{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:33:27Z","timestamp":1774499607506,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011912","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000}}],"reference-count":63,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 CA235493"],"award-info":[{"award-number":["U01 CA235493"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI149746"],"award-info":[{"award-number":["R01 AI149746"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI149746 S1"],"award-info":[{"award-number":["R01 AI149746 S1"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["UM1 HG012651"],"award-info":[{"award-number":["UM1 HG012651"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011912","type":"journal-article","created":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T13:59:37Z","timestamp":1717682377000},"page":"e1011912","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1598-1596","authenticated-orcid":true,"given":"Joshua M.","family":"Mitchell","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2139-8172","authenticated-orcid":true,"given":"Yuanye","family":"Chi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-1584","authenticated-orcid":true,"given":"Maheshwor","family":"Thapa","sequence":"additional","affiliation":[]},{"given":"Zhiqiang","family":"Pang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2040-2624","authenticated-orcid":true,"given":"Jianguo","family":"Xia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-2539","authenticated-orcid":true,"given":"Shuzhao","family":"Li","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"pcbi.1011912.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-1-0716-0239-3_1","article-title":"Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations","volume":"2104","author":"S. Barnes","year":"2020","journal-title":"Methods Mol Biol"},{"issue":"9","key":"pcbi.1011912.ref002","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.1161\/CIRCRESAHA.117.311002","article-title":"Cardiovascular Metabolomics","volume":"122","author":"RW McGarrah","year":"2018","journal-title":"Circ Res"},{"issue":"6","key":"pcbi.1011912.ref003","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41580-019-0108-4","article-title":"Identification of bioactive metabolites using activity metabolomics","volume":"20","author":"MM Rinschen","year":"2019","journal-title":"Nature Reviews Molecular Cell Biology"},{"issue":"10","key":"pcbi.1011912.ref004","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1038\/s42255-023-00903-x","article-title":"Metabolomic epidemiology offers insights into disease aetiology","volume":"5","author":"H Fuller","year":"2023","journal-title":"Nat Metab"},{"issue":"D1","key":"pcbi.1011912.ref005","doi-asserted-by":"crossref","first-page":"D463","DOI":"10.1093\/nar\/gkv1042","article-title":"Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools","volume":"44","author":"M Sud","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"pcbi.1011912.ref006","first-page":"D440","article-title":"MetaboLights: a resource evolving in response to the needs of its scientific community","volume":"48","author":"K Haug","year":"2019","journal-title":"Nucleic Acids Research"},{"issue":"10","key":"pcbi.1011912.ref007","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1038\/s41592-020-0955-0","article-title":"MassIVE.quant: a community resource of quantitative mass spectrometry-based proteomics datasets","volume":"17","author":"M Choi","year":"2020","journal-title":"Nat Methods"},{"key":"pcbi.1011912.ref008","doi-asserted-by":"crossref","first-page":"102288","DOI":"10.1016\/j.cbpa.2023.102288","article-title":"Recent advances in mass spectrometry-based computational metabolomics","volume":"74","author":"TMD Ebbels","year":"2023","journal-title":"Curr Opin Chem Biol"},{"key":"pcbi.1011912.ref009","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1007\/s40495-017-0107-0","article-title":"Bioinformatics tools for the interpretation of metabolomics data","volume":"3","author":"LG Gardinassi","year":"2017","journal-title":"Current Pharmacology Reports"},{"key":"pcbi.1011912.ref010","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/978-1-0716-0239-3_14","article-title":"A Bioinformatics Primer to Data Science, with Examples for Metabolomics","author":"WS Pittard","year":"2020","journal-title":"Computational Methods and Data Analysis for Metabolomics"},{"issue":"3","key":"pcbi.1011912.ref011","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1021\/ac051437y","article-title":"XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification","volume":"78","author":"CA Smith","year":"2006","journal-title":"Analytical Chemistry"},{"key":"pcbi.1011912.ref012","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cbpa.2015.11.009","article-title":"A roadmap for the XCMS family of software solutions in metabolomics","volume":"30","author":"NG