Metabolomics,Multiomics

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The WEIZMASS spectral library for high-confidence metabolite identification


ABSTRACT: Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue in metabolomics. WEIZMASS is a unique reference metabolite spectral library developed from high-resolution MS data acquired from a structurally diverse set of 3,540 plant metabolites. We also present MatchWeiz, a multi-module strategy using a probabilistic approach to match library and experimental data. This strategy allows efficient and high-confidence identification of dozens of metabolites in model and exotic plants, including metabolites not previously reported in plants or found in few plant species to date.

INSTRUMENT(S): Liquid Chromatography MS - Negative (LC-MS (Negative)), Liquid Chromatography MS - Positive (LC-MS (Positive))

SUBMITTER: NIR SHAHAF 

PROVIDER: MTBLS330 | MetaboLights | 2018-03-05

REPOSITORIES: MetaboLights

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The WEIZMASS spectral library for high-confidence metabolite identification.

Shahaf Nir N   Rogachev Ilana I   Heinig Uwe U   Meir Sagit S   Malitsky Sergey S   Battat Maor M   Wyner Hilary H   Zheng Shuning S   Wehrens Ron R   Aharoni Asaph A  

Nature communications 20160830


Annotation of metabolites is an essential, yet problematic, aspect of mass spectrometry (MS)-based metabolomics assays. The current repertoire of definitive annotations of metabolite spectra in public MS databases is limited and suffers from lack of chemical and taxonomic diversity. Furthermore, the heterogeneity of the data prevents the development of universally applicable metabolite annotation tools. Here we present a combined experimental and computational platform to advance this key issue  ...[more]

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