| Title |
Optimized experimental workflow for tandem mass spectrometry molecular networking in metabolomics
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, July 2017
|
| DOI | 10.1007/s00216-017-0523-3 |
| Pubmed ID | |
| 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. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 2 | 50% |
| Members of the public | 1 | 25% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 119 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 15% |
| Student > Master | 18 | 15% |
| Researcher | 12 | 10% |
| Student > Bachelor | 10 | 8% |
| Student > Doctoral Student | 7 | 6% |
| Other | 21 | 18% |
| Unknown | 33 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 42 | 35% |
| Agricultural and Biological Sciences | 10 | 8% |
| Biochemistry, Genetics and Molecular Biology | 9 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Unspecified | 4 | 3% |
| Other | 11 | 9% |
| Unknown | 37 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 15 July 2021.
All research outputs
#2,444,370
of 25,382,440 outputs
Outputs from Analytical & Bioanalytical Chemistry
#173
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Outputs of similar age
#44,941
of 326,986 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#2
of 171 outputs
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We're also able to compare this research output to 171 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.