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Mendeley readers
Attention Score in Context
| Title |
A simple consensus approach improves somatic mutation prediction accuracy
|
|---|---|
| Published in |
Genome Medicine, September 2013
|
| DOI | 10.1186/gm494 |
| Pubmed ID | |
| Authors |
David L Goode, Sally M Hunter, Maria A Doyle, Tao Ma, Simone M Rowley, David Choong, Georgina L Ryland, Ian G Campbell |
| Abstract |
Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations. |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 7% |
| United Kingdom | 3 | 4% |
| Netherlands | 1 | 1% |
| Belgium | 1 | 1% |
| Germany | 1 | 1% |
| Unknown | 58 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 28 | 41% |
| Student > Ph. D. Student | 12 | 17% |
| Other | 8 | 12% |
| Student > Master | 5 | 7% |
| Professor > Associate Professor | 4 | 6% |
| Other | 6 | 9% |
| Unknown | 6 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 38% |
| Biochemistry, Genetics and Molecular Biology | 22 | 32% |
| Mathematics | 3 | 4% |
| Medicine and Dentistry | 3 | 4% |
| Immunology and Microbiology | 2 | 3% |
| Other | 5 | 7% |
| Unknown | 8 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 15 June 2022.
All research outputs
#5,446,629
of 25,374,647 outputs
Outputs from Genome Medicine
#1,027
of 1,585 outputs
Outputs of similar age
#46,776
of 218,630 outputs
Outputs of similar age from Genome Medicine
#14
of 21 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. 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 218,630 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 76% of its contemporaries.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.