1
|
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: 4] [Impact Index Per Article: 4.0] [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.
Collapse
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.
| |
Collapse
|
2
|
Lu Y, Pang Z, Xia J. Comprehensive investigation of pathway enrichment methods for functional interpretation of LC-MS global metabolomics data. Brief Bioinform 2023; 24:bbac553. [PMID: 36572652 PMCID: PMC9851290 DOI: 10.1093/bib/bbac553] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/31/2022] [Accepted: 11/15/2022] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Global or untargeted metabolomics is widely used to comprehensively investigate metabolic profiles under various pathophysiological conditions such as inflammations, infections, responses to exposures or interactions with microbial communities. However, biological interpretation of global metabolomics data remains a daunting task. Recent years have seen growing applications of pathway enrichment analysis based on putative annotations of liquid chromatography coupled with mass spectrometry (LC-MS) peaks for functional interpretation of LC-MS-based global metabolomics data. However, due to intricate peak-metabolite and metabolite-pathway relationships, considerable variations are observed among results obtained using different approaches. There is an urgent need to benchmark these approaches to inform the best practices. RESULTS We have conducted a benchmark study of common peak annotation approaches and pathway enrichment methods in current metabolomics studies. Representative approaches, including three peak annotation methods and four enrichment methods, were selected and benchmarked under different scenarios. Based on the results, we have provided a set of recommendations regarding peak annotation, ranking metrics and feature selection. The overall better performance was obtained for the mummichog approach. We have observed that a ~30% annotation rate is sufficient to achieve high recall (~90% based on mummichog), and using semi-annotated data improves functional interpretation. Based on the current platforms and enrichment methods, we further propose an identifiability index to indicate the possibility of a pathway being reliably identified. Finally, we evaluated all methods using 11 COVID-19 and 8 inflammatory bowel diseases (IBD) global metabolomics datasets.
Collapse
Affiliation(s)
- Yao Lu
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
| | - Zhiqiang Pang
- Institute of Parasitology, McGill University, Quebec, Canada
| | - Jianguo Xia
- Department of Microbiology and Immunology, McGill University, Quebec, Canada
- Institute of Parasitology, McGill University, Quebec, Canada
| |
Collapse
|
3
|
Resurreccion EP, Fong KW. The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer. Metabolites 2022; 12:metabo12060488. [PMID: 35736421 PMCID: PMC9230859 DOI: 10.3390/metabo12060488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 02/06/2023] Open
Abstract
Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.
Collapse
Affiliation(s)
- Eleazer P. Resurreccion
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
| | - Ka-wing Fong
- Department of Toxicology and Cancer Biology, University of Kentucky, Lexington, KY 40506, USA;
- Markey Cancer Center, University of Kentucky, Lexington, KY 40506, USA
- Correspondence: ; Tel.: +1-859-562-3455
| |
Collapse
|
4
|
Zhu Y, Zang Q, Luo Z, He J, Zhang R, Abliz Z. An Organ-Specific Metabolite Annotation Approach for Ambient Mass Spectrometry Imaging Reveals Spatial Metabolic Alterations of a Whole Mouse Body. Anal Chem 2022; 94:7286-7294. [PMID: 35548855 DOI: 10.1021/acs.analchem.2c00557] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Rapid and accurate metabolite annotation in mass spectrometry imaging (MSI) can improve the efficiency of spatially resolved metabolomics studies and accelerate the discovery of reliable in situ disease biomarkers. To date, metabolite annotation tools in MSI generally utilize isotopic patterns, but high-throughput fragmentation-based identification and biological and technical factors that influence structure elucidation are active challenges. Here, we proposed an organ-specific, metabolite-database-driven approach to facilitate efficient and accurate MSI metabolite annotation. Using data-dependent acquisition (DDA) in liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) to generate high-coverage product ions, we identified 1620 unique metabolites from eight mouse organs (brain, liver, kidney, heart, spleen, lung, muscle, and pancreas) and serum. Following the evaluation of the adduct form difference of metabolite ions between LC-MS and airflow-assisted desorption electrospray ionization (AFADESI)-MSI and deciphering organ-specific metabolites, we constructed a metabolite database for MSI consisting of 27,407 adduct ions. An automated annotation tool, MSIannotator, was then created to conduct metabolite annotation in the MSI dataset with high efficiency and confidence. We applied this approach to profile the spatially resolved landscape of the whole mouse body and discovered that metabolites were distributed across the body in an organ-specific manner, which even spanned different mouse strains. Furthermore, the spatial metabolic alteration in diabetic mice was delineated across different organs, exhibiting that differentially expressed metabolites were mainly located in the liver, brain, and kidney, and the alanine, aspartate, and glutamate metabolism pathway was simultaneously altered in these three organs. This approach not only enables robust metabolite annotation and visualization on a body-wide level but also provides a valuable database resource for underlying organ-specific metabolic mechanisms.
Collapse
Affiliation(s)
- Ying Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingce Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zhigang Luo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Ruiping Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zeper Abliz
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China.,Key Laboratory of Mass Spectrometry Imaging and Metabolomics (Minzu University of China), National Ethnic Affairs Commission, Beijing 100081, China.,Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
| |
Collapse
|
5
|
Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
Collapse
|
6
|
Chen L, Lu W, Wang L, Xing X, Chen Z, Teng X, Zeng X, Muscarella AD, Shen Y, Cowan A, McReynolds MR, Kennedy BJ, Lato AM, Campagna SR, Singh M, Rabinowitz JD. Metabolite discovery through global annotation of untargeted metabolomics data. Nat Methods 2021; 18:1377-1385. [PMID: 34711973 PMCID: PMC8733904 DOI: 10.1038/s41592-021-01303-3] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 09/16/2021] [Indexed: 11/08/2022]
Abstract
Liquid chromatography-high-resolution mass spectrometry (LC-MS)-based metabolomics aims to identify and quantify all metabolites, but most LC-MS peaks remain unidentified. Here we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. The approach aims to generate, for all experimentally observed ion peaks, annotations that match the measured masses, retention times and (when available) tandem mass spectrometry fragmentation patterns. Peaks are connected based on mass differences reflecting adduction, fragmentation, isotopes, or feasible biochemical transformations. Global optimization generates a single network linking most observed ion peaks, enhances peak assignment accuracy, and produces chemically informative peak-peak relationships, including for peaks lacking tandem mass spectrometry spectra. Applying this approach to yeast and mouse data, we identified five previously unrecognized metabolites (thiamine derivatives and N-glucosyl-taurine). Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to substantially improve annotation coverage and accuracy in untargeted metabolomics datasets, facilitating metabolite discovery.
Collapse
Affiliation(s)
- Li Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Wenyun Lu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Lin Wang
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Xi Xing
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ziyang Chen
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, China
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Xin Teng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Xianfeng Zeng
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Antonio D Muscarella
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yihui Shen
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexis Cowan
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Melanie R McReynolds
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Brandon J Kennedy
- Lotus Separations, LLC, Department of Chemistry, Princeton University, Princeton, NJ, USA
| | - Ashley M Lato
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Shawn R Campagna
- Department of Chemistry, The University of Tennessee at Knoxville, Knoxville, TN, USA
| | - Mona Singh
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Joshua D Rabinowitz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
| |
Collapse
|
7
|
Bach E, Rogers S, Williamson J, Rousu J. Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification. Bioinformatics 2021; 37:1724-1731. [PMID: 33244585 PMCID: PMC8289373 DOI: 10.1093/bioinformatics/btaa998] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/27/2020] [Accepted: 11/17/2020] [Indexed: 11/14/2022] Open
Abstract
Motivation Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS2). Results We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1. Availability and implementation Software and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Eric Bach
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - John Williamson
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - Juho Rousu
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| |
Collapse
|
8
|
Diederen T, Delabrière A, Othman A, Reid ME, Zamboni N. Metabolomics. Metab Eng 2021. [DOI: 10.1002/9783527823468.ch9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
9
|
Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
Collapse
Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
| |
Collapse
|
10
|
