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X Demographics
Mendeley readers
Attention Score in Context
| Title |
IUTA: a tool for effectively detecting differential isoform usage from RNA-Seq data
|
|---|---|
| Published in |
BMC Genomics, October 2014
|
| DOI | 10.1186/1471-2164-15-862 |
| Pubmed ID | |
| Authors |
Liang Niu, Weichun Huang, David M Umbach, Leping Li |
| Abstract |
Most genes in mammals generate several transcript isoforms that differ in stability and translational efficiency through alternative splicing. Such alternative splicing can be tissue- and developmental stage-specific, and such specificity is sometimes associated with disease. Thus, detecting differential isoform usage for a gene between tissues or cell lines/types (differences in the fraction of total expression of a gene represented by the expression of each of its isoforms) is potentially important for cell and developmental biology. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 36% |
| India | 1 | 9% |
| France | 1 | 9% |
| Germany | 1 | 9% |
| Unknown | 4 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 6 | 55% |
| Members of the public | 4 | 36% |
| Practitioners (doctors, other healthcare professionals) | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| Canada | 1 | 1% |
| Brazil | 1 | 1% |
| Unknown | 65 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 30% |
| Researcher | 15 | 21% |
| Other | 9 | 13% |
| Student > Master | 7 | 10% |
| Student > Bachelor | 5 | 7% |
| Other | 10 | 14% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 29 | 41% |
| Biochemistry, Genetics and Molecular Biology | 27 | 38% |
| Computer Science | 5 | 7% |
| Medicine and Dentistry | 4 | 6% |
| Decision Sciences | 2 | 3% |
| Other | 0 | 0% |
| Unknown | 4 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 17 January 2017.
All research outputs
#2,200,369
of 22,765,347 outputs
Outputs from BMC Genomics
#645
of 10,638 outputs
Outputs of similar age
#26,378
of 254,545 outputs
Outputs of similar age from BMC Genomics
#11
of 209 outputs
Altmetric has tracked 22,765,347 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,638 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done particularly well, scoring higher than 93% 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 254,545 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 89% of its contemporaries.
We're also able to compare this research output to 209 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.