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Mendeley readers
Attention Score in Context
| Title |
NMR Metabolomics Defining Genetic Variation in Pea Seed Metabolites
|
|---|---|
| Published in |
Frontiers in Plant Science, July 2018
|
| DOI | 10.3389/fpls.2018.01022 |
| Pubmed ID | |
| Authors |
Noel Ellis, Chie Hattori, Jitender Cheema, James Donarski, Adrian Charlton, Michael Dickinson, Giampaolo Venditti, Péter Kaló, Zoltán Szabó, György B. Kiss, Claire Domoney |
| Abstract |
Nuclear magnetic resonance (NMR) spectroscopy profiling was used to provide an unbiased assessment of changes to the metabolite composition of seeds and to define genetic variation for a range of pea seed metabolites. Mature seeds from recombinant inbred lines, derived from three mapping populations for which there is substantial genetic marker linkage information, were grown in two environments/years and analyzed by non-targeted NMR. Adaptive binning of the NMR metabolite data, followed by analysis of quantitative variation among lines for individual bins, identified the main genomic regions determining this metabolic variability and the variability for selected compounds was investigated. Analysis by t-tests identified a set of bins with highly significant associations to genetic map regions, based on probability (p) values that were appreciably lower than those determined for randomized data. The correlation between bins showing high mean absolute deviation and those showing low p-values for marker association provided an indication of the extent to which the genetics of bin variation might be explained by one or a few loci. Variation in compounds related to aromatic amino acids, branched-chain amino acids, sucrose-derived metabolites, secondary metabolites and some unidentified compounds was associated with one or more genetic loci. The combined analysis shows that there are multiple loci throughout the genome that together impact on the abundance of many compounds through a network of interactions, where individual loci may affect more than one compound and vice versa. This work therefore provides a framework for the genetic analysis of the seed metabolome, and the use of genetic marker data in the breeding and selection of seeds for specific seed quality traits and compounds that have high commercial value. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 44 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 30% |
| Student > Ph. D. Student | 8 | 18% |
| Student > Master | 4 | 9% |
| Student > Bachelor | 2 | 5% |
| Professor | 2 | 5% |
| Other | 7 | 16% |
| Unknown | 8 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 21 | 48% |
| Biochemistry, Genetics and Molecular Biology | 6 | 14% |
| Business, Management and Accounting | 1 | 2% |
| Environmental Science | 1 | 2% |
| Immunology and Microbiology | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 13 | 30% |
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 13 August 2018.
All research outputs
#15,012,809
of 23,094,276 outputs
Outputs from Frontiers in Plant Science
#9,434
of 20,707 outputs
Outputs of similar age
#179,452
of 296,623 outputs
Outputs of similar age from Frontiers in Plant Science
#255
of 482 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 20,707 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 296,623 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 482 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.