Mahieu","year":"2016","journal-title":"Curr Opin Chem Biol"},{"issue":"1","key":"pcbi.1011912.ref013","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/ac202450g","article-title":"CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography\/mass spectrometry data sets","volume":"84","author":"C Kuhl","year":"2012","journal-title":"Anal Chem"},{"issue":"3","key":"pcbi.1011912.ref014","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1021\/ac102980g","article-title":"metaXCMS: Second-Order Analysis of Untargeted Metabolomics Data","volume":"83","author":"R Tautenhahn","year":"2011","journal-title":"Analytical Chemistry"},{"issue":"11","key":"pcbi.1011912.ref015","doi-asserted-by":"crossref","first-page":"5035","DOI":"10.1021\/ac300698c","article-title":"XCMS Online: a web-based platform to process untargeted metabolomic data","volume":"84","author":"R Tautenhahn","year":"2012","journal-title":"Anal Chem"},{"issue":"45","key":"pcbi.1011912.ref016","doi-asserted-by":"crossref","first-page":"15024","DOI":"10.1021\/acs.analchem.1c02687","article-title":"SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS","volume":"93","author":"A Delabriere","year":"2021","journal-title":"Analytical Chemistry"},{"issue":"6","key":"pcbi.1011912.ref017","doi-asserted-by":"crossref","first-page":"752","DOI":"10.1002\/dta.2552","article-title":"Automated optimization of XCMS parameters for improved peak picking of liquid chromatography-mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics","volume":"11","author":"SK Manier","year":"2019","journal-title":"Drug Test Anal"},{"issue":"8","key":"pcbi.1011912.ref018","doi-asserted-by":"crossref","first-page":"5724","DOI":"10.1021\/acs.analchem.9b04804","article-title":"AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing","volume":"92","author":"C McLean","year":"2020","journal-title":"Analytical Chemistry"},{"issue":"5","key":"pcbi.1011912.ref019","doi-asserted-by":"crossref","DOI":"10.3390\/metabo10050186","article-title":"MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics","volume":"10","author":"Z Pang","year":"2020","journal-title":"Metabolites"},{"issue":"1","key":"pcbi.1011912.ref020","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.1038\/s41467-022-32155-w","article-title":"TidyMass an object-oriented reproducible analysis framework for LC\u2013MS data","volume":"13","author":"X Shen","year":"2022","journal-title":"Nature Communications"},{"issue":"19","key":"pcbi.1011912.ref021","doi-asserted-by":"crossref","first-page":"9679","DOI":"10.1021\/acs.analchem.5b01660","article-title":"DynaMet: a fully automated pipeline for dynamic LC-MS data","volume":"87","author":"P Kiefer","year":"2015","journal-title":"Anal Chem"},{"issue":"10","key":"pcbi.1011912.ref022","doi-asserted-by":"crossref","DOI":"10.3390\/metabo10100416","article-title":"A Python-Based Pipeline for Preprocessing LC-MS Data for Untargeted Metabolomics Workflows","volume":"10","author":"G Riquelme","year":"2020","journal-title":"Metabolites"},{"issue":"1","key":"pcbi.1011912.ref023","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1038\/s41467-023-39889-1","article-title":"Trackable and scalable LC-MS metabolomics data processing using asari","volume":"14","author":"S Li","year":"2023","journal-title":"Nature Communications"},{"issue":"8","key":"pcbi.1011912.ref024","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.tibtech.2005.05.009","article-title":"Metabolomics or metabolite profiles","volume":"23","author":"SG Villas-B\u00f4as","year":"2005","journal-title":"Trends in Biotechnology"},{"issue":"9","key":"pcbi.1011912.ref025","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/s11306-022-01926-3","article-title":"Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management","volume":"18","author":"JA Kirwan","year":"2022","journal-title":"Metabolomics"},{"issue":"11","key":"pcbi.1011912.ref026","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11306-023-02060-4","article-title":"Metabolomics 2022 workshop report: state of QA\/QC best practices in LC\u2013MS-based untargeted metabolomics, informed through mQACC community engagement initiatives","volume":"19","author":"WB Dunn","year":"2023","journal-title":"Metabolomics"},{"issue":"12","key":"pcbi.1011912.ref027","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11306-022-01963-y","article-title":"Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools","volume":"18","author":"NF de Jonge","year":"2022","journal-title":"Metabolomics"},{"issue":"4","key":"pcbi.1011912.ref028","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.1021\/es5002105","article-title":"Identifying small molecules via high resolution mass spectrometry: communicating confidence","volume":"48","author":"EL Schymanski","year":"2014","journal-title":"Environ Sci Technol"},{"issue":"4","key":"pcbi.1011912.ref029","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1038\/s41587-023-01690-2","article-title":"Integrative