Department of Pediatrics, University of California, San Diego, CA, USA.,Center for Microbiome Innovation, University of California, San Diego, CA, USA
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19
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Rainer J, Vicini A, Salzer L, Stanstrup J, Badia JM, Neumann S, Stravs MA, Verri Hernandes V, Gatto L, Gibb S, Witting M. A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites 2022; 12:metabo12020173. [PMID: 35208247 PMCID: PMC8878271 DOI: 10.3390/metabo12020173] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/01/2022] [Accepted: 02/04/2022] [Indexed: 01/27/2023] Open
Abstract
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics experiments have become increasingly popular because of the wide range of metabolites that can be analyzed and the possibility to measure novel compounds. LC-MS instrumentation and analysis conditions can differ substantially among laboratories and experiments, thus resulting in non-standardized datasets demanding customized annotation workflows. We present an ecosystem of R packages, centered around the MetaboCoreUtils, MetaboAnnotation and CompoundDb packages that together provide a modular infrastructure for the annotation of untargeted metabolomics data. Initial annotation can be performed based on MS1 properties such as m/z and retention times, followed by an MS2-based annotation in which experimental fragment spectra are compared against a reference library. Such reference databases can be created and managed with the CompoundDb package. The ecosystem supports data from a variety of formats, including, but not limited to, MSP, MGF, mzML, mzXML, netCDF as well as MassBank text files and SQL databases. Through its highly customizable functionality, the presented infrastructure allows to build reproducible annotation workflows tailored for and adapted to most untargeted LC-MS-based datasets. All core functionality, which supports base R data types, is exported, also facilitating its re-use in other R packages. Finally, all packages are thoroughly unit-tested and documented and are available on GitHub and through Bioconductor.
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Affiliation(s)
- Johannes Rainer
- Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, 39100 Bozen, Italy; (A.V.); (V.V.H.)
- Correspondence:
| | - Andrea Vicini
- Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, 39100 Bozen, Italy; (A.V.); (V.V.H.)
| | - Liesa Salzer
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, 85764 Neuherberg, Germany;
| | - Jan Stanstrup
- Department of Nutrition, Exercise and Sports, University of Copenhagen, 1985 Frederiksberg, Denmark;
| | - Josep M. Badia
- Department of Electronic Engineering & IISPV, Universitat Rovira i Virgili, 43007 Tarragona, Spain;
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Steffen Neumann
- Leibniz Institute of Plant Biochemistry, Bioinformatics and Scientific Data, 06120 Halle, Germany;
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, 04103 Leipzig, Germany
| | - Michael A. Stravs
- Department of Environmental Chemistry, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland;
- Institute of Molecular Systems Biology, ETH Zürich, 8093 Zürich, Switzerland
| | - Vinicius Verri Hernandes
- Institute for Biomedicine (Affiliated to the University of Lübeck), Eurac Research, 39100 Bozen, Italy; (A.V.); (V.V.H.)
- School of Medicine and Surgery, Università degli Studi di Milano-Bicocca, 20854 Vedano al Lambro, Italy
| | - Laurent Gatto
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, 1200 Brussels, Belgium;
| | - Sebastian Gibb
- Department of Anesthesiology and Intensive Care, University Medicine Greifswald, 17475 Greifswald, Germany;
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, 85764 Neuherberg, Germany;
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, 85354 Freising-Weihenstephan, Germany
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20
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Comparison of chemometric strategies for potential exposure marker discovery and false-positive reduction in untargeted metabolomics: application to the serum analysis by LC-HRMS after intake of Vaccinium fruit supplements. Anal Bioanal Chem 2022; 414:1841-1855. [PMID: 35028688 DOI: 10.1007/s00216-021-03815-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 11/18/2021] [Accepted: 11/30/2021] [Indexed: 11/01/2022]
Abstract
Untargeted liquid chromatographic-high-resolution mass spectrometric (LC-HRMS) metabolomics for potential exposure marker (PEM) discovery in nutrikinetic studies generates complex outputs. The correct selection of statistically significant PEMs is a crucial analytical step for understanding nutrition-health interactions. Hence, in this paper, different chemometric selection workflows for PEM discovery, using multivariate or univariate parametric or non-parametric data analyses, were comparatively tested and evaluated. The PEM selection protocols were applied to a small-sample-size untargeted LC-HRMS study of a longitudinal set of serum samples from 20 volunteers after a single intake of (poly)phenolic-rich Vaccinium myrtillus and Vaccinium corymbosum supplements. The non-parametric Games-Howell test identified a restricted group of significant features, thus minimizing the risk of false-positive retention. Among the forty-seven PEMs exhibiting a statistically significant postprandial kinetics, twelve were successfully annotated as purine pathway metabolites, benzoic and benzodiol metabolites, indole alkaloids, and organic and fatty acids, and five (i.e. octahydro-methyl-β-carboline-dicarboxylic acid, tetrahydro-methyl-β-carboline-dicarboxylic acid, citric acid, caprylic acid, and azelaic acid) were associated to Vaccinium berry consumption for the first time. The analysis of the area under the curve of the longitudinal dataset highlighted thirteen statistically significant PEMs discriminating the two interventions, including four intra-intervention relevant metabolites (i.e. abscisic acid glucuronide, catechol sulphate, methyl-catechol sulphate, and α-hydroxy-hippuric acid). Principal component analysis and sample classification through linear discriminant analysis performed on PEM maximum intensity confirmed the discriminating role of these PEMs.
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21
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Geng F, Nie R, Yang N, Cai L, Hu Y, Chen S, Cheng X, Wang Z, Chen L. Integrated transcriptome and metabolome profiling of Camellia reticulata reveal mechanisms of flower color differentiation. Front Genet 2022; 13:1059717. [PMID: 36482888 PMCID: PMC9725097 DOI: 10.3389/fgene.2022.1059717] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 10/31/2022] [Indexed: 03/19/2023] Open
Abstract
Camellia reticulata (Lindl.) is an important ornamental plant in China. Long-term natural or artificial selections have resulted in diverse phenotypes, especially for flower colors. Modulating flower colors can enhance the visual appeal and economic value in ornamental plants. In this study, we investigated the molecular mechanisms underlying flower color differentiation in C. reticulata. We performed a combined transcriptome and metabolome analysis of the petals of a popular variety C. reticulata (HHYC) (red), and its two cultivars "Xuejiao" (XJ) (pink) and "Tongzimian" (TZM) (white). Targeted metabolome profiling identified 310 flavonoid compounds of which 18 anthocyanins were differentially accumulated among the three samples with an accumulation pattern of HHYC > XJ > TZM. Likewise, transcriptome analysis showed that carotenoid and anthocyanin biosynthetic structural genes were mostly expressed in order of HHYC > XJ > TZM. Two genes (gene-LOC114287745765 and gene-LOC114289234) encoding for anthocyanidin 3-O-glucosyltransferase are predicted to be responsible for red coloration in HHYC and XJ. We also detected 42 MYB and 29 bHLH transcription factors as key regulators of anthocyanin-structural genes. Overall, this work showed that flavonoids, particularly anthocyanins contents are the major determinants of flower color differentiation among the 3 C. reticulata samples. In addition, the main regulatory and structural genes modulating anthocyanin contents in C. reticulata have been unveiled. Our results will help in the development of Camellia varieties with specific flower color and quality.
