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PyVar: An Extensible Framework for Variant Annotator Comparison

Julie Wertz, Qianli Liao, Thomas B Bair, Michael S Chimenti
doi: https://doi.org/10.1101/078386
Julie Wertz
Iowa Institute of Human Genetics, Bioinformatics Division; Carver College of Medicine, Iowa City, Iowa, 52242
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Qianli Liao
Iowa Institute of Human Genetics, Bioinformatics Division; Carver College of Medicine, Iowa City, Iowa, 52242
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Thomas B Bair
Iowa Institute of Human Genetics, Bioinformatics Division; Carver College of Medicine, Iowa City, Iowa, 52242
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Michael S Chimenti
Iowa Institute of Human Genetics, Bioinformatics Division; Carver College of Medicine, Iowa City, Iowa, 52242
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  • For correspondence: michael-chimenti@uiowa.edu
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Abstract

Modern genomics projects are generating millions of variant calls that must be annotated for predicted functional consequences at the level of gene expression and protein function. Many of these variants are of interest owing to their potential clinical significance. Unfortunately, state-of-the-art methods do not always agree on downstream effects for any given variant. Here we present a readily extensible python framework (PyVar) for comparing the output of variant annotator methods in order to aid the research community in quickly assessing differences between methods and benchmarking new methods as they are developed. We also apply our framework to assess the annotation performance of ANNOVAR, VEP, and SnpEff when annotating 81 million variants from the ‘1000 Genomes Project’ against both RefSeq and Ensembl human transcript sets.

Footnotes

  • Abbreviations
    bp
    base pair
    LoF
    loss of function
    UTR
    untranslated region
    SNP
    single- nucleotide polymorphism
    VEP
    Variant Effect Predictor

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted September 30, 2016.
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PyVar: An Extensible Framework for Variant Annotator Comparison
Julie Wertz, Qianli Liao, Thomas B Bair, Michael S Chimenti
bioRxiv 078386; doi: https://doi.org/10.1101/078386
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PyVar: An Extensible Framework for Variant Annotator Comparison
Julie Wertz, Qianli Liao, Thomas B Bair, Michael S Chimenti
bioRxiv 078386; doi: https://doi.org/10.1101/078386

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