Optimized experimental workflow for tandem mass spectrometry molecular networking in metabolomics View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09

AUTHORS

Florent Olivon, Fanny Roussi, Marc Litaudon, David Touboul

ABSTRACT

New omics sciences generate massive amounts of data, requiring to be sorted, curated, and statistically analyzed by dedicated software. Data-dependent acquisition mode including inclusion and exclusion rules for tandem mass spectrometry is routinely used to perform such analyses. While acquisition parameters are well described for proteomics, no general rule is currently available to generate reliable metabolomic data for molecular networking analysis on the Global Natural Product Social Molecular Networking platform (GNPS). Following on from an exploration of key parameters influencing the quality of molecular networks, universal optimal acquisition conditions for metabolomic studies are suggested in the present paper. The benefit of data pre-clustering before initiating large datasets for GNPS analyses is also demonstrated. Moreover, an efficient workflow dedicated to Agilent Technologies instruments is described, making the dereplication process easier by unambiguously distinguishing isobaric isomers eluted at different retention times, annotating the molecular networks with chemical formulas, and giving access to semi-quantitative data. This specific workflow foreshadows future developments of the GNPS platform. More... »

PAGES

5767-5778

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-017-0523-3

DOI

http://dx.doi.org/10.1007/s00216-017-0523-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090931595

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28762069


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