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X Demographics
Mendeley readers
Attention Score in Context
| Title |
RNA-Seq for gene identification and transcript profiling of three Stevia rebaudiana genotypes
|
|---|---|
| Published in |
BMC Genomics, July 2014
|
| DOI | 10.1186/1471-2164-15-571 |
| Pubmed ID | |
| Authors |
Junwen Chen, Kai Hou, Peng Qin, Hongchang Liu, Bin Yi, Wenting Yang, Wei Wu |
| Abstract |
Stevia (Stevia rebaudiana) is an important medicinal plant that yields diterpenoid steviol glycosides (SGs). SGs are currently used in the preparation of medicines, food products and neutraceuticals because of its sweetening property (zero calories and about 300 times sweeter than sugar). Recently, some progress has been made in understanding the biosynthesis of SGs in Stevia, but little is known about the molecular mechanisms underlying this process. Additionally, the genomics of Stevia, a non-model species, remains uncharacterized. The recent advent of RNA-Seq, a next generation sequencing technology, provides an opportunity to expand the identification of Stevia genes through in-depth transcript profiling. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 1 | <1% |
| Malaysia | 1 | <1% |
| India | 1 | <1% |
| China | 1 | <1% |
| Serbia | 1 | <1% |
| Unknown | 112 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 25% |
| Researcher | 29 | 25% |
| Student > Master | 14 | 12% |
| Student > Doctoral Student | 10 | 9% |
| Student > Postgraduate | 9 | 8% |
| Other | 14 | 12% |
| Unknown | 12 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 65 | 56% |
| Biochemistry, Genetics and Molecular Biology | 28 | 24% |
| Medicine and Dentistry | 3 | 3% |
| Engineering | 2 | 2% |
| Chemistry | 2 | 2% |
| Other | 5 | 4% |
| Unknown | 12 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 09 July 2014.
All research outputs
#20,232,430
of 22,758,248 outputs
Outputs from BMC Genomics
#9,263
of 10,637 outputs
Outputs of similar age
#190,317
of 225,737 outputs
Outputs of similar age from BMC Genomics
#156
of 192 outputs
Altmetric has tracked 22,758,248 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,637 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 225,737 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 192 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.