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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting genomic indel variants with exact breakpoints in single- and paired-end sequencing data using SplazerS
|
|---|---|
| Published in |
Bioinformatics, January 2012
|
| DOI | 10.1093/bioinformatics/bts019 |
| Pubmed ID | |
| Authors |
Anne-Katrin Emde, Marcel H. Schulz, David Weese, Ruping Sun, Martin Vingron, Vera M. Kalscheuer, Stefan A. Haas, Knut Reinert |
| Abstract |
The reliable detection of genomic variation in resequencing data is still a major challenge, especially for variants larger than a few base pairs. Sequencing reads crossing boundaries of structural variation carry the potential for their identification, but are difficult to map. |
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% |
| United States | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 10 | 8% |
| Netherlands | 2 | 2% |
| Germany | 1 | <1% |
| Australia | 1 | <1% |
| Sweden | 1 | <1% |
| United Kingdom | 1 | <1% |
| France | 1 | <1% |
| New Zealand | 1 | <1% |
| Canada | 1 | <1% |
| Other | 2 | 2% |
| Unknown | 110 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 42 | 32% |
| Student > Ph. D. Student | 39 | 30% |
| Student > Master | 10 | 8% |
| Professor > Associate Professor | 9 | 7% |
| Other | 8 | 6% |
| Other | 14 | 11% |
| Unknown | 9 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 64 | 49% |
| Computer Science | 23 | 18% |
| Biochemistry, Genetics and Molecular Biology | 14 | 11% |
| Engineering | 5 | 4% |
| Mathematics | 4 | 3% |
| Other | 9 | 7% |
| Unknown | 12 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 02 May 2014.
All research outputs
#8,534,976
of 25,373,627 outputs
Outputs from Bioinformatics
#6,956
of 12,808 outputs
Outputs of similar age
#73,383
of 248,981 outputs
Outputs of similar age from Bioinformatics
#66
of 138 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,808 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 34th percentile – i.e., 34% 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 248,981 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.