| Title |
miREvo: an integrative microRNA evolutionary analysis platform for next-generation sequencing experiments
|
|---|---|
| Published in |
BMC Bioinformatics, June 2012
|
| DOI | 10.1186/1471-2105-13-140 |
| Pubmed ID | |
| Authors |
Ming Wen, Yang Shen, Suhua Shi, Tian Tang |
| Abstract |
MicroRNAs (miRNAs) are small (~19-24nt) non-coding RNAs that play important roles in various biological processes. To date, the next-generation sequencing (NGS) technology has been widely used to discover miRNAs in plants and animals. Although evolutionary analysis is important to reveal the functional dynamics of miRNAs, few computational tools have been developed to analyze the evolution of miRNA sequence and expression across species, especially the newly emerged ones, |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 20% |
| France | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 4 | 3% |
| Canada | 3 | 2% |
| United States | 2 | 1% |
| India | 2 | 1% |
| United Kingdom | 2 | 1% |
| Norway | 1 | <1% |
| France | 1 | <1% |
| Netherlands | 1 | <1% |
| Malaysia | 1 | <1% |
| Other | 7 | 5% |
| Unknown | 123 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 42 | 29% |
| Student > Ph. D. Student | 27 | 18% |
| Student > Master | 18 | 12% |
| Student > Bachelor | 11 | 7% |
| Professor > Associate Professor | 7 | 5% |
| Other | 21 | 14% |
| Unknown | 21 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 82 | 56% |
| Biochemistry, Genetics and Molecular Biology | 19 | 13% |
| Computer Science | 8 | 5% |
| Linguistics | 1 | <1% |
| Nursing and Health Professions | 1 | <1% |
| Other | 8 | 5% |
| Unknown | 28 | 19% |
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 26 June 2012.
All research outputs
#13,363,429
of 22,668,244 outputs
Outputs from BMC Bioinformatics
#4,188
of 7,247 outputs
Outputs of similar age
#90,860
of 164,033 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 103 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 164,033 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.