1
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. PLoS Comput Biol 2024; 20:e1011912. [PMID: 38843301 PMCID: PMC11185459 DOI: 10.1371/journal.pcbi.1011912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Revised: 06/18/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
- University of Connecticut School of Medicine, Farmington, Connecticut, United States of America
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2
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Mitchell JM, Chi Y, Thapa M, Pang Z, Xia J, Li S. Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580048. [PMID: 38405981 PMCID: PMC10888883 DOI: 10.1101/2024.02.13.580048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.
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Affiliation(s)
- Joshua M. Mitchell
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Yuanye Chi
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Maheshwor Thapa
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, Quebec, Canada
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA
- University of Connecticut School of Medicine, Farmington, CT 06032, USA
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3
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Peralbo-Molina Á, Solà-Santos P, Perera-Lluna A, Chicano-Gálvez E. Data Processing and Analysis in Mass Spectrometry-Based Metabolomics. Methods Mol Biol 2023; 2571:207-239. [PMID: 36152164 DOI: 10.1007/978-1-0716-2699-3_20] [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
Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.
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Affiliation(s)
- Ángela Peralbo-Molina
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain.
| | - Pol Solà-Santos
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Alexandre Perera-Lluna
- B2SLab, Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain
- Networking Biomedical Research Centre in the Subject Area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Barcelona, Spain
| | - Eduardo Chicano-Gálvez
- IMIBIC Mass Spectrometry and Molecular Imaging Unit, Maimonides, Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba (UCO), Córdoba, Spain
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4
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Zhou C, Li C, Cui H, Lin L. Metabolomics insights into the potential of encapsulated essential oils as multifunctional food additives. Crit Rev Food Sci Nutr 2022; 64:5143-5160. [PMID: 36454059 DOI: 10.1080/10408398.2022.2151974] [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: 12/05/2022]
Abstract
Growing consumer concern about foodborne disease outbreaks and health risks associated with chemical additives has propelled the usage of essential oils (EOs) as novel food additives, but are limited by instability. In this regard, a series of EOs nano/micro-capsules have been widely used to enhance their stability and improve food quality. However, classical food quality assessment methods are insufficient to fully characterize the effects of encapsulated EOs on food properties, including physical, biochemical, organoleptic, and microbial changes. Recently, the rapid development of high-throughput sequencing is accelerating the application of metabolomics in food safety and quality analysis. This review seeks to present the most recent achievements in the application of non-targeted metabolomics to identify and quantify the overall metabolite profile associated with food quality, which can guide the development of emerging food preservation technologies. The scientific findings confirm that metabolomics opens up exciting prospects for biomarker screening in food preservation and contributes to an in-depth understanding of the mechanisms of action (MoA) of EOs. Future research should focus on constructing food quality assessment criteria based on multi-omics technologies, which will drive the standardization and commercialization of EOs for food industry applications.
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Affiliation(s)
- Changqian Zhou
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Changzhu Li
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China
| | - Haiying Cui
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
| | - Lin Lin
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China
- State Key Laboratory of Utilization of Woody Oil Resource, Hunan Academy of Forestry, Changsha, China
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5
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González-Plaza JJ, Furlan C, Rijavec T, Lapanje A, Barros R, Tamayo-Ramos JA, Suarez-Diez M. Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels. Front Microbiol 2022; 13:1006946. [PMID: 36519168 PMCID: PMC9744117 DOI: 10.3389/fmicb.2022.1006946] [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: 07/29/2022] [Accepted: 11/02/2022] [Indexed: 08/31/2023] Open
Abstract
The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.
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Affiliation(s)
- Juan José González-Plaza
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | - Cristina Furlan
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
| | - Tomaž Rijavec
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Aleš Lapanje
- Department of Environmental Sciences, Jožef Stefan Institute, Ljubljana, Slovenia
| | - Rocío Barros
- International Research Centre in Critical Raw Materials-ICCRAM, University of Burgos, Burgos, Spain
| | | | - Maria Suarez-Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University and Research, Wageningen, Netherlands
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6
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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: 564] [Impact Index Per Article: 282.0] [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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7
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de Macedo LH, Souza COS, Gardinassi LG, Faccioli LH. CD14 regulates the metabolomic profiles of distinct macrophage subsets under steady and activated states. Immunobiology 2022; 227:152191. [DOI: 10.1016/j.imbio.2022.152191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 01/28/2022] [Accepted: 02/14/2022] [Indexed: 11/05/2022]
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8
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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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9
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Khoubnasabjafari M, Mogaddam MRA, Rahimpour E, Soleymani J, Saei AA, Jouyban A. Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications. Crit Rev Anal Chem 2021; 52:1461-1487. [PMID: 33691552 DOI: 10.1080/10408347.2021.1889961] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.
