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16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model

View ORCID ProfileRuibang Luo, View ORCID ProfileMichael C. Schatz, View ORCID ProfileSteven L. Salzberg
doi: https://doi.org/10.1101/111393
Ruibang Luo
1Department of Computer Science, Johns Hopkins University,
2Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine,
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Michael C. Schatz
1Department of Computer Science, Johns Hopkins University,
2Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine,
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Steven L. Salzberg
1Department of Computer Science, Johns Hopkins University,
2Center for Computational Biology, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine,
3Departments of Biomedical Engineering and Biostatistics, Johns Hopkins University
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Abstract

Summary 16GT is a variant caller for Illumina WGS and WES germline data. It uses a new 16-genotype probabilistic model to unify SNP and indel calling in a single variant calling algorithm. In benchmark comparisons with five other widely used variant callers on a modern 36-core server, 16GT ran faster and demonstrated improved sensitivity in calling SNPs, and it provided comparable sensitivity and accuracy in calling indels as compared to the GATK HaplotypeCaller.

Availability and implementation https://github.com/aquaskyline/16GT

Contact rluo5{at}jhu.edu

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-ND 4.0 International license.
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Posted February 24, 2017.
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16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model
Ruibang Luo, Michael C. Schatz, Steven L. Salzberg
bioRxiv 111393; doi: https://doi.org/10.1101/111393
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16GT: a fast and sensitive variant caller using a 16-genotype probabilistic model
Ruibang Luo, Michael C. Schatz, Steven L. Salzberg
bioRxiv 111393; doi: https://doi.org/10.1101/111393

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