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• W2059065861 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. Methodology/Principal Findings 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." @default.
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• W2059065861 date "2012-07-27" @default.
• W2059065861 modified "2023-09-27" @default.
• W2059065861 title "Analyzing Multi-locus Plant Barcoding Datasets with a Composition Vector Method Based on Adjustable Weighted Distance" @default.
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• W2059065861 doi "https://doi.org/10.1371/journal.pone.0042154" @default.
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