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Affiliation(s)
- Maryam Khoubnasabjafari
- Tuberculosis and Lung Diseases Research Center and Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.,Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohamad Reza Afshar Mogaddam
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Elaheh Rahimpour
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jafar Soleymani
- Pharmaceutical Analysis Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.,Liver and Gastrointestinal Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Ata Saei
- Department of Medical Biochemistry and Biophysics, Division of Physiological Chemistry I, Karolinska Institutet, Stockholm, Sweden
| | - Abolghasem Jouyban
- Food and Drug Safety Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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10
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Application of Differential Network Enrichment Analysis for Deciphering Metabolic Alterations. Metabolites 2020; 10:metabo10120479. [PMID: 33255384 PMCID: PMC7761243 DOI: 10.3390/metabo10120479] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
Modern analytical methods allow for the simultaneous detection of hundreds of metabolites, generating increasingly large and complex data sets. The analysis of metabolomics data is a multi-step process that involves data processing and normalization, followed by statistical analysis. One of the biggest challenges in metabolomics is linking alterations in metabolite levels to specific biological processes that are disrupted, contributing to the development of disease or reflecting the disease state. A common approach to accomplishing this goal involves pathway mapping and enrichment analysis, which assesses the relative importance of predefined metabolic pathways or other biological categories. However, traditional knowledge-based enrichment analysis has limitations when it comes to the analysis of metabolomics and lipidomics data. We present a Java-based, user-friendly bioinformatics tool named Filigree that provides a primarily data-driven alternative to the existing knowledge-based enrichment analysis methods. Filigree is based on our previously published differential network enrichment analysis (DNEA) methodology. To demonstrate the utility of the tool, we applied it to previously published studies analyzing the metabolome in the context of metabolic disorders (type 1 and 2 diabetes) and the maternal and infant lipidome during pregnancy.
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11
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Hu X, Li S, Cirillo P, Krigbaum N, Tran V, Ishikawa T, La Merrill MA, Jones DP, Cohn B. Metabolome Wide Association Study of serum DDT and DDE in Pregnancy and Early Postpartum. Reprod Toxicol 2020; 92:129-137. [PMID: 31102720 PMCID: PMC7055929 DOI: 10.1016/j.reprotox.2019.05.059] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 02/06/2023]
Abstract
The advancement of high-resolution metabolomics (HRM) and metabolome-wide-association study (MWAS) enables the readout of environmental effects in human specimens. We used HRM to understand DDT-induced alterations of in utero environment and potential health effects. Endogenous metabolites were measured in 397 maternal perinatal serum samples collected during 1959-1967 in the Child Health and Development Studies (CHDS) and in 16 maternal postnatal serum samples of mice treated with or without DDT. MWAS was performed to assess associations between metabolites and p,p'-DDT, o,p'-DDT and p,p'-DDE levels, followed by pathway analysis. Distinct metabolic profiles were found with p,p'-DDT and p,p'-DDE. Amino acids such arginine had a strong association with p,p'-DDT and o,p'-DDT in both women and mice, whereas lipids and acyl-carnitine intermediates were found exclusively associated with p,p'-DDE in CHDS women indicating mitochondrial impairment. It suggests that the role of serine and fatty acid metabolism on the causal disease pathway should be examined.
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Affiliation(s)
- Xin Hu
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Shuzhao Li
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Piera Cirillo
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - Nickilou Krigbaum
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - ViLinh Tran
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Tomoko Ishikawa
- Department of Environmental Toxicology, University of California, Davis, CA 95616, USA
| | - Michele A La Merrill
- Department of Environmental Toxicology, University of California, Davis, CA 95616, USA
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA.
| | - Barbara Cohn
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA.
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12
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Hu X, Li S, Cirillo PM, Krigbaum NY, Tran V, Jones DP, Cohn BA. Reprint of "Metabolome Wide Association Study of Serum Poly and Perfluoroalkyl Substances (PFASs) in Pregnancy and Early Postpartum". Reprod Toxicol 2020; 92:120-128. [PMID: 31923462 DOI: 10.1016/j.reprotox.2020.01.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 12/29/2022]
Abstract
High-resolution metabolomics (HRM) profiling of metabolic fingerprints can improve understanding of how poly and perfluoroalkyl substances (PFASs) induce metabolic alterations of in utero environment and impact fetal health. HRM profiling and quantification of PFASs were performed for 397 maternal perinatal serum samples collected from 1959-1967 in the Child Health and Development Studies (CHDS). We used Metabolome-Wide Association Studies (MWAS) and pathway enrichment analysis for metabolic associations with PFOS, its precursor EtFOSAA, and EtFOSAA-to-PFOS ratio. Distinct metabolic profiles were found with EtFOSAA and PFOS. Urea cycle metabolites such as arginine, lysine and creatine had opposite associations with EtFOSAA (negative) and PFOS (positive); whereas, carnitine shuttle metabolites were found to be exclusively and positively associated with PFOS indicating perturbation in fatty acid metabolism. These differential metabolic associations for precursor and end-product represent an important first step in identifying how PFASs alter the in utero environment and potentially leads to disease risk.
