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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-s5-s16 |
| Pubmed ID | |
| Authors |
Xi Wang, Murray J Cairns |
| Abstract |
RNA-Seq has become a key technology in transcriptome studies because it can quantify overall expression levels and the degree of alternative splicing for each gene simultaneously. To interpret high-throughout transcriptome profiling data, functional enrichment analysis is critical. However, existing functional analysis methods can only account for differential expression, leaving differential splicing out altogether. |
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 | 5 | 45% |
| France | 2 | 18% |
| Australia | 1 | 9% |
| Switzerland | 1 | 9% |
| Germany | 1 | 9% |
| Unknown | 1 | 9% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Scientists | 5 | 45% |
Mendeley readers
The data shown below were compiled from readership statistics for 184 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| Colombia | 1 | <1% |
| Germany | 1 | <1% |
| Australia | 1 | <1% |
| South Africa | 1 | <1% |
| Israel | 1 | <1% |
| Hong Kong | 1 | <1% |
| Egypt | 1 | <1% |
| United Kingdom | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 166 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 52 | 28% |
| Student > Ph. D. Student | 51 | 28% |
| Student > Master | 20 | 11% |
| Student > Bachelor | 12 | 7% |
| Other | 7 | 4% |
| Other | 25 | 14% |
| Unknown | 17 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 94 | 51% |
| Biochemistry, Genetics and Molecular Biology | 26 | 14% |
| Computer Science | 14 | 8% |
| Medicine and Dentistry | 9 | 5% |
| Mathematics | 4 | 2% |
| Other | 13 | 7% |
| Unknown | 24 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 10 January 2014.
All research outputs
#4,607,728
of 22,835,198 outputs
Outputs from BMC Bioinformatics
#1,756
of 7,288 outputs
Outputs of similar age
#39,690
of 199,763 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 135 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,288 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 75% 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 199,763 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 80% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.