Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics

Analytical Chemistry, Journal Year: 2019, Volume and Issue: 91(5), P. 3246 - 3253

Published: Jan. 25, 2019

Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing strategies rely on the observation adducts to determine masses. However, a significant fraction features usually detected experiments remains unannotated, which limits our ability Despite availability tools annotate, relatively few them benefit from inherent presence in-source fragments liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce strategy annotate data using low-energy tandem mass spectrometry (MS) spectra METLIN library. Our algorithm, MISA (METLIN-guided annotation), compares against MS/MS spectra, enabling robust identification metabolic based spectral matching. The algorithm was evaluated through an analysis total 140 across three different sets biological samples analyzed chromatography-mass Results showed that, cases where were not formed or detected, able uncover by fragment also provide identities via two scores. These scores take into account number matched relative intensity similarity between experimental reference spectra. Overall, results that fragmentation is highly frequent phenomena should be considered for comprehensive feature annotation. Thus, combined adduct annotation, adds complementary layer, annotated increasing confidence. integrated XCMS Online platform freely available http://xcmsonline.scripps.edu.

Language: Английский

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Advances in computational metabolomics and databases deepen the understanding of metabolisms

Current Opinion in Biotechnology, Journal Year: 2018, Volume and Issue: 54, P. 10 - 17

Published: Feb. 5, 2018

Language: Английский

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Single-cell metabolomics by mass spectrometry: Advances, challenges, and future applications

TrAC Trends in Analytical Chemistry, Journal Year: 2019, Volume and Issue: 120, P. 115436 - 115436

Published: March 12, 2019

Language: Английский

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The metaRbolomics Toolbox in Bioconductor and beyond

Metabolites, Journal Year: 2019, Volume and Issue: 9(10), P. 200 - 200

Published: Sept. 23, 2019

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. studies involve sophisticated analytical techniques such as mass spectrometry nuclear magnetic resonance spectroscopy, generate large amounts high-dimensional experimental data. Open source processing analysis tools are major interest light innovative, open reproducible science. The scientific community has developed wide range software, providing freely available advanced approaches. programming statistics environment R emerged one most popular environments process analyse datasets. A benefit an is possibility connecting different into more workflows. Combining reusable data scripts with thus allows for open, research. This review provides extensive overview existing packages steps typical computational metabolomics workflow, including processing, biostatistics, metabolite annotation identification, biochemical network pathway analysis. Multifunctional workflows, possible user interfaces integration workflow management systems also reviewed. In total, this summarises than two hundred specific primarily on CRAN, Bioconductor GitHub.

Language: Английский

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Metabolomics and complementary techniques to investigate the plant phytochemical cosmos

Natural Product Reports, Journal Year: 2021, Volume and Issue: 38(10), P. 1729 - 1759

Published: Jan. 1, 2021

Plants and their associated microbial communities are known to produce millions of metabolites, a majority which still not characterized will be illuminated by the advance metabolomics informatics techniques.

Language: Английский

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