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MInfer: Bridging MetaboAnalyst and Jacobian analysis for metabolomic networks

J. Schwarzerova, EG. Mate, J. Idkowiak, D. Olesova, A. Kvasnicka, D. Dobesova, D. Friedecky, V. Provaznik, J. Skarda, W. Weckwerth, T. Nägele

. 2025 ; 263 (-) : 108672. [pub] 20250218

Jazyk angličtina Země Irsko

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc25009292

BACKGROUND AND OBJECTIVE: Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks. METHODS: MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways. RESULTS: The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations. CONCLUSIONS: MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.

Citace poskytuje Crossref.org

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$a Schwarzerova, Jana $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Czech Republic; Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria; Department of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, Ostrava, Czech Republic. Electronic address: Jana.Schwarzerova@vut.cz
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$a BACKGROUND AND OBJECTIVE: Metabolomic interaction networks provide critical insights into the dynamic relationships between metabolites and their regulatory mechanisms. This study introduces MInfer, a novel computational framework that integrates outputs from MetaboAnalyst, a widely used metabolomic analysis tool, with Jacobian analysis to enhance the derivation and interpretation of these networks. METHODS: MInfer combines the comprehensive data processing capabilities of MetaboAnalyst with the mathematical modeling power of Jacobian analysis. This framework was applied to various metabolomic datasets, employing advanced statistical tests to construct interaction networks and identify key metabolic pathways. RESULTS: The application of MInfer revealed significant metabolic pathways and potential regulatory mechanisms across multiple datasets. The framework demonstrated high precision, sensitivity, and specificity in identifying interactions, enabling robust network interpretations. CONCLUSIONS: MInfer enhances the interpretation of metabolomic data by providing detailed interaction networks and uncovering key regulatory insights. This tool holds significant potential for advancing the study of complex biological systems.
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$a Mate, Erdő Gabor $u Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria
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$a Idkowiak, Jakub $u Department of Analytical Chemistry, Faculty of Chemical Technology, University of Pardubice, Czech Republic
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$a Olesova, Dominika $u Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Slovak Republic; Institute of Neuroimmunology, Slovak Academy of Sciences, Slovak Republic
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$a Kvasnicka, Ales $u Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
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$a Dobesova, Dana $u Department of Clinical Biochemistry, Palacký University Olomouc and University Hospital Olomouc, Czech Republic
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$a Friedecky, David $u Department of Clinical Biochemistry, Palacký University Olomouc and University Hospital Olomouc, Czech Republic
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$a Provaznik, Valentyna $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Czech Republic
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$a Skarda, Jozef $u Department of Molecular and Clinical Pathology and Medical Genetics, University Hospital Ostrava, Ostrava, Czech Republic
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$a Weckwerth, Wolfram $u Molecular Systems Biology (MOSYS), University of Vienna, Vienna, Austria; Vienna Metabolomics Center (VIME), University of Vienna, Vienna, Austria. Electronic address: wolfram.weckwerth@univie.ac.at
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$a Nägele, Thomas $u Faculty of Biology, Plant Evolutionary Cell Biology, Ludwig-Maximilians-Universität München, Germany. Electronic address: thomas.naegele@biologie.uni-muenchen.de
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