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Affiliation(s)
- Fang Geng
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Ruimin Nie
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Nan Yang
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Lei Cai
- Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China
| | - YunChong Hu
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Shengtong Chen
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Xiaomao Cheng
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
| | - Zhonglang Wang
- Kunming Institute of Botany, Chinese Academy of Science, Kunming, Yunnan, China
| | - Longqing Chen
- College of Landscape Architecture and Horticulture Sciences, Southwest Landscape Architecture Engineering Technology Research Center of National Forestry and Grassland Administration, Yunnan Functional Flower Resources and Industrialization Technology Engineering Research Center, Southwest Forestry University, Kunming, Yunnan, China
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22
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Chetnik K, Benedetti E, Gomari DP, Schweickart A, Batra R, Buyukozkan M, Wang Z, Arnold M, Zierer J, Suhre K, Krumsiek J. maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines. Bioinformatics 2021; 38:1168-1170. [PMID: 34694386 PMCID: PMC8796365 DOI: 10.1093/bioinformatics/btab741] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/24/2021] [Accepted: 10/22/2021] [Indexed: 02/03/2023] Open
Abstract
This article presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results and visualizations. maplet's key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. AVAILABILITY AND IMPLEMENTATION The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.
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Affiliation(s)
- Kelsey Chetnik
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Elisa Benedetti
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Daniel P Gomari
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | - Annalise Schweickart
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Richa Batra
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Mustafa Buyukozkan
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Zeyu Wang
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medical College—Qatar Education City, Doha, Qatar
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23
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Fakhlaei R, Selamat J, Razis AFA, Sukor R, Ahmad S, Amani Babadi A, Khatib A. In Vivo Toxicity Evaluation of Sugar Adulterated Heterotrigona itama Honey Using Zebrafish Model. Molecules 2021; 26:molecules26206222. [PMID: 34684803 PMCID: PMC8538600 DOI: 10.3390/molecules26206222] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 12/21/2022] Open
Abstract
Honey is prone to be adulterated through mixing with sugars, cheap and low-quality honey, and other adulterants. Consumption of adulterated honey may cause several health issues such as weight gain, diabetes, and liver and kidney dysfunction. Therefore, studying the impact of consumption of adulterated honey on consumers is critical since there is a lack of study in this field. Hence, the aims of this paper were: (1) to determine the lethal concentration (LC50) of adulterated honey using zebrafish embryo, (2) to elucidate toxicology of selected adulterated honey based on lethal dose (LD50) using adult zebrafish, (3) to determine the effects of adulterated honey on histological changes of zebrafish, and (4) to screen the metabolites profile of adulterated honey by using zebrafish blood serum. The LC50 of Heterotrigona itama honey (acacia honey) and its sugar adulterants (light corn sugar, cane sugar, inverted sugar, and palm sugar in the proportion of 1-3% (w/w) from the total volume) was determined by the toxicological assessment of honey samples on zebrafish embryos (different exposure concentrations in 24, 48, 72, and 96 h postfertilization (hpf)). Pure H. itama honey represents the LC50 of 34.40 ± 1.84 (mg/mL) at 96 hpf, while the inverted sugar represents the lowest LC50 (5.03 ± 0.92 mg/mL) among sugar adulterants. The highest concentration (3%) of sugar adulterants were used to study the toxicology of adulterated honey using adult zebrafish in terms of acute, prolong-acute, and sub-acute tests. The results of the LD50 from the sub-acute toxicity test of pure H. itama honey was 2.33 ± 0.24 (mg/mL). The histological studies of internal organs showed a lesion in the liver, kidney, and spleen of adulterated treated-honey groups compared to the control group. Furthermore, the LC-MS/MS results revealed three endogenous metabolites in both the pure and adulterated honey treated groups, as follows: (1) S-Cysteinosuccinic acid, (2) 2,3-Diphosphoglyceric acid, and (3) Cysteinyl-Tyrosine. The results of this study demonstrated that adulterated honey caused mortality, which contributes to higher toxicity, and also suggested that the zebrafish toxicity test could be a standard method for assessing the potential toxicity of other hazardous food additives. The information gained from this research will permit an evaluation of the potential risk associated with the consumption of adulterated compared to pure honey.
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Affiliation(s)
- Rafieh Fakhlaei
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (R.F.); (R.S.)
| | - Jinap Selamat
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (R.F.); (R.S.)
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
- Correspondence: ; Tel.: +60-38-9769-1099
| | - Ahmad Faizal Abdull Razis
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
| | - Rashidah Sukor
- Food Safety and Food Integrity (FOSFI), Institute of Tropical Agriculture and Food Security, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (R.F.); (R.S.)
- Department of Food Science, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Syahida Ahmad
- Department of Biochemistry, Faculty of Biotechnology & Biomolecular Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Arman Amani Babadi
- Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran 55469-14177, Iran;
| | - Alfi Khatib
- Department of Pharmaceutical Chemistry, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan 25200, Pahang, Malaysia;
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Escriba-Montagut X, Basagaña X, Vrijheid M, Gonzalez JR. Software Application Profile: exposomeShiny—a toolbox for exposome data analysis. Int J Epidemiol 2021. [PMCID: PMC8855999 DOI: 10.1093/ije/dyab220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Motivation Studying the role of the exposome in human health and its impact on different omic layers requires advanced statistical methods. Many of these methods are implemented in different R and Bioconductor packages, but their use may require strong expertise in R, in writing pipelines and in using new R classes which may not be familiar to non-advanced users. ExposomeShiny provides a bridge between researchers and most of the state-of-the-art exposome analysis methodologies, without the need of advanced programming skills. Implementation ExposomeShiny is a standalone web application implemented in R. It is available as source files and can be installed in any server or computer avoiding problems with data confidentiality. It is executed in RStudio which opens a browser window with the web application. General features The presented implementation allows the conduct of: (i) data pre-processing: normalization and missing imputation (including limit of detection); (ii) descriptive analysis; (iii) exposome principal component analysis (PCA) and hierarchical clustering; (iv) exposome-wide association studies (ExWAS) and variable selection ExWAS; (v) omic data integration by single association and multi-omic analyses; and (vi) post-exposome data analyses to gain biological insight for the exposures, genes or using the Comparative Toxicogenomics Database (CTD) and pathway analysis. Availability The exposomeShiny source code is freely available on Github at [https://github.com/isglobal-brge/exposomeShiny], Git tag v1.4. The software is also available as a Docker image [https://hub.docker.com/r/brgelab/exposome-shiny], tag v1.4. A user guide with information about the analysis methodologies as well as information on how to use exposomeShiny is freely hosted at [https://isglobal-brge.github.io/exposome_bookdown/].
