vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework

V. P. Nagraj; Matthew Scholz; Shakeel Jessa; Jianye Ge; August E. Woerner; Meng Huang; Bruce Budowle; Stephen D. Turner

doi:10.12688/f1000research.122840.1

vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework

V. P. Nagraj, Matthew Scholz, Shakeel Jessa, Jianye Ge, August E. Woerner, Meng Huang, Bruce Budowle, Stephen D. Turner

Research output: Contribution to journal › Article › peer-review

Abstract

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)

Original language	English
Article number	775
Journal	F1000Research
Volume	11
DOIs	https://doi.org/10.12688/f1000research.122840.1
State	Published - 2022

Keywords

benchmarking
bioinformatics
genealogy
GWAS
kinship
python
simulation

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10.12688/f1000research.122840.1

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title = "vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework",

abstract = "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)",

keywords = "benchmarking, bioinformatics, genealogy, GWAS, kinship, python, simulation",

author = "Nagraj, {V. P.} and Matthew Scholz and Shakeel Jessa and Jianye Ge and Woerner, {August E.} and Meng Huang and Bruce Budowle and Turner, {Stephen D.}",

note = "Funding Information: This work was supported in part by award 2019-DU-BX-0046 (Dense DNA Data for Enhanced Missing Persons Identification) to B.B., awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice and by internal funds from the Center for Human Identification. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice. Publisher Copyright: Copyright: {\textcopyright} 2022 Nagraj VP et al.",

year = "2022",

doi = "10.12688/f1000research.122840.1",

language = "English",

volume = "11",

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AU - Nagraj, V. P.

AU - Scholz, Matthew

AU - Jessa, Shakeel

AU - Ge, Jianye

AU - Woerner, August E.

AU - Huang, Meng

AU - Budowle, Bruce

AU - Turner, Stephen D.

N1 - Funding Information: This work was supported in part by award 2019-DU-BX-0046 (Dense DNA Data for Enhanced Missing Persons Identification) to B.B., awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice and by internal funds from the Center for Human Identification. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the U.S. Department of Justice. Publisher Copyright: Copyright: © 2022 Nagraj VP et al.

PY - 2022

Y1 - 2022

N2 - 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)

AB - 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)

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vcferr: Development, validation, and application of a single nucleotide polymorphism genotyping error simulation framework

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