analysis of multimodal mass spectrometry data in MZmine 3","volume":"41","author":"R Schmid","year":"2023","journal-title":"Nature Biotechnology"},{"issue":"9","key":"pcbi.1011912.ref030","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1038\/nmeth.3959","article-title":"OpenMS: a flexible open-source software platform for mass spectrometry data analysis","volume":"13","author":"HL R\u00f6st","year":"2016","journal-title":"Nature Methods"},{"issue":"10","key":"pcbi.1011912.ref031","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1038\/s41580-023-00615-w","article-title":"The technological landscape and applications of single-cell multi-omics","volume":"24","author":"A Baysoy","year":"2023","journal-title":"Nature Reviews Molecular Cell Biology"},{"key":"pcbi.1011912.ref032","doi-asserted-by":"crossref","first-page":"1177932219899051","DOI":"10.1177\/1177932219899051","article-title":"Multi-omics Data Integration, Interpretation, and Its Application","volume":"14","author":"I Subramanian","year":"2020","journal-title":"Bioinform Biol Insights"},{"issue":"W1","key":"pcbi.1011912.ref033","doi-asserted-by":"crossref","first-page":"W486","DOI":"10.1093\/nar\/gky310","article-title":"MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis","volume":"46","author":"J Chong","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"15","key":"pcbi.1011912.ref034","doi-asserted-by":"crossref","first-page":"6212","DOI":"10.1021\/acs.analchem.2c05810","article-title":"Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data","volume":"95","author":"S Li","year":"2023","journal-title":"Analytical Chemistry"},{"issue":"1","key":"pcbi.1011912.ref035","doi-asserted-by":"crossref","DOI":"10.1074\/mcp.R110.000133","article-title":"mzML\u2014a community standard for mass spectrometry data","volume":"10","author":"L Martens","year":"2011","journal-title":"Molecular & Cellular Proteomics"},{"issue":"1","key":"pcbi.1011912.ref036","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1021\/acs.jproteome.9b00328","article-title":"ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion","volume":"19","author":"N Hulstaert","year":"2020","journal-title":"J Proteome Res"},{"key":"pcbi.1011912.ref037","first-page":"45","volume-title":"Machine Learning for Evolution Strategies","author":"Springer International Publishing","year":"2016"},{"key":"pcbi.1011912.ref038","author":"M Waskom","year":"2017","journal-title":"mwaskom\/seaborn"},{"issue":"3","key":"pcbi.1011912.ref039","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: fundamental algorithms for scientific computing in Python","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nature methods"},{"key":"pcbi.1011912.ref040","author":"TE Oliphant","year":"2006","journal-title":"Guide to numpy: Trelgol Publishing USA"},{"issue":"9","key":"pcbi.1011912.ref041","first-page":"1","article-title":"pandas: a foundational Python library for data analysis and statistics","volume":"14","author":"W. McKinney","year":"2011","journal-title":"Python for high performance and scientific computing"},{"issue":"3","key":"pcbi.1011912.ref042","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D Graphics Environment","volume":"9","author":"JD Hunter","year":"2007","journal-title":"Computing in Science & Engineering"},{"issue":"1","key":"pcbi.1011912.ref043","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1186\/s12859-023-05578-5","article-title":"pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods","volume":"24","author":"A Behdenna","year":"2023","journal-title":"BMC Bioinformatics"},{"issue":"15","key":"pcbi.1011912.ref044","doi-asserted-by":"crossref","first-page":"6212","DOI":"10.1021\/acs.analchem.2c05810","article-title":"Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data","volume":"95","author":"S Li","year":"2023","journal-title":"Anal Chem"},{"key":"pcbi.1011912.ref045","author":"NF de Jonge","year":"2023","journal-title":"Reproducible MS\/MS library cleaning pipeline in matchms"},{"issue":"52","key":"pcbi.1011912.ref046","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.21105\/joss.02411","article-title":"Spaaks. matchms \u2010 processing and similarity evaluation of mass spectrometry data","volume":"5","author":"SV Florian Huber","year":"2020","journal-title":"The Journal of Open Source Software"},{"key":"pcbi.1011912.ref047","unstructured":"AcquireX Intelligent Data Acquisition Technology for Orbitrap Tribrid mass spectrometers. 2020."},{"issue":"1","key":"pcbi.1011912.ref048","doi-asserted-by":"crossref","first-page":"4841","DOI":"10.1038\/s41598-024-55356-3","article-title":"An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics","volume":"14","author":"B Cooper","year":"2024","journal-title":"Scientific Reports"},{"key":"pcbi.1011912.ref049","unstructured":"MassBank of North America (MoNA) 2023 [Feb 8, 2024]. Available from: https:\/\/mona.fiehnlab.ucdavis.edu\/."