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Affiliation(s)
| | - Xavier Basagaña
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Martine Vrijheid
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
| | - Juan R Gonzalez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
- Department of Mathematics, Universitat Autònoma de Barcelona (UAB), Bellaterra (Barcelona), Spain
- Corresponding author. Barcelona Biomedical Research Park (PRBB), Doctor Aiguader, 88. 08003 Barcelona, Spain. E-mail:
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25
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MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools. Metabolites 2021; 11:metabo11100678. [PMID: 34677393 PMCID: PMC8540572 DOI: 10.3390/metabo11100678] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/18/2021] [Accepted: 09/22/2021] [Indexed: 01/06/2023] Open
Abstract
The bottleneck for taking full advantage of metabolomics data is often the availability, awareness, and usability of analysis tools. Software tools specifically designed for metabolomics data are being developed at an increasing rate, with hundreds of available tools already in the literature. Many of these tools are open-source and freely available but are very diverse with respect to language, data formats, and stages in the metabolomics pipeline. To help mitigate the challenges of meeting the increasing demand for guidance in choosing analytical tools and coordinating the adoption of best practices for reproducibility, we have designed and built the MSCAT (Metabolomics Software CATalog) database of metabolomics software tools that can be sustainably and continuously updated. This database provides a survey of the landscape of available tools and can assist researchers in their selection of data analysis workflows for metabolomics studies according to their specific needs. We used machine learning (ML) methodology for the purpose of semi-automating the identification of metabolomics software tool names within abstracts. MSCAT searches the literature to find new software tools by implementing a Named Entity Recognition (NER) model based on a neural network model at the sentence level composed of a character-level convolutional neural network (CNN) combined with a bidirectional long-short-term memory (LSTM) layer and a conditional random fields (CRF) layer. The list of potential new tools (and their associated publication) is then forwarded to the database maintainer for the curation of the database entry corresponding to the tool. The end-user interface allows for filtering of tools by multiple characteristics as well as plotting of the aggregate tool data to monitor the metabolomics software landscape.
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Viallon V, His M, Rinaldi S, Breeur M, Gicquiau A, Hemon B, Overvad K, Tjønneland A, Rostgaard-Hansen AL, Rothwell JA, Lecuyer L, Severi G, Kaaks R, Johnson T, Schulze MB, Palli D, Agnoli C, Panico S, Tumino R, Ricceri F, Verschuren WMM, Engelfriet P, Onland-Moret C, Vermeulen R, Nøst TH, Urbarova I, Zamora-Ros R, Rodriguez-Barranco M, Amiano P, Huerta JM, Ardanaz E, Melander O, Ottoson F, Vidman L, Rentoft M, Schmidt JA, Travis RC, Weiderpass E, Johansson M, Dossus L, Jenab M, Gunter MJ, Lorenzo Bermejo J, Scherer D, Salek RM, Keski-Rahkonen P, Ferrari P. A New Pipeline for the Normalization and Pooling of Metabolomics Data. Metabolites 2021; 11:631. [PMID: 34564446 PMCID: PMC8467830 DOI: 10.3390/metabo11090631] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/10/2021] [Accepted: 09/13/2021] [Indexed: 01/10/2023] Open
Abstract
Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples' originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.
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Affiliation(s)
- Vivian Viallon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mathilde His
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Sabina Rinaldi
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marie Breeur
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Audrey Gicquiau
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Bertrand Hemon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Kim Overvad
- Department of Public Health, Aarhus University Bartholins Alle 2, DK-8000 Aarhus, Denmark;
| | - Anne Tjønneland
- Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark; (A.T.); (A.L.R.-H.)
| | | | - Joseph A. Rothwell
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Lucie Lecuyer
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
| | - Gianluca Severi
- UVSQ, Inserm, CESP U1018, “Exposome and Heredity” Team, Université Paris-Saclay, Gustave Roussy, 94800 Villejuif, France; (J.A.R.); (L.L.); (G.S.)
- Department of Statistics, Computer Science, Applications “G. Parenti”, University of Florence, 50134 Florence, Italy
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Theron Johnson
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (R.K.); (T.J.)
| | - Matthias B. Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany;
- Institute of Nutritional Science, University of Potsdam, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany
| | - Domenico Palli
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), 50139 Florence, Italy;
| | - Claudia Agnoli
- Epidemiology and Prevention Unit Department of Research, Fondazione IRCCS—Istituto Nazionale dei Tumori, 20133 Milan, Italy;
| | - Salvatore Panico
- Dipartimento di Medicina Clinica e Chirurgia, Federico II University, 80131 Naples, Italy;
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Provincial Health Authority (ASP 7), 97100 Ragusa, Italy;
| | - Fulvio Ricceri
- Department of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, Italy;
- Unit of Epidemiology, Regional Health Service ASL TO3, 10095 Grugliasco, Italy
| | - W. M. Monique Verschuren
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Peter Engelfriet
- National Institute for Public Health and the Environment, Centre for Nutrition, Prevention and Health Services, Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, The Netherlands; (W.M.M.V.); (P.E.)
| | - Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
| | - Roel Vermeulen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands; (C.O.-M.); (R.V.)
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, 3584 CM Utrecht, The Netherlands
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Ilona Urbarova
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, 9037 Tromsø, Norway; (T.H.N.); (I.U.)
| | - Raul Zamora-Ros
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L’Hospitalet de Llobregat, Spain;
| | - Miguel Rodriguez-Barranco
- Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain;
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
| | - Pilar Amiano
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, 20013 San Sebastián, Spain
- Biodonostia Health Research Institute, Group of Epidemiology of Chronic and Communicable Diseases, 20014 San Sebastián, Spain
| | - José Maria Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, 30007 Murcia, Spain
| | - Eva Ardanaz
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (P.A.); (J.M.H.); (E.A.)