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Affiliation(s)
- Xin Hu
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Shuzhao Li
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Piera M Cirillo
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - Nickilou Y Krigbaum
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - ViLinh Tran
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA.
| | - Barbara A Cohn
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA.
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13
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Pittard WS, Villaveces CK, Li S. A Bioinformatics Primer to Data Science, with Examples for Metabolomics. Methods Mol Biol 2020; 2104:245-263. [PMID: 31953822 DOI: 10.1007/978-1-0716-0239-3_14] [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] [Indexed: 01/07/2023]
Abstract
With the increasing importance of big data in biomedicine, skills in data science are a foundation for the individual career development and for the progress of science. This chapter is a practical guide to working with high-throughput biomedical data. It covers how to understand and set up the computing environment, to start a research project with proper and effective data management, and to perform common bioinformatics tasks such as data wrangling, quality control, statistical analysis, and visualization, with examples on metabolomics data. Concepts and tools related to coding and scripting are discussed. Version control, knitr and Jupyter notebooks are important to project management, collaboration, and research reproducibility. Overall, this chapter describes a core set of skills to work in bioinformatics, and can serve as a reference text at the level of a graduate course and interfacing with data science.
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Affiliation(s)
- W Stephen Pittard
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Shuzhao Li
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA.
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Razzaq A, Sadia B, Raza A, Khalid Hameed M, Saleem F. Metabolomics: A Way Forward for Crop Improvement. Metabolites 2019; 9:E303. [PMID: 31847393 PMCID: PMC6969922 DOI: 10.3390/metabo9120303] [Citation(s) in RCA: 83] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/02/2019] [Accepted: 12/11/2019] [Indexed: 12/15/2022] Open
Abstract
Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Bushra Sadia
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
| | - Ali Raza
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan 430062, China;
| | - Muhammad Khalid Hameed
- School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad 38040, Pakistan; (A.R.); (B.S.)
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15
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Chong J, Xia J. MetaboAnalystR: an R package for flexible and reproducible analysis of metabolomics data. Bioinformatics 2019; 34:4313-4314. [PMID: 29955821 PMCID: PMC6289126 DOI: 10.1093/bioinformatics/bty528] [Citation(s) in RCA: 399] [Impact Index Per Article: 79.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Accepted: 06/27/2018] [Indexed: 11/24/2022] Open
Abstract
Summary The MetaboAnalyst web application has been widely used for metabolomics data analysis and interpretation. Despite its user-friendliness, the web interface has presented its inherent limitations (especially for advanced users) with regard to flexibility in creating customized workflow, support for reproducible analysis, and capacity in dealing with large data. To address these limitations, we have developed a companion R package (MetaboAnalystR) based on the R code base of the web server. The package has been thoroughly tested to ensure that the same R commands will produce identical results from both interfaces. MetaboAnalystR complements the MetaboAnalyst web server to facilitate transparent, flexible and reproducible analysis of metabolomics data. Availability and implementation MetaboAnalystR is freely available from https://github.com/xia-lab/MetaboAnalystR.
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Affiliation(s)
| | - Jianguo Xia
- To whom correspondence should be addressed. E-mail:
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16
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Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. Metaproteomic and Metabolomic Approaches for Characterizing the Gut Microbiome. Proteomics 2019; 19:e1800363. [PMID: 31321880 DOI: 10.1002/pmic.201800363] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/27/2019] [Indexed: 12/14/2022]
Abstract
The gut microbiome has been shown to play a significant role in human healthy and diseased states. The dynamic signaling that occurs between the host and microbiome is critical for the maintenance of host homeostasis. Analyzing the human microbiome with metaproteomics, metabolomics, and integrative multi-omics analyses can provide significant information on markers for healthy and diseased states, allowing for the eventual creation of microbiome-targeted treatments for diseases associated with dysbiosis. Metaproteomics enables functional activity information to be gained from the microbiome samples, while metabolomics provides insight into the overall metabolic states affecting/representing the host-microbiome interactions. Combining these functional -omic platforms together with microbiome composition profiling allows for a holistic overview on the functional and metabolic state of the microbiome and its influence on human health. Here the benefits of metaproteomics, metabolomics, and the integrative multi-omic approaches to investigating the gut microbiome in the context of human health and diseases are reviewed.
