↓ Skip to main content

A simple consensus approach improves somatic mutation prediction accuracy

Overview of attention for article published in Genome Medicine, January 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)

Mentioned by

5 patents


31 Dimensions

Readers on

65 Mendeley
1 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
A simple consensus approach improves somatic mutation prediction accuracy
Published in
Genome Medicine, January 2013
DOI 10.1186/gm494
Pubmed ID

David L Goode, Sally M Hunter, Maria A Doyle, Tao Ma, Simone M Rowley, David Choong, Georgina L Ryland, Ian G Campbell


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 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 8%
United Kingdom 3 5%
Netherlands 1 2%
Belgium 1 2%
Germany 1 2%
Unknown 54 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 43%
Student > Ph. D. Student 12 18%
Other 8 12%
Student > Master 5 8%
Professor > Associate Professor 4 6%
Other 5 8%
Unknown 3 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 40%
Biochemistry, Genetics and Molecular Biology 22 34%
Mathematics 3 5%
Medicine and Dentistry 3 5%
Immunology and Microbiology 2 3%
Other 3 5%
Unknown 6 9%

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
of 22,005,197 outputs
Outputs from Genome Medicine
of 1,397 outputs
Outputs of similar age
of 185,279 outputs
Outputs of similar age from Genome Medicine
of 4 outputs
Altmetric has tracked 22,005,197 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,397 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.4. 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 185,279 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 77% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.