- Navarra Public Health Institute, 31003 Pamplona, Spain
- IdiSNA, Navarra Institute for Health Research, 31008 Pamplona, Spain
| | - Olle Melander
- Department of Clincal Sciences, Lund University, SE-21 428 Malmö, Sweden;
- Department of Emergency and Internal Medicine, Skåne University Hospital, SE-20 502 Malmö, Sweden
| | - Filip Ottoson
- Department of Immunotechnology, Lund University, SE-22 100 Lund, Sweden;
| | - Linda Vidman
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Matilda Rentoft
- Department of Radiation Sciences, Oncology, Umeå University, SE-901 87 Umeå, Sweden; (L.V.); (M.R.)
| | - Julie A. Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Ruth C. Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (J.A.S.); (R.C.T.)
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, 69008 Lyon, France;
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France;
| | - Laure Dossus
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Mazda Jenab
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Marc J. Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Justo Lorenzo Bermejo
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Dominique Scherer
- Statistical Genetics Group, Institute of Medical Biometry, University of Heidelberg, 69120 Heidelberg, Germany; (J.L.B.); (D.S.)
| | - Reza M. Salek
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pekka Keski-Rahkonen
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
| | - Pietro Ferrari
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC-WHO), 69008 Lyon, France; (M.H.); (S.R.); (M.B.); (A.G.); (B.H.); (L.D.); (M.J.); (M.J.G.); (R.M.S.); (P.K.-R.); (P.F.)
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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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Nicolotti L, Hack J, Herderich M, Lloyd N. MStractor: R Workflow Package for Enhancing Metabolomics Data Pre-Processing and Visualization. Metabolites 2021; 11:metabo11080492. [PMID: 34436433 PMCID: PMC8398219 DOI: 10.3390/metabo11080492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022] Open
Abstract
Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.
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Affiliation(s)
- Luca Nicolotti
- The Australian Wine Research Institute, Adelaide, SA 5064, Australia; (J.H.); (M.H.); (N.L.)
- Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia
- Correspondence:
| | - Jeremy Hack
- The Australian Wine Research Institute, Adelaide, SA 5064, Australia; (J.H.); (M.H.); (N.L.)
- Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia
| | - Markus Herderich
- The Australian Wine Research Institute, Adelaide, SA 5064, Australia; (J.H.); (M.H.); (N.L.)
- Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia
| | - Natoiya Lloyd
- The Australian Wine Research Institute, Adelaide, SA 5064, Australia; (J.H.); (M.H.); (N.L.)
- Metabolomics Australia, The Australian Wine Research Institute, Adelaide, SA 5064, Australia
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29
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Pang Z, Chong J, Zhou G, de Lima Morais DA, Chang L, Barrette M, Gauthier C, Jacques PÉ, Li S, Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res 2021; 49:W388-W396. [PMID: 34019663 PMCID: PMC8265181 DOI: 10.1093/nar/gkab382] [Citation(s) in RCA: 1998] [Impact Index Per Article: 666.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/17/2021] [Accepted: 04/27/2021] [Indexed: 12/31/2022] Open
Abstract
Since its first release over a decade ago, the MetaboAnalyst web-based platform has become widely used for comprehensive metabolomics data analysis and interpretation. Here we introduce MetaboAnalyst version 5.0, aiming to narrow the gap from raw data to functional insights for global metabolomics based on high-resolution mass spectrometry (HRMS). Three modules have been developed to help achieve this goal, including: (i) a LC-MS Spectra Processing module which offers an easy-to-use pipeline that can perform automated parameter optimization and resumable analysis to significantly lower the barriers to LC-MS1 spectra processing; (ii) a Functional Analysis module which expands the previous MS Peaks to Pathways module to allow users to intuitively select any peak groups of interest and evaluate their enrichment of potential functions as defined by metabolic pathways and metabolite sets; (iii) a Functional Meta-Analysis module to combine multiple global metabolomics datasets obtained under complementary conditions or from similar studies to arrive at comprehensive functional insights. There are many other new functions including weighted joint-pathway analysis, data-driven network analysis, batch effect correction, merging technical replicates, improved compound name matching, etc. The web interface, graphics and underlying codebase have also been refactored to improve performance and user experience. At the end of an analysis session, users can now easily switch to other compatible modules for a more streamlined data analysis. MetaboAnalyst 5.0 is freely available at https://www.metaboanalyst.ca.
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Affiliation(s)
- Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jasmine Chong
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Guangyan Zhou
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | | | - Le Chang
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Michel Barrette
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Carol Gauthier
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Pierre-Étienne Jacques
- Centre de Calcul Scientifique, Université de Sherbrooke, Sherbrooke, Quebec, Canada
- Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Department of Animal Science, McGill University, Montreal, Quebec, Canada
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30
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Castellano-Escuder P, González-Domínguez R, Carmona-Pontaque F, Andrés-Lacueva C, Sánchez-Pla A. POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis. PLoS Comput Biol 2021; 17:e1009148. [PMID: 34197462 PMCID: PMC8279420 DOI: 10.1371/journal.pcbi.1009148] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 07/14/2021] [Accepted: 06/05/2021] [Indexed: 12/14/2022] Open
Abstract
Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively. Metabolomics and proteomics are two growing areas in human health and personalized medicine fields. Often, one of the main applications of metabolomics and proteomics is the discovery of novel biomarkers and new therapeutic targets in these areas. However, these data are extremely complex and hard to analyse, since they have a large number of features, several missing values, and often important clinical variables to consider in the analyses. Therefore, powerful and versatile tools are needed to provide efficient methods for data visualization and exploration, as well as a wide range of robust statistical methods to meet all data and users requirements. Although powerful tools do exist for the analysis of these data, many of them are still limiting the analyses in terms of visualization and statistical analysis. To address this limitation and complement the existing tools, we have developed a web-based application, named POMAShiny, for the data analysis of metabolomics and proteomics. This novel and versatile tool offers a wholly interactive and easy-to-use environment for the analysis of these data, including numerous methods for preprocessing, data visualization and statistical analysis. The POMAShiny open-source tool is extremely flexible and portable, as it can be installed locally and freely accessed online at https://webapps.nutrimetabolomics.com/POMAShiny.