},{"key":"pcbi.1011912.ref050","unstructured":"Slabon J. FPDF. v1.86 ed: github; 2023. p. FPDF is a PHP class which allows to generate PDF files with pure PHP. F from FPDF stands for Free: you may use it for any kind of usage and modify it to suit your needs."},{"issue":"4","key":"pcbi.1011912.ref051","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/nbt.3820","article-title":"Nextflow enables reproducible computational workflows","volume":"35","author":"P Di Tommaso","year":"2017","journal-title":"Nature Biotechnology"},{"issue":"1","key":"pcbi.1011912.ref052","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-020-00477-w","article-title":"patRoon: open source software platform for environmental mass spectrometry based non-target screening","volume":"13","author":"R Helmus","year":"2021","journal-title":"Journal of Cheminformatics"},{"issue":"1","key":"pcbi.1011912.ref053","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s13321-022-00586-8","article-title":"Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features","volume":"14","author":"M Yu","year":"2022","journal-title":"Journal of Cheminformatics"},{"issue":"3","key":"pcbi.1011912.ref054","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1021\/acs.jproteome.1c00392","article-title":"Omics Untargeted Key Script: R-Based Software Toolbox for Untargeted Metabolomics with Bladder Cancer Biomarkers Discovery Case Study","volume":"21","author":"IV Plyushchenko","year":"2022","journal-title":"J Proteome Res"},{"issue":"9","key":"pcbi.1011912.ref055","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1093\/bioinformatics\/btu813","article-title":"Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics","volume":"31","author":"F Giacomoni","year":"2015","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1011912.ref056","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1186\/s12859-020-03786-x","article-title":"IP4M: an integrated platform for mass spectrometry-based metabolomics data mining","volume":"21","author":"D Liang","year":"2020","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pcbi.1011912.ref057","doi-asserted-by":"crossref","first-page":"4653","DOI":"10.1038\/s41467-023-40333-7","article-title":"Simultaneously discovering the fate and biochemical effects of pharmaceuticals through untargeted metabolomics","volume":"14","author":"TJ Bowen","year":"2023","journal-title":"Nat Commun"},{"issue":"6","key":"pcbi.1011912.ref058","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1200\/JCO.22.01503","article-title":"Five-Year Survival Outcomes With Nivolumab Plus Ipilimumab Versus Chemotherapy as First-Line Treatment for Metastatic Non-Small-Cell Lung Cancer in CheckMate 227","volume":"41","author":"JR Brahmer","year":"2023","journal-title":"J Clin Oncol"},{"issue":"3","key":"pcbi.1011912.ref059","doi-asserted-by":"crossref","first-page":"e0033821","DOI":"10.1128\/spectrum.00338-21","article-title":"Amino Acid Metabolism is Significantly Altered at the Time of Admission in Hospital for Severe COVID-19 Patients: Findings from Longitudinal Targeted Metabolomics Analysis","volume":"9","author":"L Ansone","year":"2021","journal-title":"Microbiol Spectr"},{"key":"pcbi.1011912.ref060","unstructured":"Halbert CL, Tretyakov K. intervaltree. v3.1.0 ed: github; 2023. p. A mutable, self-balancing interval tree for Python 2 and 3. Queries may be by point, by range overlap, or by range envelopment."},{"issue":"1","key":"pcbi.1011912.ref061","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Y Benjamini","year":"1995","journal-title":"Journal of the Royal statistical society: series B (Methodological)"},{"issue":"1","key":"pcbi.1011912.ref062","first-page":"1","article-title":"Tukey\u2019s honestly significant difference (HSD) test","volume":"3","author":"H Abdi","year":"2010","journal-title":"Encyclopedia of research design"},{"issue":"1","key":"pcbi.1011912.ref063","doi-asserted-by":"crossref","first-page":"4040","DOI":"10.1038\/s41598-022-07944-4","article-title":"Reduced levels of pulmonary surfactant in COVID-19 ARDS","volume":"12","author":"P Schousboe","year":"2022","journal-title":"Sci Rep"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1011912","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T00:00:00Z","timestamp":1718668800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011912","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T13:37:31Z","timestamp":1718717851000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1011912"}},"subtitle":[],"editor":[{"given":"Sunil","family":"Laxman","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,6,6]]},"references-count":63,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6,6]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1011912","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.02.13.580048","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,6]]}}}