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BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity

Overview of attention for article published in BMC Bioinformatics, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

twitter
15 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
37 Dimensions

Readers on

mendeley
112 Mendeley
citeulike
3 CiteULike
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Title
BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-104
Pubmed ID
Authors

Brandi L Cantarel, Daniel Weaver, Nathan McNeill, Jianhua Zhang, Aaron J Mackey, Justin Reese

Abstract

Accurate genomic variant detection is an essential step in gleaning medically useful information from genome data. However, low concordance among variant-calling methods reduces confidence in the clinical validity of whole genome and exome sequence data, and confounds downstream analysis for applications in genome medicine.Here we describe BAYSIC (BAYeSian Integrated Caller), which combines SNP variant calls produced by different methods (e.g. GATK, FreeBayes, Atlas, SamTools, etc.) into a more accurate set of variant calls. BAYSIC differs from majority voting, consensus or other ad hoc intersection-based schemes for combining sets of genome variant calls. Unlike other classification methods, the underlying BAYSIC model does not require training using a "gold standard" of true positives. Rather, with each new dataset, BAYSIC performs an unsupervised, fully Bayesian latent class analysis to estimate false positive and false negative error rates for each input method. The user specifies a posterior probability threshold according to the user's tolerance for false positive and false negative errors; lowering the posterior probability threshold allows the user to trade specificity for sensitivity while raising the threshold increases specificity in exchange for sensitivity.

Twitter Demographics

The data shown below were collected from the profiles of 15 tweeters 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 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
United Kingdom 3 3%
Sweden 1 <1%
France 1 <1%
Netherlands 1 <1%
Turkey 1 <1%
Canada 1 <1%
New Zealand 1 <1%
Spain 1 <1%
Other 1 <1%
Unknown 98 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 43 38%
Student > Ph. D. Student 26 23%
Student > Master 9 8%
Other 6 5%
Professor 4 4%
Other 15 13%
Unknown 9 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 52 46%
Biochemistry, Genetics and Molecular Biology 25 22%
Computer Science 9 8%
Medicine and Dentistry 4 4%
Immunology and Microbiology 3 3%
Other 9 8%
Unknown 10 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 29 April 2019.
All research outputs
#1,809,600
of 14,747,541 outputs
Outputs from BMC Bioinformatics
#737
of 5,480 outputs
Outputs of similar age
#25,887
of 189,878 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 11 outputs
Altmetric has tracked 14,747,541 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,480 research outputs from this source. They receive a mean Attention Score of 5.0. This one has done well, scoring higher than 86% 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 189,878 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 86% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.