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Affiliation(s)
- Danielle L Peters
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Wenju Wang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Xu Zhang
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Zhibin Ning
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Janice Mayne
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
| | - Daniel Figeys
- Ottawa Institute of Systems Biology and Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada.,Canadian Institute for Advanced Research, 661 University Ave, Toronto, ON, M5G 1M1, Canada.,The University of Ottawa and Shanghai Institute of Materia Medica Joint Research Center on Systems and Personalized Pharmacology, 451 Smyth Road, Ottawa, ON, KIH 8M5, Canada
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17
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Hu X, Li S, Cirillo PM, Krigbaum NY, Tran V, Jones DP, Cohn BA. Metabolome Wide Association Study of Serum Poly and Perfluoroalkyl Substances (PFASs) in Pregnancy and Early Postpartum. Reprod Toxicol 2019; 87:70-78. [PMID: 31121237 DOI: 10.1016/j.reprotox.2019.05.058] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 05/07/2019] [Accepted: 05/09/2019] [Indexed: 12/16/2022]
Abstract
High-resolution metabolomics (HRM) profiling of metabolic fingerprints can improve understanding of how poly and perfluoroalkyl substances (PFASs) induce metabolic alterations of in utero environment and impact fetal health. HRM profiling and quantification of PFASs were performed for 397 maternal perinatal serum samples collected from 1959-1967 in the Child Health and Development Studies (CHDS). We used Metabolome-Wide Association Studies (MWAS) and pathway enrichment analysis for metabolic associations with PFOS, its precursor EtFOSAA, and EtFOSAA-to-PFOS ratio. Distinct metabolic profiles were found with EtFOSAA and PFOS. Urea cycle metabolites such as arginine, lysine and creatine had opposite associations with EtFOSAA (negative) and PFOS (positive); whereas, carnitine shuttle metabolites were found to be exclusively and positively associated with PFOS indicating perturbation in fatty acid metabolism. These differential metabolic associations for precursor and end-product represent an important first step in identifying how PFASs alter the in utero environment and potentially leads to disease risk.
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Affiliation(s)
- Xin Hu
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Shuzhao Li
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Piera M Cirillo
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - Nickilou Y Krigbaum
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA
| | - ViLinh Tran
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA
| | - Dean P Jones
- Department of Medicine, School of Medicine, Emory University, Atlanta, GA 30303, USA.
| | - Barbara A Cohn
- The Center for Research on Women and Children's Health, Child Health and Development Studies, Public Health Institute, 1683 Shattuck Avenue, Suite B, Berkeley, CA 94709, USA.
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18
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Dan Z, Chen Y, Xu Y, Huang J, Huang J, Hu J, Yao G, Zhu Y, Huang W. A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments. PLANT BIOTECHNOLOGY JOURNAL 2019; 17:906-913. [PMID: 30321482 PMCID: PMC6587747 DOI: 10.1111/pbi.13024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 08/21/2018] [Accepted: 10/10/2018] [Indexed: 05/05/2023]
Abstract
Marker-based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F1 hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight (TGW) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 (r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 (r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted (r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 (r = 0.72). Our study demonstrates that the TGW of F1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate-adaptive varieties.
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Affiliation(s)
- Zhiwu Dan
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Yunping Chen
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Yanghong Xu
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Junran Huang
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Jishuai Huang
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Jun Hu
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Guoxin Yao
- School of Life and Science TechnologyHubei Engineering UniversityXiaoganChina
| | - Yingguo Zhu
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
| | - Wenchao Huang
- State Key Laboratory of Hybrid RiceKey Laboratory for Research and Utilization of Heterosis in Indica RiceThe Yangtze River Valley Hybrid Rice Collaboration & Innovation CenterCollege of Life SciencesWuhan UniversityWuhanChina
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MetaboAnalystR 2.0: From Raw Spectra to Biological Insights. Metabolites 2019; 9:metabo9030057. [PMID: 30909447 PMCID: PMC6468840 DOI: 10.3390/metabo9030057] [Citation(s) in RCA: 206] [Impact Index Per Article: 41.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 03/20/2019] [Accepted: 03/21/2019] [Indexed: 02/07/2023] Open
Abstract
Global metabolomics based on high-resolution liquid chromatography mass spectrometry (LC-MS) has been increasingly employed in recent large-scale multi-omics studies. Processing and interpretation of these complex metabolomics datasets have become a key challenge in current computational metabolomics. Here, we introduce MetaboAnalystR 2.0 for comprehensive LC-MS data processing, statistical analysis, and functional interpretation. Compared to the previous version, this new release seamlessly integrates XCMS and CAMERA to support raw spectral processing and peak annotation, and also features high-performance implementations of mummichog and GSEA approaches for predictions of pathway activities. The application and utility of the MetaboAnalystR 2.0 workflow were demonstrated using a synthetic benchmark dataset and a clinical dataset. In summary, MetaboAnalystR 2.0 offers a unified and flexible workflow that enables end-to-end analysis of LC-MS metabolomics data within the open-source R environment.
