Title |
BAYSIC: a Bayesian method for combining sets of genome variants with improved specificity and sensitivity
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Published in |
BMC Bioinformatics, April 2014
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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. |
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Geographical breakdown
Country | Count | As % |
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United States | 4 | 29% |
Japan | 1 | 7% |
United Kingdom | 1 | 7% |
Norway | 1 | 7% |
Mexico | 1 | 7% |
Unknown | 6 | 43% |
Demographic breakdown
Type | Count | As % |
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Scientists | 7 | 50% |
Members of the public | 7 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United Kingdom | 3 | 2% |
United States | 3 | 2% |
Netherlands | 1 | <1% |
France | 1 | <1% |
Sweden | 1 | <1% |
Portugal | 1 | <1% |
Canada | 1 | <1% |
Turkey | 1 | <1% |
Spain | 1 | <1% |
Other | 1 | <1% |
Unknown | 113 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 43 | 34% |
Student > Ph. D. Student | 29 | 23% |
Student > Master | 10 | 8% |
Student > Bachelor | 7 | 6% |
Other | 6 | 5% |
Other | 18 | 14% |
Unknown | 14 | 11% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 52 | 41% |
Biochemistry, Genetics and Molecular Biology | 30 | 24% |
Computer Science | 12 | 9% |
Medicine and Dentistry | 4 | 3% |
Immunology and Microbiology | 3 | 2% |
Other | 11 | 9% |
Unknown | 15 | 12% |