VLF: An R package for the analysis of very low frequency variants in DNA sequences

• Published in: Biodiversity Data Journal
• Main Authors: Phillips,Jarrett, Athey,Taryn, McNicholas,Paul, Hanner,Robert
• Format: Other/Unknown Material
• Language: English
• Published: Pensoft Publishers 2023
• Subjects: DNA barcoding; frequency matrix; genetic diversity; PCR error; sequencing error; trace file; Canada; Branta canadensis; Canada Goose
• Online Access: https://doi.org/10.3897/BDJ.11.e96480; https://bdj.pensoft.net/article/96480/

Here, we introduce VLF, an R package to determine the distribution of very low frequency variants (VLFs) in nucleotide and amino acid sequences for the analysis of errors in DNA sequence records. The package allows users to assess VLFs in aligned and trimmed protein-coding sequences by automatically calculating the frequency of nucleotides or amino acids in each sequence position and outputting those that occur under a user-specified frequency (default of p = 0.001). These results can then be used to explore fundamental population genetic and phylogeographic patterns, mechanisms and processes at the microevolutionary level, such as nucleotide and amino acid sequence conservation.Our package extends earlier work pertaining to an implementation of VLF analysis in Microsoft Excel, which was found to be both computationally slow and error prone. We compare those results to our own herein. Results between the two implementations are found to be highly consistent for a large DNA barcode dataset of bird species. Differences in results are readily explained by both manual human error and inadequate Linnean taxonomy (specifically, species synonymy). Here, VLF is also applied to a subset of avian barcodes to assess the extent of biological artifacts at the species level for Canada goose (Branta canadensis), as well as within a large dataset of DNA barcodes for fishes of forensic and regulatory importance. The novelty of VLF and its benefit over the previous implementation include its high level of automation, speed, scalability and ease-of-use, each desirable characteristics which will be extremely valuable as more sequence data are rapidly accumulated in popular reference databases, such as BOLD and GenBank.