Multi-omics Study of Planobispora rosea, Producer of the Thiopeptide Antibiotic GE2270A. mSystems 2021; 6:e0034121. [PMID: 34156292 PMCID: PMC8269224 DOI: 10.1128/msystems.00341-21] [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] [Indexed: 11/23/2022] Open
Abstract
Planobispora rosea is the natural producer of the potent thiopeptide antibiotic GE2270A. Here, we present the results of a metabolomics and transcriptomics analysis of P. rosea during production of GE2270A. The data generated provides useful insights into the biology of this genetically intractable bacterium. We characterize the details of the shutdown of protein biosynthesis and the respiratory chain associated with the end of the exponential growth phase. We also provide the first description of the phosphate regulon in P. rosea. Based on the transcriptomics data, we show that both phosphate and iron are limiting P. rosea growth in our experimental conditions. Additionally, we identified and validated a new biosynthetic gene cluster associated with the production of the siderophores benarthin and dibenarthin in P. rosea. Together, the metabolomics and transcriptomics data are used to inform and refine the very first genome-scale metabolic model for P. rosea, which will be a valuable framework for the interpretation of future studies of the biology of this interesting but poorly characterized species. IMPORTANCEPlanobispora rosea is a genetically intractable bacterium used for the production of GE2270A on an industrial scale. GE2270A is a potent thiopeptide antibiotic currently used as a precursor for the synthesis of two compounds under clinical studies for the treatment of Clostridium difficile infection and acne. Here, we present the very first systematic multi-omics investigation of this important bacterium, which provides a much-needed detailed picture of the dynamics of metabolism of P. rosea while producing GE2270A. Author Video: An author video summary of this article is available.
Collapse
|
11
|
Ludwig M, Nothias LF, Dührkop K, Koester I, Fleischauer M, Hoffmann MA, Petras D, Vargas F, Morsy M, Aluwihare L, Dorrestein PC, Böcker S. Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. NAT MACH INTELL 2020. [DOI: 10.1038/s42256-020-00234-6] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
12
|
Hosseini R, Hassanpour N, Liu LP, Hassoun S. Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics Using Probabilistic Modeling. Metabolites 2020; 10:E183. [PMID: 32375258 PMCID: PMC7281100 DOI: 10.3390/metabo10050183] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Revised: 04/19/2020] [Accepted: 04/27/2020] [Indexed: 12/22/2022] Open
Abstract
Motivation: Untargeted metabolomics comprehensively characterizes small molecules and elucidates activities of biochemical pathways within a biological sample. Despite computational advances, interpreting collected measurements and determining their biological role remains a challenge. Results: To interpret measurements, we present an inference-based approach, termed Probabilistic modeling for Untargeted Metabolomics Analysis (PUMA). Our approach captures metabolomics measurements and the biological network for the biological sample under study in a generative model and uses stochastic sampling to compute posterior probability distributions. PUMA predicts the likelihood of pathways being active, and then derives probabilistic annotations, which assign chemical identities to measurements. Unlike prior pathway analysis tools that analyze differentially active pathways, PUMA defines a pathway as active if the likelihood that the path generated the observed measurements is above a particular (user-defined) threshold. Due to the lack of "ground truth" metabolomics datasets, where all measurements are annotated and pathway activities are known, PUMA is validated on synthetic datasets that are designed to mimic cellular processes. PUMA, on average, outperforms pathway enrichment analysis by 8%. PUMA is applied to two case studies. PUMA suggests many biological meaningful pathways as active. Annotation results were in agreement to those obtained using other tools that utilize additional information in the form of spectral signatures. Importantly, PUMA annotates many measurements, suggesting 23 chemical identities for metabolites that were previously only identified as isomers, and a significant number of additional putative annotations over spectral database lookups. For an experimentally validated 50-compound dataset, annotations using PUMA yielded 0.833 precision and 0.676 recall.
Collapse
|