Revision and annotation of DNA barcode records for marine invertebrates: report of the 8th iBOL conference hackathon

• Published in: Metabarcoding and Metagenomics Vol. 5; pp. 207 - 217
• Main Authors: Radulovici, Adriana E., Vieira, Pedro E., Duarte, Sofia, Teixeira, Marcos A. L., Borges, Luisa M. S., Deagle, Bruce E., Majaneva, Sanna, Redmond, Niamh, Schultz, Jessica A., Costa, Filipe O.
• Format: Journal Article
• Language: English
• Published: Sofia Pensoft Publishers 01.12.2021
• Subjects: Contamination; Crustacea; Crustaceans; Deoxyribonucleic acid; DNA; Invertebrates; Polychaeta; Reviews; Species; Taxonomic revision; Taxonomy
• ISSN: 2534-9708; 2534-9708
• DOI: 10.3897/mbmg.5.67862

The accuracy of specimen identification through DNA barcoding and metabarcoding relies on reference libraries containing records with reliable taxonomy and sequence quality. The considerable growth in barcode data requires stringent data curation, especially in taxonomically difficult groups such as marine invertebrates. A major effort in curating marine barcode data in the Barcode of Life Data Systems (BOLD) was undertaken during the 8 th International Barcode of Life Conference (Trondheim, Norway, 2019). Major taxonomic groups (crustaceans, echinoderms, molluscs, and polychaetes) were reviewed to identify those which had disagreement between Linnaean names and Barcode Index Numbers (BINs). The records with disagreement were annotated with four tags: a) MIS-ID (misidentified, mislabeled, or contaminated records), b) AMBIG (ambiguous records unresolved with the existing data), c) COMPLEX (species names occurring in multiple BINs), and d) SHARE (barcodes shared between species). A total of 83,712 specimen records corresponding to 7,576 species were reviewed and 39% of the species were tagged (7% MIS-ID, 17% AMBIG, 14% COMPLEX, and 1% SHARE). High percentages (>50%) of AMBIG tags were recorded in gastropods, whereas COMPLEX tags dominated in crustaceans and polychaetes. The high proportion of tagged species reflects either flaws in the barcoding workflow (e.g., misidentification, cross-contamination) or taxonomic difficulties (e.g., synonyms, undescribed species). Although data curation is essential for barcode applications, such manual attempts to examine large datasets are unsustainable and automated solutions are extremely desirable.