ArticlePhytochemistry letters2017
Using MS-FINDER for identifying 19 natural products in the CASMI 2016 contest.
Article in Phytochemistry letters, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 28 citations in OpenAlex.
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- Development of Cross-Reactive Antibodies for the Identification and Treatment of Synthetic Cannabinoid Receptor Agonist Toxicity.Vaccines · 2022Article
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- Sunflower Bark Extract as a Biostimulant Suppresses Reactive Oxygen Species in Salt-Stressed Arabidopsis.Frontiers in plant science · 2022Article
- Metabolic diversification of nitrogen-containing metabolites by the expression of a heterologous lysine decarboxylase gene in Arabidopsis.The Plant journal : for cell and molecular biology · 2019Article
- Serum Metabolomics Identifies Altered Bioenergetics, Signaling Cascades in Parallel with Exposome Markers in Crohn's Disease.Molecules (Basel, Switzerland) · 2019Article
- Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.Metabolites · 2018Review
- Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy.Journal of cheminformatics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 3 countries.
Funding
Abstract
In its fourth year, the CASMI 2016 contest was organized to evaluate current chemical structure identification strategies for 19 natural products using high-resolution LC-MS and LC-MS/MS challenge datasets using automated methods with or without the combination of other tools. These natural products originate from plants, fungi, marine sponges, algae, or micro-algae. Every compound annotation workflow must start with determination of elemental compositions. Of these 19 challenges, one was excluded by the organizers after submission. For the remaining 18 challenges, three software programs were used. MS-FINDER version 1.62 was able to correctly identify 89% of the molecular formulas using an internal database that comprised of 13 metabolomics repositories with 45,181 formulas. SIRIUS correctly identified 61% compositions using PubChem formulas and Seven Golden Rules correctly identified 83% by using the Dictionary of Natural Products as a targeted database. Next, we performed structural dereplication for which we used the consensus formula from the three software programs. We submitted two solution sets for these challenges. In the first solution set,
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Registered trials
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