title: A new method for species identification via protein-coding and non-coding DNA barcodes by combining machine learning with bioinformatic methods.
abstract: Species identification via DNA barcodes is contributing greatly to current bioinventory efforts. The initial, and widely accepted, proposal was to use the protein-coding cytochrome c oxidase subunit I (COI) region as the standard barcode for animals, but recently non-coding internal transcribed spacer (ITS) genes have been proposed as candidate barcodes for both animals and plants. However, achieving a robust alignment for non-coding regions can be problematic. Here we propose two new methods (DV-RBF and FJ-RBF) to address this issue for species assignment by both coding and non-coding sequences that take advantage of the power of machine learning and bioinformatics. We demonstrate the value of the new methods with four empirical datasets, two representing typical protein-coding COI barcode datasets (neotropical bats and marine fish) and two representing non-coding ITS barcodes (rust fungi and brown algae). Using two random sub-sampling approaches, we demonstrate that the new methods significantly outperformed existing Neighbor-joining (NJ) and Maximum likelihood (ML) methods for both coding and non-coding barcodes when there was complete species coverage in the reference dataset. The new methods also out-performed NJ and ML methods for non-coding sequences in circumstances of potentially incomplete species coverage, although then the NJ and ML methods performed slightly better than the new methods for protein-coding barcodes. A 100% success rate of species identification was achieved with the two new methods for 4,122 bat queries and 5,134 fish queries using COI barcodes, with 95% confidence intervals (CI) of 99.75-100%. The new methods also obtained a 96.29% success rate (95%CI: 91.62-98.40%) for 484 rust fungi queries and a 98.50% success rate (95%CI: 96.60-99.37%) for 1094 brown algae queries, both using ITS barcodes.
authors.affiliation: College of Life Sciences, Capital Normal University, Beijing, People's Republic of China. zhangab2008@mail.cnu.edu.cn
authors.forename: Ai-bing
authors.initials: AB
authors.lastname: Zhang
authors.forename: Jie
authors.initials: J
authors.lastname: Feng
authors.forename: Robert D
authors.initials: RD
authors.lastname: Ward
authors.forename: Ping
authors.initials: P
authors.lastname: Wan
authors.forename: Qiang
authors.initials: Q
authors.lastname: Gao
authors.forename: Jun
authors.initials: J
authors.lastname: Wu
authors.forename: Wei-zhong
authors.initials: WZ
authors.lastname: Zhao
mesh_terms: D000818:Animals; D059001:Aquatic Organisms; D001185:Artificial Intelligence; D001483:Base Sequence; D002170:Canada; D002685:Chiroptera / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D004247:DNA / Q000235:genetics*; D058893:DNA Barcoding, Taxonomic / Q000379:methods*; D021903:DNA, Ribosomal Spacer / Q000235:genetics; D030541:Databases, Genetic; D003576:Electron Transport Complex IV / Q000235:genetics; D005399:Fishes / Q000235:genetics; D005658:Fungi / Q000235:genetics; D008969:Molecular Sequence Data; D016366:Open Reading Frames / Q000235:genetics*; D010140:Pacific Ocean; D000459:Phaeophyceae / Q000235:genetics; D013045:Species Specificity; D013997:Time Factors; D014329:Tropical Climate
chemical_list: D021903:DNA, Ribosomal Spacer; D004247:DNA; D003576:Electron Transport Complex IV