vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework [version 1; peer review: 1 approved, 2 approved with reservations]
Bruce Budowle,
Shakeel Jessa,
Jianye Ge,
Stephen D. Turner,
Matthew Scholz,
August E. Woerner,
Meng Huang,
V.P. Nagraj
Affiliations
Bruce Budowle
Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
Shakeel Jessa
Signature Science LLC., Austin, TX, 78759, USA
Jianye Ge
Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
Meng Huang
Center for Human Identification, Department of Microbiology, Immunology, and Genetics, University of North Texas Health Science Center, Fort Worth, TX, 76107, USA
Motivation: Genotyping error can impact downstream single nucleotide polymorphism (SNP)-based analyses. Simulating various modes and levels of error can help investigators better understand potential biases caused by miscalled genotypes. Methods: We have developed and validated vcferr, a tool to probabilistically simulate genotyping error and missingness in variant call format (VCF) files. We demonstrate how vcferr could be used to address a research question by introducing varying levels of error of different type into a sample in a simulated pedigree, and assessed how kinship analysis degrades as a function of the kind and type of error. Software availability: vcferr is available for installation via PyPi (https://pypi.org/project/vcferr/) or conda (https://anaconda.org/bioconda/vcferr). The software is released under the MIT license with source code available on GitHub (https://github.com/signaturescience/vcferr)
