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X Demographics
Mendeley readers
Attention Score in Context
| Title |
FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-65 |
| Pubmed ID | |
| Authors |
Bernard J Pope, Tú Nguyen-Dumont, Fabrice Odefrey, Fleur Hammet, Russell Bell, Kayoko Tao, Sean V Tavtigian, David E Goldgar, Andrew Lonie, Melissa C Southey, Daniel J Park |
| Abstract |
Characterising genetic diversity through the analysis of massively parallel sequencing (MPS) data offers enormous potential to significantly improve our understanding of the genetic basis for observed phenotypes, including predisposition to and progression of complex human disease. Great challenges remain in resolving genetic variants that are genuine from the millions of artefactual signals. |
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% |
| Malaysia | 1 | 20% |
| Montenegro | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| Sweden | 1 | 2% |
| Italy | 1 | 2% |
| Japan | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 42 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 38% |
| Student > Ph. D. Student | 7 | 14% |
| Professor > Associate Professor | 5 | 10% |
| Student > Master | 5 | 10% |
| Other | 4 | 8% |
| Other | 7 | 14% |
| Unknown | 3 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 23 | 46% |
| Biochemistry, Genetics and Molecular Biology | 9 | 18% |
| Medicine and Dentistry | 4 | 8% |
| Computer Science | 4 | 8% |
| Mathematics | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 5 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 05 March 2013.
All research outputs
#7,181,643
of 22,699,621 outputs
Outputs from BMC Bioinformatics
#2,857
of 7,254 outputs
Outputs of similar age
#61,480
of 193,194 outputs
Outputs of similar age from BMC Bioinformatics
#68
of 159 outputs
Altmetric has tracked 22,699,621 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
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 gotten more attention than average, scoring higher than 58% 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 193,194 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.