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Mendeley readers
Attention Score in Context
| Title |
Identification of genes associated with nitrogen-use efficiency by genome-wide transcriptional analysis of two soybean genotypes
|
|---|---|
| Published in |
BMC Genomics, October 2011
|
| DOI | 10.1186/1471-2164-12-525 |
| Pubmed ID | |
| Authors |
Qing N Hao, Xin A Zhou, Ai H Sha, Cheng Wang, Rong Zhou, Shui L Chen |
| Abstract |
Soybean is a valuable crop that provides protein and oil. Soybean requires a large amount of nitrogen (N) to accumulate high levels of N in the seed. The yield and protein content of soybean seeds are directly affected by the N-use efficiency (NUE) of the plant, and improvements in NUE will improve yields and quality of soybean products. Genetic engineering is one of the approaches to improve NUE, but at present, it is hampered by the lack of information on genes associated with NUE. Solexa sequencing is a new method for estimating gene expression in the transcription level. Here, the expression profiles were analyzed between two soybean varieties in N-limited conditions to identify genes related to NUE. |
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 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 2% |
| India | 1 | <1% |
| France | 1 | <1% |
| Unknown | 117 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 37 | 31% |
| Researcher | 21 | 17% |
| Student > Master | 10 | 8% |
| Student > Doctoral Student | 9 | 7% |
| Student > Bachelor | 6 | 5% |
| Other | 14 | 12% |
| Unknown | 24 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 66 | 55% |
| Biochemistry, Genetics and Molecular Biology | 14 | 12% |
| Computer Science | 3 | 2% |
| Unspecified | 2 | 2% |
| Engineering | 2 | 2% |
| Other | 5 | 4% |
| Unknown | 29 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 October 2014.
All research outputs
#6,375,523
of 22,655,397 outputs
Outputs from BMC Genomics
#2,862
of 10,607 outputs
Outputs of similar age
#38,921
of 140,468 outputs
Outputs of similar age from BMC Genomics
#23
of 97 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 10,607 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 71% 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 140,468 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.