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Affiliation(s)
- Pol Castellano-Escuder
- Biomarkers and Nutritional & Food Metabolomics Research Group, Department of Nutrition, Food Science and Gastronomy, Food Innovation Network (XIA), University of Barcelona, Barcelona, Spain
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
- * E-mail: (PC-E); (AS-P)
| | - Raúl González-Domínguez
- Biomarkers and Nutritional & Food Metabolomics Research Group, Department of Nutrition, Food Science and Gastronomy, Food Innovation Network (XIA), University of Barcelona, Barcelona, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
| | - Francesc Carmona-Pontaque
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
| | - Cristina Andrés-Lacueva
- Biomarkers and Nutritional & Food Metabolomics Research Group, Department of Nutrition, Food Science and Gastronomy, Food Innovation Network (XIA), University of Barcelona, Barcelona, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
| | - Alex Sánchez-Pla
- Statistics and Bioinformatics Research Group, Department of Genetics, Microbiology and Statistics, University of Barcelona, Barcelona, Spain
- CIBERFES, Instituto de Salud Carlos III, Madrid, Spain
- * E-mail: (PC-E); (AS-P)
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31
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Plyushchenko IV, Fedorova ES, Potoldykova NV, Polyakovskiy KA, Glukhov AI, Rodin IA. Omics Untargeted Key Script: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study. J Proteome Res 2021; 21:833-847. [PMID: 34161108 DOI: 10.1021/acs.jproteome.1c00392] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Large-scale untargeted LC-MS-based metabolomic profiling is a valuable source for systems biology and biomarker discovery. Data analysis and processing are major tasks due to the high complexity of generated signals and the presence of unwanted variations. In the present study, we introduce an R-based open-source collection of scripts called OUKS (Omics Untargeted Key Script), which provides comprehensive data processing. OUKS is developed by integrating various R packages and metabolomics software tools and can be easily set up and prepared to create a custom pipeline. Novel computational features are related to quality control samples-based signal processing and are implemented by gradient boosting, tree-based, and other nonlinear regression algorithms. Bladder cancer biomarkers discovery study which is based on untargeted LC-MS profiling of urine samples is performed to demonstrate exhaustive functionality of the developed software tool. Unique examination among dozens of metabolomics-specific data curation methods was carried out at each processing step. As a result, potential biomarkers were identified, statistically validated, and described by metabolism disorders. Our study demonstrates that OUKS helps to make untargeted LC-MS metabolomic profiling with the latest computational features readily accessible in a ready-to-use unified manner to a research community.
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Affiliation(s)
- Ivan V Plyushchenko
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Elizaveta S Fedorova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 119071 Moscow, Russia
| | - Natalia V Potoldykova
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Konstantin A Polyakovskiy
- Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University, 119992 Moscow, Russia
| | - Alexander I Glukhov
- Biology Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Biochemistry, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Igor A Rodin
- Chemistry Department, Lomonosov Moscow State University, 119991 Moscow, Russia.,Department of Epidemiology and Evidence-Based Medicine, Sechenov First Moscow State Medical University, 119435 Moscow, Russia
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32
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Laursen MF, Pekmez CT, Larsson MW, Lind MV, Yonemitsu C, Larnkjær A, Mølgaard C, Bode L, Dragsted LO, Michaelsen KF, Licht TR, Bahl MI. Maternal milk microbiota and oligosaccharides contribute to the infant gut microbiota assembly. ISME COMMUNICATIONS 2021; 1:21. [PMID: 36737495 PMCID: PMC9723702 DOI: 10.1038/s43705-021-00021-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/10/2021] [Accepted: 05/20/2021] [Indexed: 02/07/2023]
Abstract
Breastfeeding protects against diseases, with potential mechanisms driving this being human milk oligosaccharides (HMOs) and the seeding of milk-associated bacteria in the infant gut. In a cohort of 34 mother-infant dyads we analyzed the microbiota and HMO profiles in breast milk samples and infant's feces. The microbiota in foremilk and hindmilk samples of breast milk was compositionally similar, however hindmilk had higher bacterial load and absolute abundance of oral-associated bacteria, but a lower absolute abundance of skin-associated Staphylococcus spp. The microbial communities within both milk and infant's feces changed significantly over the lactation period. On average 33% and 23% of the bacterial taxa detected in infant's feces were shared with the corresponding mother's milk at 5 and 9 months of age, respectively, with Streptococcus, Veillonella and Bifidobacterium spp. among the most frequently shared. The predominant HMOs in feces associated with the infant's fecal microbiota, and the dominating infant species B. longum ssp. infantis and B. bifidum correlated inversely with HMOs. Our results show that breast milk microbiota changes over time and within a feeding session, likely due to transfer of infant oral bacteria during breastfeeding and suggest that milk-associated bacteria and HMOs direct the assembly of the infant gut microbiota.
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Affiliation(s)
| | - Ceyda T Pekmez
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Melanie Wange Larsson
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
- Department of Nursing and Nutrition, University College Copenhagen, Copenhagen, Denmark
| | - Mads Vendelbo Lind
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Chloe Yonemitsu
- Department of Pediatrics and Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence, University of California San Diego, La Jolla, CA, USA
| | - Anni Larnkjær
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Christian Mølgaard
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Lars Bode
- Department of Pediatrics and Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence, University of California San Diego, La Jolla, CA, USA
| | - Lars Ove Dragsted
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Kim F Michaelsen
- Department of Nutrition, Exercise, and Sports, University of Copenhagen, Frederiksberg, Denmark
| | - Tine Rask Licht
- National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Martin Iain Bahl
- National Food Institute, Technical University of Denmark, Kongens Lyngby, Denmark.
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33
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Misra BB. Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2021; 13:2265-2282. [PMID: 33987631 DOI: 10.1039/d1ay00173f] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Gas chromatography-mass spectrometry (GC-MS) provides a complementary analytical platform for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes. High resolution (HR) GC-MS as a data generation platform can capture data on analytes that are usually not detectable/quantifiable in liquid chromatography mass-spectrometry-based solutions. With the rise of high-resolution accurate mass (HRAM) GC-MS systems such as GC-Orbitrap-MS in the last decade after the time-of-flight (ToF) renaissance, numerous applications have been found in the fields of metabolomics and exposomics. In a short span of time, a multitude of studies have used GC-Orbitrap-MS to generate exciting new high throughput data spanning from diverse basic to applied research areas. The GC-Orbitrap-MS has found application in both targeted and untargeted efforts for capturing metabolomes and exposomes across diverse studies. In this review, I capture and summarize all the reported studies to date, and provide a snapshot of the milieu of commercial and open-source software solutions, spectral libraries, and informatics solutions available to a GC-Orbitrap-MS system instrument user or a data analyst dealing with these datasets. Lastly, but importantly, I provide an account on data sharing and meta-data capturing solutions that are available to make HRAM GC-MS based metabolomics and exposomics studies findable, accessible, interoperable, and reproducible (FAIR). These FAIR practices would allow data generators and users of GC-HRMS instruments to help the community of GC-MS researchers to collaborate and co-develop exciting tools and algorithms in the future.