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20
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Recent Advances in MS-Based Plant Proteomics: Proteomics Data Validation Through Integration with Other Classic and -Omics Approaches. PROGRESS IN BOTANY 2019. [DOI: 10.1007/124_2019_32] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Beale DJ, Pinu FR, Kouremenos KA, Poojary MM, Narayana VK, Boughton BA, Kanojia K, Dayalan S, Jones OAH, Dias DA. Review of recent developments in GC-MS approaches to metabolomics-based research. Metabolomics 2018; 14:152. [PMID: 30830421 DOI: 10.1007/s11306-018-1449-2] [Citation(s) in RCA: 242] [Impact Index Per Article: 40.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Accepted: 11/08/2018] [Indexed: 12/19/2022]
Abstract
BACKGROUND Metabolomics aims to identify the changes in endogenous metabolites of biological systems in response to intrinsic and extrinsic factors. This is accomplished through untargeted, semi-targeted and targeted based approaches. Untargeted and semi-targeted methods are typically applied in hypothesis-generating investigations (aimed at measuring as many metabolites as possible), while targeted approaches analyze a relatively smaller subset of biochemically important and relevant metabolites. Regardless of approach, it is well recognized amongst the metabolomics community that gas chromatography-mass spectrometry (GC-MS) is one of the most efficient, reproducible and well used analytical platforms for metabolomics research. This is due to the robust, reproducible and selective nature of the technique, as well as the large number of well-established libraries of both commercial and 'in house' metabolite databases available. AIM OF REVIEW This review provides an overview of developments in GC-MS based metabolomics applications, with a focus on sample preparation and preservation techniques. A number of chemical derivatization (in-time, in-liner, offline and microwave assisted) techniques are also discussed. Electron impact ionization and a summary of alternate mass analyzers are highlighted, along with a number of recently reported new GC columns suited for metabolomics. Lastly, multidimensional GC-MS and its application in environmental and biomedical research is presented, along with the importance of bioinformatics. KEY SCIENTIFIC CONCEPTS OF REVIEW The purpose of this review is to both highlight and provide an update on GC-MS analytical techniques that are common in metabolomics studies. Specific emphasis is given to the key steps within the GC-MS workflow that those new to this field need to be aware of and the common pitfalls that should be looked out for when starting in this area.
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Affiliation(s)
- David J Beale
- Land and Water, Commonwealth Scientific & Industrial Research Organization (CSIRO), P.O. Box 2583, Brisbane, QLD, 4001, Australia.
| | - Farhana R Pinu
- The New Zealand Institute for Plant & Food Research Limited, Private Bag 92169, Auckland, 1142, New Zealand
| | - Konstantinos A Kouremenos
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
- Trajan Scientific and Medical, 7 Argent Pl, Ringwood, 3134, Australia
| | - Mahesha M Poojary
- Chemistry Section, School of Science and Technology, University of Camerino, via S. Agostino 1, 62032, Camerino, Italy
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958, Frederiksberg C, Denmark
| | - Vinod K Narayana
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Berin A Boughton
- Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, 3010, Australia
| | - Komal Kanojia
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Saravanan Dayalan
- Metabolomics Australia, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, 3010, Australia
| | - Oliver A H Jones
- Australian Centre for Research on Separation Science (ACROSS), School of Science, RMIT University, GPO Box 2476, Melbourne, 3001, Australia
| | - Daniel A Dias
- School of Health and Biomedical Sciences, Discipline of Laboratory Medicine, RMIT University, PO Box 71, Bundoora, 3083, Australia.
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22
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Valledor L, Carbó M, Lamelas L, Escandón M, Colina FJ, Cañal MJ, Meijón M. When the Tree Let Us See the Forest: Systems Biology and Natural Variation Studies in Forest Species. ACTA ACUST UNITED AC 2018. [DOI: 10.1007/124_2018_22] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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