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Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis

Overview of attention for article published in Frontiers in Molecular Biosciences, September 2016
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Title
Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis
Published in
Frontiers in Molecular Biosciences, September 2016
DOI 10.3389/fmolb.2016.00059
Pubmed ID
Authors

Fausto Carnevale Neto, Alan C. Pilon, Denise M. Selegato, Rafael T. Freire, Haiwei Gu, Daniel Raftery, Norberto P. Lopes, Ian Castro-Gamboa

Abstract

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 15%
Researcher 10 13%
Student > Bachelor 8 10%
Student > Master 8 10%
Student > Doctoral Student 6 8%
Other 12 15%
Unknown 22 28%
Readers by discipline Count As %
Chemistry 23 29%
Agricultural and Biological Sciences 8 10%
Biochemistry, Genetics and Molecular Biology 6 8%
Pharmacology, Toxicology and Pharmaceutical Science 4 5%
Nursing and Health Professions 2 3%
Other 10 13%
Unknown 25 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 October 2016.
All research outputs
#14,273,624
of 22,890,496 outputs
Outputs from Frontiers in Molecular Biosciences
#1,129
of 3,814 outputs
Outputs of similar age
#184,291
of 322,482 outputs
Outputs of similar age from Frontiers in Molecular Biosciences
#8
of 32 outputs
Altmetric has tracked 22,890,496 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,814 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,482 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.