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 barcod

SUBMITTER: Zhang AB 

PROVIDER: S-EPMC3282726 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature