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X Demographics
Mendeley readers
Attention Score in Context
| Title |
IAOseq: inferring abundance of overlapping genes using RNA-seq data
|
|---|---|
| Published in |
BMC Bioinformatics, January 2015
|
| DOI | 10.1186/1471-2105-16-s1-s3 |
| Pubmed ID | |
| Authors |
Hong Sun, Shuang Yang, Liangliang Tun, Yixue Li |
| Abstract |
Overlapping transcription constitutes a common mechanism for regulating gene expression. A major limitation of the overlapping transcription assays is the lack of high throughput expression data. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 17% |
| France | 1 | 17% |
| India | 1 | 17% |
| Netherlands | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 1 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 67% |
| Members of the public | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 4% |
| Sweden | 1 | 4% |
| Unknown | 23 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 24% |
| Researcher | 5 | 20% |
| Student > Bachelor | 3 | 12% |
| Professor > Associate Professor | 3 | 12% |
| Other | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 3 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 52% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Computer Science | 3 | 12% |
| Chemistry | 2 | 8% |
| Physics and Astronomy | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 3 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 27 February 2015.
All research outputs
#3,181,710
of 22,792,160 outputs
Outputs from BMC Bioinformatics
#1,163
of 7,280 outputs
Outputs of similar age
#48,151
of 351,785 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 146 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,280 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 83% 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 351,785 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 86% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.