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Twitter Demographics
Mendeley readers
Attention Score in Context
| Title |
miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data
|
|---|---|
| Published in |
BMC Bioinformatics, August 2014
|
| DOI | 10.1186/1471-2105-15-275 |
| Pubmed ID | |
| Authors |
Jiyuan An, John Lai, Atul Sajjanhar, Melanie L Lehman, Colleen C Nelson |
| Abstract |
Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep's probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction. |
Twitter Demographics
The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 1% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Norway | 1 | <1% |
| United Kingdom | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 131 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 20% |
| Student > Master | 25 | 18% |
| Researcher | 24 | 17% |
| Student > Bachelor | 13 | 9% |
| Student > Doctoral Student | 9 | 7% |
| Other | 28 | 20% |
| Unknown | 11 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 76 | 55% |
| Biochemistry, Genetics and Molecular Biology | 24 | 17% |
| Computer Science | 17 | 12% |
| Arts and Humanities | 1 | <1% |
| Medicine and Dentistry | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 18 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 12 September 2014.
All research outputs
#4,029,876
of 23,310,485 outputs
Outputs from BMC Bioinformatics
#1,502
of 7,382 outputs
Outputs of similar age
#39,672
of 232,412 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 117 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,382 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 79% 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 232,412 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.