title: Analyzing multi-locus plant barcoding datasets with a composition vector method based on adjustable weighted distance.
abstract: BACKGROUND
  The composition vector (CV) method has been proved to be a reliable and fast alignment-free method to analyze large COI barcoding data. In this study, we modify this method for analyzing multi-gene datasets for plant DNA barcoding. The modified method includes an adjustable-weighted algorithm for the vector distance according to the ratio in sequence length of the candidate genes for each pair of taxa.
  
  
  RESULTS
  Three datasets, matK+rbcL dataset with 2,083 sequences, matK+rbcL dataset with 397 sequences and matK+rbcL+trnH-psbA dataset with 397 sequences, were tested. We showed that the success rates of grouping sequences at the genus/species level based on this modified CV approach are always higher than those based on the traditional K2P/NJ method. For the matK+rbcL datasets, the modified CV approach outperformed the K2P-NJ approach by 7.9% in both the 2,083-sequence and 397-sequence datasets, and for the matK+rbcL+trnH-psbA dataset, the CV approach outperformed the traditional approach by 16.7%.
  
  
  CONCLUSIONS
  We conclude that the modified CV approach is an efficient method for analyzing large multi-gene datasets for plant DNA barcoding. Source code, implemented in C++ and supported on MS Windows, is freely available for download at http://math.xtu.edu.cn/myphp/math/research/source/Barcode_source_codes.zip.
authors: School of Life Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China.
authors: Chi Pang
authors: CP
authors: Li
authors: Zu Guo
authors: ZG
authors: Yu
authors: Guo Sheng
authors: GS
authors: Han
authors: Ka Hou
authors: KH
authors: Chu
mesh_terms: D058893:DNA Barcoding, Taxonomic / Q000379:methods*; D003627:Data Interpretation, Statistical; D056426:Genetic Loci / Q000235:genetics*; D010944:Plants / Q000145:classification* / Q000235:genetics*