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Analyzing Multi-locus Plant Barcoding Datasets with a Composition Vector Method Based on Adjustable Weighted Distance
- Source :
- PLoS ONE, PLoS ONE, Vol 7, Iss 7, p e42154 (2012)
- Publication Year :
- 2012
- Publisher :
- Public Library of Science (PLoS), 2012.
-
Abstract
- BackgroundThe 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 findingsThree 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%.ConclusionsWe 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.
- Subjects :
- Plant Phylogenetics
Vector method
Science
Sequence Databases
Locus (genetics)
Modified method
Plant Science
Bioinformatics
Coi barcoding
Barcode
DNA barcoding
law.invention
Species level
Genome Analysis Tools
law
Genome Databases
DNA Barcoding, Taxonomic
Genome Sequencing
Biology
Mathematics
Weighted distance
Sequence Assembly Tools
Multidisciplinary
business.industry
Computational Biology
Plant Taxonomy
Pattern recognition
Genomics
Plants
Genetic Loci
Data Interpretation, Statistical
Medicine
Artificial intelligence
business
Sequence Analysis
Research Article
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....29bef61d6d188c67f7295b5b06bf3f4d