Title |
Vcfanno: fast, flexible annotation of genetic variants
|
---|---|
Published in |
Genome Biology, June 2016
|
DOI | 10.1186/s13059-016-0973-5 |
Pubmed ID | |
Authors |
Brent S. Pedersen, Ryan M. Layer, Aaron R. Quinlan |
Abstract |
The integration of genome annotations is critical to the identification of genetic variants that are relevant to studies of disease or other traits. However, comprehensive variant annotation with diverse file formats is difficult with existing methods. Here we describe vcfanno, which flexibly extracts and summarizes attributes from multiple annotation files and integrates the annotations within the INFO column of the original VCF file. By leveraging a parallel "chromosome sweeping" algorithm, we demonstrate substantial performance gains by annotating ~85,000 variants per second with 50 attributes from 17 commonly used genome annotation resources. Vcfanno is available at https://github.com/brentp/vcfanno under the MIT license. |
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Country | Count | As % |
---|---|---|
United States | 12 | 48% |
Germany | 2 | 8% |
Spain | 1 | 4% |
Vietnam | 1 | 4% |
Thailand | 1 | 4% |
United Kingdom | 1 | 4% |
Netherlands | 1 | 4% |
Chile | 1 | 4% |
Taiwan | 1 | 4% |
Other | 0 | 0% |
Unknown | 4 | 16% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 16 | 64% |
Members of the public | 9 | 36% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Netherlands | 1 | <1% |
France | 1 | <1% |
Sweden | 1 | <1% |
United Kingdom | 1 | <1% |
Canada | 1 | <1% |
Egypt | 1 | <1% |
Japan | 1 | <1% |
United States | 1 | <1% |
Unknown | 167 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 59 | 34% |
Student > Ph. D. Student | 32 | 18% |
Student > Master | 19 | 11% |
Student > Bachelor | 9 | 5% |
Student > Doctoral Student | 6 | 3% |
Other | 20 | 11% |
Unknown | 30 | 17% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 62 | 35% |
Agricultural and Biological Sciences | 52 | 30% |
Computer Science | 10 | 6% |
Medicine and Dentistry | 8 | 5% |
Neuroscience | 3 | 2% |
Other | 8 | 5% |
Unknown | 32 | 18% |