Author, as appears in the article.: de Cripan, Sara M.; Cereto-Massague, Adria; Herrero, Pol; Barcaru, Andrei; Canela, Nuria; Domingo-Almenara, Xavier;
Department: Bioquímica i Biotecnologia
URV's Author/s: Cereto Massagué, Adrián José / Domingo Almenara, Xavier / HERRERO GIL, POL
Keywords: Tool Similarity Retention time Retention index Random forest Metabolomics Mass-spectrometry Machine-learning Indexes Identification Gc-ms Accuracy
Abstract: In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.
Thematic Areas: Pharmacology & pharmacy Medicine, research & experimental Medicine (miscellaneous) General biochemistry,genetics and molecular biology Ciencias sociales Biochemistry, genetics and molecular biology (miscellaneous) Biochemistry, genetics and molecular biology (all) Biochemistry & molecular biology
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: xavier.domingo@urv.cat adrianjose.cereto@urv.cat
Record's date: 2024-10-26
Papper version: info:eu-repo/semantics/publishedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Biomedicines. 10 (4):
APA: de Cripan, Sara M.; Cereto-Massague, Adria; Herrero, Pol; Barcaru, Andrei; Canela, Nuria; Domingo-Almenara, Xavier; (2022). Machine Learning-Based Retention Time Prediction of Trimethylsilyl Derivatives of Metabolites. Biomedicines, 10(4), -. DOI: 10.3390/biomedicines10040879
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications
Repositori URV