Automated analysis of large-scale NMR data generates metabolomic signatures and links them to candidate metabolites
| Autor: | Rico Rueedi, Mirjam Mattei, Reyhan Sonmez, Sven Bergmann, Roger Mallol Parera, Bita Khalili, Daniel Krefl, Mattia Tomasoni |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Databases Factual Computer science Metabolite Scale (descriptive set theory) 01 natural sciences Biochemistry Digital Signature Algorithm 03 medical and health sciences chemistry.chemical_compound Metabolomics Humans Nuclear Magnetic Resonance Biomolecular 030304 developmental biology 0303 health sciences Principal Component Analysis 1D NMR automated analysis ISA NMR spectroscopy STOCSY metabolite identification modular analysis pseudoquantification untargeted metabolomics 030102 biochemistry & molecular biology 010401 analytical chemistry Proteins General Chemistry Nuclear magnetic resonance spectroscopy Nmr data 0104 chemical sciences Body Fluids NMR spectra database 030104 developmental biology chemistry Principal component analysis Metabolome Proton NMR Biological system Algorithms |
| Zdroj: | Journal of proteome research, vol. 18, no. 9, pp. 3360-3368 |
| DOI: | 10.1101/613935 |
| Popis: | Identification of metabolites in large-scale 1H NMR data from human biofluids remains challenging due to the complexity of the spectra and their sensitivity to pH and ionic concentrations. In this work, we test the capacity of three analysis tools to extract metabolite signatures from 968 NMR profiles of human urine samples. Specifically, we studied sets of co-varying features derived from Principal Component Analysis (PCA), the Iterative Signature Algorithm (ISA) and Averaged Correlation Profiles (ACP), a new method we devised inspired by the STOCSY approach. We used our previously developed metabomatching method to match the sets generated by these algorithms to NMR spectra of individual metabolites available in public databases. Based on the number and quality of the matches we concluded that both ISA and ACP can robustly identify about a dozen metabolites, half of which were shared, while PCA did not produce any signatures with robust matches. |
| Databáze: | OpenAIRE |
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