loading page

SNPfiltR: an R package for interactive and reproducible SNP filtering
  • Devon DeRaad
Devon DeRaad
University of Kansas

Corresponding Author:[email protected]

Author Profile


Here I describe the novel R package SNPfiltR and demonstrate its functionalities as the backbone of a customizable, reproducible SNP filtering pipeline implemented exclusively via the widely adopted R programming language. SNPfiltR extends existing SNP filtering functionalities by automating the visualization of key parameters such as depth, quality, and missing data, then allowing users to set filters based on optimized thresholds, all within a single, cohesive working environment. All SNPfiltR functions require a vcfR object as input, which can be easily generated by reading a SNP dataset stored as a standard vcf file into an R working environment using the function read.vcfR() from the R package vcfR. Performance benchmarking reveals that for moderately sized SNP datasets (up to 50M genotypes with associated quality information), SNPfiltR performs filtering with comparable efficiency to current state of the art command-line-based programs. These benchmarking results indicate that for most reduced-representation genomic datasets, SNPfiltR is an ideal choice for investigating, visualizing, and filtering SNPs as part of a cohesive and easily documentable bioinformatic pipeline. The SNPfiltR package can be downloaded from CRAN with the command [install.packages(“SNPfiltR”)], and a development version is available from GitHub at: (github.com/DevonDeRaad/SNPfiltR). Additionally, thorough documentation for SNPfiltR, including multiple comprehensive vignettes, is available at the website: (devonderaad.github.io/SNPfiltR/).
10 Nov 2021Submitted to Molecular Ecology Resources
24 Nov 2021Submission Checks Completed
24 Nov 2021Assigned to Editor
17 Dec 2021Reviewer(s) Assigned
17 Feb 2022Review(s) Completed, Editorial Evaluation Pending
22 Feb 2022Editorial Decision: Revise Minor
05 Mar 2022Review(s) Completed, Editorial Evaluation Pending
05 Mar 20221st Revision Received
23 Mar 2022Editorial Decision: Accept
24 Apr 2022Published in Molecular Ecology Resources. 10.1111/1755-0998.13618