a new method for species identification via protein-coding and non-coding dna barcodes by combining machine learning with bioinformatic methods一种物种鉴定的新方法通过蛋白质编码和非编码dna条形码结合机器学习与生物信息学方法.pdfVIP

A New Method for Species Identification via Protein- Coding and Non-Coding DNA Barcodes by Combining Machine Learning with Bioinformatic Methods 1 2 3 1 1 1 2 Ai-bing Zhang *, Jie Feng , Robert D. Ward , Ping Wan , Qiang Gao , Jun Wu , Wei-zhong Zhao 1 College of Life Sciences, Capital Normal University, Beijing, People’s Republic of China, 2 School of Mathematical Sciences, Capital Normal University, Beijing, People’s Republic of China, 3 Wealth from Oceans Flagship, CSIRO Marine and Atmospheric Research, Hobart, Tasmania, Australia 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