BAGS: an automated barcode, audit & grade system for DNA barcode reference libraries

Biodiversity studies greatly benefit from molecular tools, such as DNA
metabarcoding, which provides an effective identification tool in
biomonitoring and conservation programmes. The accuracy of species-level
assignment, and consequent taxonomic coverage, relies on comprehensive DNA
barcode reference libraries. The role of these libraries is to support
species identification, but accidental errors in the generation of the
barcodes may compromise their accuracy. Here we present an R-based
application, BAGS (Barcode, Audit & Grade System;
https://github.com/tadeu95/BAGS), that performs automated auditing and
annotation of cytochrome c oxidase subunit I (COI) sequences libraries,
for a given taxonomic group of animals, available in the Barcode of Life
Data System (BOLD). This is followed by implementing a qualitative ranking
system that assigns one of five grades (A to E) to each species in the
reference library, according to the attributes of the data and congruency
of species names with sequences clustered in Barcode Index Numbers (BINs).
Our goal is to allow researchers to obtain the most useful and reliable
data, highlighting and segregating records according to their congruency.
Different tests were performed to perceive its usefulness and limitations.
BAGS fulfils a significant gap in the current landscape of DNA barcoding
research tools by quickly screening reference libraries to gauge the
congruence status of data and facilitate the triage of ambiguous data for
posterior review. Thereby, BAGS has the potential to become a valuable
addition in forthcoming DNA metabarcoding studies, in the long term
contributing to globally improve the quality and reliability of the public
reference libraries.