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Affiliation(s)
- Biswapriya B Misra
- Independent Researcher, Pine-211, Raintree Park Dwaraka Krishna, Namburu, AP-522508, India.
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34
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Pettini F, Visibelli A, Cicaloni V, Iovinelli D, Spiga O. Multi-Omics Model Applied to Cancer Genetics. Int J Mol Sci 2021; 22:ijms22115751. [PMID: 34072237 PMCID: PMC8199287 DOI: 10.3390/ijms22115751] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 05/18/2021] [Accepted: 05/26/2021] [Indexed: 12/29/2022] Open
Abstract
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
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Affiliation(s)
- Francesco Pettini
- Department of Medical Biotechnology, University of Siena, Via M. Bracci 2, 53100 Siena, Italy
- Correspondence: ; Tel.: +39-3755461426
| | - Anna Visibelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Vittoria Cicaloni
- Toscana Life Sciences Foundation, Via Fiorentina 1, 53100 Siena, Italy;
| | - Daniele Iovinelli
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via A. Moro 2, 53100 Siena, Italy; (A.V.); (D.I.); (O.S.)
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35
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Price EJ, Palát J, Coufaliková K, Kukučka P, Codling G, Vitale CM, Koudelka Š, Klánová J. Open, High-Resolution EI+ Spectral Library of Anthropogenic Compounds. Front Public Health 2021; 9:622558. [PMID: 33768085 PMCID: PMC7985345 DOI: 10.3389/fpubh.2021.622558] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 02/08/2021] [Indexed: 01/21/2023] Open
Abstract
To address the lack of high-resolution electron ionisation mass spectral libraries (HR-[EI+]-MS) for environmental chemicals, a retention-indexed HR-[EI+]-MS library has been constructed following analysis of authentic compounds via GC-Orbitrap MS. The library is freely provided alongside a compound database of predicted physicochemical properties. Currently, the library contains over 350 compounds from 56 compound classes and includes a range of legacy and emerging contaminants. The RECETOX Exposome HR-[EI+]-MS library expands the number of freely available resources for use in full-scan chemical exposure studies and is available at: https://doi.org/10.5281/zenodo.4471217.
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Affiliation(s)
- Elliott J Price
- Faculty of Sports Studies, Masaryk University, Brno, Czechia.,RECETOX Centre, Masaryk University, Brno, Czechia
| | - Jirí Palát
- RECETOX Centre, Masaryk University, Brno, Czechia
| | | | - Petr Kukučka
- RECETOX Centre, Masaryk University, Brno, Czechia
| | | | | | | | - Jana Klánová
- RECETOX Centre, Masaryk University, Brno, Czechia
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36
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Yamamoto H, Nakayama Y, Tsugawa H. OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data. Metabolites 2021; 11:metabo11030149. [PMID: 33807892 PMCID: PMC7999099 DOI: 10.3390/metabo11030149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/20/2022] Open
Abstract
Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data.
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Affiliation(s)
- Hiroyuki Yamamoto
- Human Metabolome Technologies, Inc., 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
- Correspondence:
| | - Yasumune Nakayama
- Department of Applied Microbial Technology, Sojo University, 4-22-1 Ikeda, Kumamoto 860-0082, Japan;
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan;
- RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
- Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
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37
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Chang HY, Colby SM, Du X, Gomez JD, Helf MJ, Kechris K, Kirkpatrick CR, Li S, Patti GJ, Renslow RS, Subramaniam S, Verma M, Xia J, Young JD. A Practical Guide to Metabolomics Software Development. Anal Chem 2021; 93:1912-1923. [PMID: 33467846 PMCID: PMC7859930 DOI: 10.1021/acs.analchem.0c03581] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
![]()
A growing number
of software tools have been developed for metabolomics
data processing and analysis. Many new tools are contributed by metabolomics
practitioners who have limited prior experience with software development,
and the tools are subsequently implemented by users with expertise
that ranges from basic point-and-click data analysis to advanced coding.
This Perspective is intended to introduce metabolomics software users
and developers to important considerations that determine the overall
impact of a publicly available tool within the scientific community.
The recommendations reflect the collective experience of an NIH-sponsored
Metabolomics Consortium working group that was formed with the goal
of researching guidelines and best practices for metabolomics tool
development. The recommendations are aimed at metabolomics researchers
with little formal background in programming and are organized into
three stages: (i) preparation, (ii) tool development, and (iii) distribution
and maintenance.
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Affiliation(s)
- Hui-Yin Chang
- Department of Pathology, University of Michigan, 1301 Catherine Street, Ann Arbor, Michigan 48109, United States.,Department of Biomedical Sciences and Engineering, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City 320, Taiwan
| | - Sean M Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland, Washington 99352, United States
| | - Xiuxia Du
- Department of Bioinformatics & Genomics, University of North Carolina at Charlotte, 9201 University City Boulevard, Charlotte, North Carolina 28223, United States
| | - Javier D Gomez
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States
| | - Maximilian J Helf
- Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, 533 Tower Road, Ithaca, New York 14853, United States
| | - Katerina Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, 13001 East 17th Place B119, Aurora, Colorado 80045, United States
| | - Christine R Kirkpatrick
- San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, California 92093, United States
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, Connecticut 06032, United States
| | - Gary J Patti
- Department of Chemistry, Department of Medicine, and Siteman Cancer Center, Washington University in St. Louis, CB 1134, One Brookings Drive, St. Louis, Missouri 63130, United States
| | - Ryan S Renslow
- Biological Sciences Division, Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K8-98, Richland, Washington 99352, United States.,Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, P.O. Box 646515, Pullman, Washington 99164, United States
| | - Shankar Subramaniam
- San Diego Supercomputer Center, University of California San Diego, MC 0505, 9500 Gilman Drive, La Jolla, California 92093, United States.,Department of Bioengineering, Department of Computer Science and Engineering, Department of Cellular and Molecular Medicine, and Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive #0412, La Jolla, California 92093, United States
| | - Mukesh Verma
- Epidemiology and Genomics Research Program, National Cancer Institute, National Institutes of Health, Suite 4E102, 9609 Medical Center Drive, MSC 9763, Rockville, Maryland 20850, United States
| | - Jianguo Xia
- Faculty of Agricultural and Environmental Sciences, McGill University, 21111 Lakeshore Road, Ste. Anne de Bellevue, Quebec H9X 3 V9, Canada
| | - Jamey D Young
- Department of Chemical and Biomolecular Engineering, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States.,Department of Molecular Physiology and Biophysics, Vanderbilt University, PMB 351604, 2301 Vanderbilt Place, Nashville, Tennessee 37235, United States
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38
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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39
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Helmus R, Ter Laak TL, van Wezel AP, de Voogt P, Schymanski EL. patRoon: open source software platform for environmental mass spectrometry based non-target screening. J Cheminform 2021; 13:1. [PMID: 33407901 PMCID: PMC7789171 DOI: 10.1186/s13321-020-00477-w] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 11/23/2020] [Indexed: 12/22/2022] Open
Abstract
Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.
