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Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.
Pakkir Shah, Abzer K; Walter, Axel; Ottosson, Filip; Russo, Francesco; Navarro-Diaz, Marcelo; Boldt, Judith; Kalinski, Jarmo-Charles J; Kontou, Eftychia Eva; Elofson, James; Polyzois, Alexandros; González-Marín, Carolina; Farrell, Shane; Aggerbeck, Marie R; Pruksatrakul, Thapanee; Chan, Nathan; Wang, Yunshu; Pöchhacker, Magdalena; Brungs, Corinna; Cámara, Beatriz; Caraballo-Rodríguez, Andrés Mauricio; Cumsille, Andres; de Oliveira, Fernanda; Dührkop, Kai; El Abiead, Yasin; Geibel, Christian; Graves, Lana G; Hansen, Martin; Heuckeroth, Steffen; Knoblauch, Simon; Kostenko, Anastasiia; Kuijpers, Mirte C M; Mildau, Kevin; Papadopoulos Lambidis, Stilianos; Portal Gomes, Paulo Wender; Schramm, Tilman; Steuer-Lodd, Karoline; Stincone, Paolo; Tayyab, Sibgha; Vitale, Giovanni Andrea; Wagner, Berenike C; Xing, Shipei; Yazzie, Marquis T; Zuffa, Simone; de Kruijff, Martinus; Beemelmanns, Christine; Link, Hannes; Mayer, Christoph; van der Hooft, Justin J J; Damiani, Tito; Pluskal, Tomás.
  • Pakkir Shah AK; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Walter A; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
  • Ottosson F; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Russo F; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
  • Navarro-Diaz M; Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany.
  • Boldt J; Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark.
  • Kalinski JJ; Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen S, Denmark.
  • Kontou EE; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
  • Elofson J; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Polyzois A; Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
  • González-Marín C; German Center for Infection Research, Partner Site Braunschweig-Hannover, Braunschweig, Germany.
  • Farrell S; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Aggerbeck MR; Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa.
  • Pruksatrakul T; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Chan N; The Novo Nordisk Foundation for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
  • Wang Y; Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA.
  • Pöchhacker M; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Brungs C; Boyce Thompson Institute and Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA.
  • Cámara B; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Caraballo-Rodríguez AM; Universidad EAFIT, Medellín, Antioquia, Colombia.
  • Cumsille A; Bigelow Laboratory for Ocean Sciences, East Boothbay, ME, USA.
  • de Oliveira F; School of Marine Sciences, Darling Marine Center, University of Maine, Walpole, ME, USA.
  • Dührkop K; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • El Abiead Y; Department of Environmental Science, Aarhus University, Roskilde, Denmark.
  • Geibel C; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Graves LG; National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Thailand Science Park, Pathum Thani, Thailand.
  • Hansen M; Department of Computer Science, University of California Riverside, Riverside, CA, USA.
  • Heuckeroth S; Department of Computer Science, University of California Riverside, Riverside, CA, USA.
  • Knoblauch S; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • Kostenko A; Department of Food Chemistry and Toxicology, University of Vienna, Vienna, Austria.
  • Kuijpers MCM; Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Prague, Czech Republic.
  • Mildau K; Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile.
  • Papadopoulos Lambidis S; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
  • Portal Gomes PW; Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Centro de Biotecnología DAL, Universidad Técnica Federico Santa María, Valparaíso, Chile.
  • Schramm T; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
  • Steuer-Lodd K; Department of Biotechnology, Engineering School of Lorena, University of São Paulo, Lorena, São Paulo, Brazil.
  • Stincone P; Department of Bioinformatics, University of Jena, Jena, Germany.
  • Tayyab S; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego, CA, USA.
  • Vitale GA; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
  • Wagner BC; Department of Environmental Systems Analysis, University of Tübingen, Tübingen, Germany.
  • Xing S; Leibniz Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany.
  • Yazzie MT; Department of Environmental Science, Aarhus University, Roskilde, Denmark.
  • Zuffa S; Institute of Inorganic and Analytical Chemistry, University of Münster, Münster, Germany.
  • de Kruijff M; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
  • Beemelmanns C; Department of Chemistry and Biochemistry, University of Denver, Denver, CO, USA.
  • Link H; Department of Ecology, Behavior and Evolution, University of California San Diego, San Diego, CA, USA.
  • Mayer C; Virtual Multi-Omics Laboratory, The Internet, Riverside, CA, USA.
  • van der Hooft JJJ; Department of Analytical Chemistry, University of Vienna, Vienna, Austria.
  • Damiani T; Bioinformatics Group, Wageningen University and Research, Wageningen, the Netherlands.
  • Pluskal T; University of Tübingen, Interfaculty Institute of Microbiology and Infection Medicine, Tübingen, Germany.
Nat Protoc ; 2024 Sep 20.
Article en En | MEDLINE | ID: mdl-39304763
ABSTRACT
Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https//github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https//fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article
Texto completo
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PubMed Links
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Texto completo: 1 Banco de datos: MEDLINE Idioma: En Año: 2024 Tipo del documento: Article