vcfr: a package to manipulate and visualize variant call format data in R

Abstract

Software to call single-nucleotide polymorphisms or related genetic variants has converged on the variant call format (VCF) as the output format of choice. This has created a need for tools to work with VCF files. While an increasing number of software exists to read VCF data, many only extract the genotypes without including the data associated with each genotype that describes its quality. We created the r package vcfr to address this issue. We developed a VCF file exploration tool implemented in the r language because r provides an interactive experience and an environment that is commonly used for genetic data analysis. Functions to read and write VCF files into r as well as functions to extract portions of the data and to plot summary statistics of the data are implemented. vcfr further provides the ability to visualize how various parameterizations of the data affect the results. Additional tools are included to integrate sequence (fasta) and annotation data (GFF) for visualization of genomic regions such as chromosomes. Conversion functions translate data from the vcfr data structure to formats used by other r genetics packages. Computationally intensive functions are implemented in C++ to improve performance. Use of these tools is intended to facilitate VCF data exploration, including intuitive methods for data quality control and easy export to other r packages for further analysis. vcfr thus provides essential, novel tools currently not available in r.

Distance, elevation and environment as drivers of diversity and divergence in bumble bees across latitude and altitude

“…We also performed principle components analysis in adegenet 2.1.0 (Jombart, ; Jombart & Ahmed, ) to visualize genomic similarity among individuals. Input files were prepared for r using vcfr (Knaus & Grünwald, ).…”

Capturing variation in Lens (Fabaceae): Development and utility of an exome capture array for lentil

“…We used VCFtools v0.1.14 (Danecek et al., 2011) for the filtering process with the following parameters: minimum read depth (min_DP): 3; maximum read depth (max_DP): 5000; and minimum Phred‐scaled quality score (min_QUAL): 20. We used the R package VcfR (Knaus and Grünwald, 2017) to visualize the distribution of the quality parameters. At the end of the filtering process, we kept 6,679,012 variants (38% of the initial set) to use in downstream analyses.…”

Genome‐wide markers reveal temporal instability of local population genetic structure in the cotton fleahopper, Pseudatomoscelis seriatus (Hemiptera: Miridae)

“…The open‐source software RStudio version 1.1.442 and its associated packages were used to carry out conventional population genetic analyses . R/vcfR version 1.7.0 was used to read the filtered vcf file and prepare objects for use with other packages . To first verify that any observed population genetic structure was not due to sequencing batch effects, we carried out an analysis of molecular variance (AMOVA) using the year of sequencing as a hierarchical subdivision of the data above collecting locality .…”