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Affiliation(s)
- Rick Helmus
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94240, 1090 GE, Amsterdam, The Netherlands.
| | - Thomas L Ter Laak
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94240, 1090 GE, Amsterdam, The Netherlands.,KWR Water Research Institute, Chemical Water Quality and Health, P.O. Box 1072, 3430 BB, Nieuwegein, The Netherlands
| | - Annemarie P van Wezel
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94240, 1090 GE, Amsterdam, The Netherlands
| | - Pim de Voogt
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, P.O. Box 94240, 1090 GE, Amsterdam, The Netherlands
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, L-4367, Belvaux, Luxembourg
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40
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Uppal K. Models of Metabolomic Networks. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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41
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Lloyd GR, Jankevics A, Weber RJM. Struct: an R/bioconductor-based framework for standardised metabolomics data analysis and beyond. Bioinformatics 2020; 36:5551-5552. [PMID: 33325493 PMCID: PMC8016465 DOI: 10.1093/bioinformatics/btaa1031] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 10/30/2020] [Accepted: 11/28/2020] [Indexed: 01/21/2023] Open
Abstract
Summary Implementing and combining methods from a diverse range of R/Bioconductor packages into ‘omics’ data analysis workflows represents a significant challenge in terms of standardization, readability and reproducibility. Here, we present an R/Bioconductor package, named struct (Statistics in R using Class-based Templates), which defines a suite of class-based templates that allows users to develop and implement highly standardized and readable statistical analysis workflows. Struct integrates with the STATistics Ontology to ensure consistent reporting and maximizes semantic interoperability. We also present a toolbox, named structToolbox, which includes an extensive set of commonly used data analysis methods that have been implemented using struct. This toolbox can be used to build data-analysis workflows for metabolomics and other omics technologies. Availability and implementation struct and structToolbox are implemented in R, and are freely available from Bioconductor (http://bioconductor.org/packages/struct and http://bioconductor.org/packages/structToolbox), including documentation and vignettes. Source code is available and maintained at https://github.com/computational-metabolomics.
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Affiliation(s)
- Gavin Rhys Lloyd
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Andris Jankevics
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Ralf J M Weber
- Phenome Centre Birmingham and School of Biosciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
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Dataset on the Effects of Different Pre-Harvest Factors on the Metabolomics Profile of Lettuce (Lactuca sativa L.) Leaves. DATA 2020. [DOI: 10.3390/data5040119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
The study of the relationship between cultivated plants and environmental factors can provide information ranging from a deeper understanding of the plant biological system to the development of more effective management strategies for improving yield, quality, and sustainability of the produce. In this article, we present a comprehensive metabolomics dataset of two phytochemically divergent lettuce (Lactuca sativa L.) butterhead varieties under different growing conditions. Plants were cultivated in hydroponics in a growth chamber with ambient control. The pre-harvest factors that were independently investigated were light intensity (two levels), the ionic strength of the nutrient solutions (three levels), and the molar ratio of three macroelements (K, Mg, and Ca) in the nutrient solution (three levels). We used an untargeted, mass-spectrometry-based approach to characterize the metabolomics profiles of leaves harvested 19 days after transplant. The data revealed the ample impact on both primary and secondary metabolism and its range of variation. Moreover, our dataset is useful for uncovering the complex effects of the genotype, the environmental factor(s), and their interaction, which may deserve further investigation.
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Gatto L, Gibb S, Rainer J. MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data. J Proteome Res 2020; 20:1063-1069. [PMID: 32902283 DOI: 10.1021/acs.jproteome.0c00313] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing, and visualization of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry experiments on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualization.
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Affiliation(s)
- Laurent Gatto
- Computational Biology Unit, de Duve Institute, Université catholique de Louvain, Brussels, 1200, Belgium
| | - Sebastian Gibb
- Department of Anaesthesiology and Intensive Care, University Medicine Greifswald, University of Greifswald, 17475 Greifswald, Germany
| | - Johannes Rainer
- Institute for Biomedicine, Eurac Research, Affiliated Institute of the University of Lübeck, 39100 Bolzano, Italy
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Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 2020; 17:243-255. [PMID: 32380880 DOI: 10.1080/14789450.2020.1766975] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
INTRODUCTION Metabolomics has become a crucial part of systems biology; however, data analysis is still often undertaken in a reductionist way focusing on changes in individual metabolites. Whilst such approaches indeed provide relevant insights into the metabolic phenotype of an organism, the intricate nature of metabolic relationships may be better explored when considering the whole system. AREAS COVERED This review highlights multiple network strategies that can be applied for metabolomics data analysis from different perspectives including: association networks based on quantitative information, mass spectra similarity networks to assist metabolite annotation and biochemical networks for systematic data interpretation. We also highlight some relevant insights into metabolic organization obtained through the exploration of such approaches. EXPERT OPINION Network based analysis is an established method that allows the identification of non-intuitive metabolic relationships as well as the identification of unknown compounds in mass spectrometry. Additionally, the representation of data from metabolomics within the context of metabolic networks is intuitive and allows for the use of statistical analysis that can better summarize relevant metabolic changes from a systematic perspective.
