BarcodingR: an integrated r package for species identification using DNA barcodes

• Published in: Methods in ecology and evolution Vol. 8; no. 5; pp. 627 - 634
• Main Authors: Zhang, Ai‐bing, Hao, Meng‐di, Yang, Cai‐qing, Shi, Zhi‐yong, Yu, Douglas
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01.05.2017
• Subjects: Artificial intelligence; barcodes evaluation; Bayesian analysis; Biodiversity; BP‐based species identification; Computer programs; Deoxyribonucleic acid; DNA; DNA barcoding; DNA barcoding gaps; DNA taxonomy; Empirical analysis; Fuzzy sets; fuzzy‐set‐theory‐based approach; Gap analysis; Gene sequencing; Identification; Identification methods; Integrated software; Nucleotide sequence; Operating systems; species identification
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.12682

Summary Species identification via DNA barcodes has recently become an important and routine task in many biodiversity projects using DNA sequence data. Here, we present BarcodingR, an integrated software package that provides a comprehensive implementation of species identification methods, including artificial intelligence, fuzzy‐set, Bayesian and kmer‐based methods, that are not readily available in other packages. BarcodingR additionally provides new functions for barcode evaluation, barcoding gap analysis, delimitation comparison analysis, species membership analysis and consensus identification. Comparison with other barcoding methods using 11 empirical data sets indicates that on average, FZKMER (implemented in BarcodingR) and one extant barcoding method BRONX outperform all other methods examined in this study. Two other methods, BP and FZ (both implemented in BarcodingR), present similar performance as SVM and BLOG, respectively, and all display better performance than that of Jrip. The software of BarcodingR is open source under GNU General Public License and freely available for all major operating systems.