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X Demographics
Mendeley readers
Attention Score in Context
| Title |
PASTA: splice junction identification from RNA-Sequencing data
|
|---|---|
| Published in |
BMC Bioinformatics, April 2013
|
| DOI | 10.1186/1471-2105-14-116 |
| Pubmed ID | |
| Authors |
Shaojun Tang, Alberto Riva |
| Abstract |
Next generation transcriptome sequencing (RNA-Seq) is emerging as a powerful experimental tool for the study of alternative splicing and its regulation, but requires ad-hoc analysis methods and tools. PASTA (Patterned Alignments for Splicing and Transcriptome Analysis) is a splice junction detection algorithm specifically designed for RNA-Seq data, relying on a highly accurate alignment strategy and on a combination of heuristic and statistical methods to identify exon-intron junctions with high accuracy. |
X Demographics
The data shown below were collected from the profiles of 12 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 | 3 | 25% |
| France | 2 | 17% |
| Montenegro | 1 | 8% |
| China | 1 | 8% |
| United Kingdom | 1 | 8% |
| Germany | 1 | 8% |
| Spain | 1 | 8% |
| Unknown | 2 | 17% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 8 | 67% |
| Members of the public | 4 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 7% |
| France | 2 | 2% |
| Germany | 1 | 1% |
| Switzerland | 1 | 1% |
| Brazil | 1 | 1% |
| Portugal | 1 | 1% |
| Denmark | 1 | 1% |
| Sweden | 1 | 1% |
| Unknown | 83 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 32 | 33% |
| Student > Ph. D. Student | 20 | 20% |
| Student > Master | 11 | 11% |
| Professor > Associate Professor | 8 | 8% |
| Student > Doctoral Student | 7 | 7% |
| Other | 15 | 15% |
| Unknown | 5 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 58 | 59% |
| Biochemistry, Genetics and Molecular Biology | 15 | 15% |
| Computer Science | 9 | 9% |
| Chemistry | 4 | 4% |
| Engineering | 2 | 2% |
| Other | 3 | 3% |
| Unknown | 7 | 7% |
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 03 October 2019.
All research outputs
#3,920,667
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#1,500
of 7,254 outputs
Outputs of similar age
#33,867
of 199,687 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 141 outputs
Altmetric has tracked 22,703,044 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 7,254 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 199,687 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 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.