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Affiliation(s)
- Leonardo Perez De Souza
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany
| | - Saleh Alseekh
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev , Beersheba, Israel
| | - Alisdair R Fernie
- Department of molecular physiology, Max-Planck-Institute of Molecular Plant Physiology , Potsdam-Golm, Germany.,Department of plant metabolomics, Centre of Plant Systems Biology and Biotechnology , Plovdiv, Bulgaria
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Pourchet M, Debrauwer L, Klanova J, Price EJ, Covaci A, Caballero-Casero N, Oberacher H, Lamoree M, Damont A, Fenaille F, Vlaanderen J, Meijer J, Krauss M, Sarigiannis D, Barouki R, Le Bizec B, Antignac JP. Suspect and non-targeted screening of chemicals of emerging concern for human biomonitoring, environmental health studies and support to risk assessment: From promises to challenges and harmonisation issues. ENVIRONMENT INTERNATIONAL 2020; 139:105545. [PMID: 32361063 DOI: 10.1016/j.envint.2020.105545] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 02/02/2020] [Accepted: 02/02/2020] [Indexed: 05/07/2023]
Abstract
Large-scale suspect and non-targeted screening approaches based on high-resolution mass spectrometry (HRMS) are today available for chemical profiling and holistic characterisation of biological samples. These advanced techniques allow the simultaneous detection of a large number of chemical features, including markers of human chemical exposure. Such markers are of interest for biomonitoring, environmental health studies and support to risk assessment. Furthermore, these screening approaches have the promising capability to detect chemicals of emerging concern (CECs), document the extent of human chemical exposure, generate new research hypotheses and provide early warning support to policy. Whilst of growing importance in the environment and food safety areas, respectively, CECs remain poorly addressed in the field of human biomonitoring. This shortfall is due to several scientific and methodological reasons, including a global lack of harmonisation. In this context, the main aim of this paper is to present an overview of the basic principles, promises and challenges of suspect and non-targeted screening approaches applied to human samples as this specific field introduce major specificities compared to other fields. Focused on liquid chromatography coupled to HRMS-based data acquisition methods, this overview addresses all steps of these new analytical workflows. Beyond this general picture, the main activities carried out on this topic within the particular framework of the European Human Biomonitoring initiative (project HBM4EU, 2017-2021) are described, with an emphasis on harmonisation measures.
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Affiliation(s)
| | - Laurent Debrauwer
- TOXALIM (Research Centre in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University, 31027 Toulouse, France; Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toxalim, INRAE, F-31027 Toulouse, France
| | - Jana Klanova
- RECETOX Centre, Masaryk University, Brno, Czech Republic
| | - Elliott J Price
- RECETOX Centre, Masaryk University, Brno, Czech Republic; Faculty of Sports Studies, Masaryk University, Brno, Czech Republic
| | - Adrian Covaci
- Toxicological Center, University of Antwerp, Belgium
| | | | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Austria
| | - Marja Lamoree
- Vrije Universiteit, Department Environment & Health, Amsterdam, the Netherlands
| | - Annelaure Damont
- Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, Gif-sur-Yvette, France
| | - François Fenaille
- Service de Pharmacologie et d'Immunoanalyse, Laboratoire d'Etude du Métabolisme des Médicaments, CEA, INRA, Université Paris Saclay, MetaboHUB, Gif-sur-Yvette, France
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Jeroen Meijer
- Vrije Universiteit, Department Environment & Health, Amsterdam, the Netherlands; Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, the Netherlands
| | - Martin Krauss
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Denis Sarigiannis
- HERACLES Research Center on the Exposome and Health, Aristotle University of Thessaloniki, Greece
| | - Robert Barouki
- Unité UMR-S 1124 Inserm-Université Paris Descartes "Toxicologie Pharmacologie et Signalisation Cellulaire", Paris, France
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Abstract
Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.
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Affiliation(s)
- Miriam Sindelar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
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Eicher T, Kinnebrew G, Patt A, Spencer K, Ying K, Ma Q, Machiraju R, Mathé EA. Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources. Metabolites 2020; 10:E202. [PMID: 32429287 PMCID: PMC7281435 DOI: 10.3390/metabo10050202] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/07/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.
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Affiliation(s)
- Tara Eicher
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
| | - Garrett Kinnebrew
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Bioinformatics Shared Resource Group, The Ohio State University, Columbus, OH 43210, USA
| | - Andrew Patt
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
| | - Kyle Spencer
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Biomedical Sciences Graduate Program, The Ohio State University, Columbus, OH 43210, USA
- Nationwide Children’s Research Hospital, Columbus, OH 43210, USA
| | - Kevin Ying
- Comprehensive Cancer Center, The Ohio State University and James Cancer Hospital, Columbus, OH 43210, USA;
- Molecular, Cellular and Developmental Biology Program, The Ohio State University, Columbus, OH 43210, USA
| | - Qin Ma
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
| | - Raghu Machiraju
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Computer Science and Engineering Department, The Ohio State University College of Engineering, Columbus, OH 43210, USA
- Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Ewy A. Mathé
- Biomedical Informatics Department, The Ohio State University College of Medicine, Columbus, OH 43210, USA; (T.E.); (G.K.); (K.S.); (Q.M.); (R.M.)
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences, NIH, 9800 Medical Center Dr., Rockville, MD, 20892, USA;
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de Anda-Jáuregui G, Hernández-Lemus E. Computational Oncology in the Multi-Omics Era: State of the Art. Front Oncol 2020; 10:423. [PMID: 32318338 PMCID: PMC7154096 DOI: 10.3389/fonc.2020.00423] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 03/10/2020] [Indexed: 12/24/2022] Open
Abstract
Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.
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Affiliation(s)
- Guillermo de Anda-Jáuregui
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Cátedras Conacyt Para Jóvenes Investigadores, National Council on Science and Technology, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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50
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O'Shea K, Misra BB. Software tools, databases and resources in metabolomics: updates from 2018 to 2019. Metabolomics 2020; 16:36. [PMID: 32146531 DOI: 10.1007/s11306-020-01657-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/01/2020] [Indexed: 12/24/2022]
Abstract
Metabolomics has evolved as a discipline from a discovery and functional genomics tool, and is now a cornerstone in the era of big data-driven precision medicine. Sample preparation strategies and analytical technologies have seen enormous growth, and keeping pace with data analytics is challenging, to say the least. This review introduces and briefly presents around 100 metabolomics software resources, tools, databases, and other utilities that have surfaced or have improved in 2019. Table 1 provides the computational dependencies of the tools, categorizes the resources based on utility and ease of use, and provides hyperlinks to webpages where the tools can be downloaded or used. This review intends to keep the community of metabolomics researchers up to date with all the software tools, resources, and databases developed in 2019, in one place.
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Affiliation(s)
- Keiron O'Shea
- Institute of Biological, Environmental, and Rural Studies, Aberystwyth University, Ceredigion, Wales, SY23 3DA, UK
| | - Biswapriya B Misra
- Center for Precision Medicine, Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
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