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Virmid: accurate detection of somatic mutations with sample impurity inference

Overview of attention for article published in Genome Biology (Online Edition), January 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)

Mentioned by

twitter
1 tweeter
patent
2 patents

Citations

dimensions_citation
48 Dimensions

Readers on

mendeley
113 Mendeley
citeulike
5 CiteULike
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Title
Virmid: accurate detection of somatic mutations with sample impurity inference
Published in
Genome Biology (Online Edition), January 2013
DOI 10.1186/gb-2013-14-8-r90
Pubmed ID
Authors

Sangwoo Kim, Kyowon Jeong, Kunal Bhutani, Jeong Lee, Anand Patel, Eric Scott, Hojung Nam, Hayan Lee, Joseph G Gleeson, Vineet Bafna

Abstract

Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 8 7%
Canada 1 <1%
Unknown 104 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 35 31%
Student > Ph. D. Student 25 22%
Student > Master 11 10%
Student > Bachelor 7 6%
Professor 6 5%
Other 22 19%
Unknown 7 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 36%
Biochemistry, Genetics and Molecular Biology 19 17%
Computer Science 17 15%
Medicine and Dentistry 9 8%
Engineering 7 6%
Other 11 10%
Unknown 9 8%

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 03 January 2019.
All research outputs
#5,231,148
of 17,351,915 outputs
Outputs from Genome Biology (Online Edition)
#2,642
of 3,593 outputs
Outputs of similar age
#50,775
of 174,826 outputs
Outputs of similar age from Genome Biology (Online Edition)
#2
of 2 outputs
Altmetric has tracked 17,351,915 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 3,593 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 25th percentile – i.e., 25% 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 174,826 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 69% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.