2015
DOI: 10.1371/journal.pone.0122903
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DNA Barcoding Evaluation and Its Taxonomic Implications in the Species-Rich Genus Primula L. in China

Abstract: The genus Primula is extremely diverse in the east Himalaya-Hengduan Mountains (HHM) in China as a result of rapid radiation. In order to overcome the difficulty of morphological classification of this genus, we surveyed three plastid regions (rbcL, matK, and trnH-psbA) and two nuclear markers (ITS and ITS2) from 227 accessions representing 66 Primula species across 18 sections, to assess their discriminatory power as barcodes. We found that ITS alone or combined with plastid regions showed the best discrimina… Show more

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Cited by 42 publications
(64 citation statements)
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References 61 publications
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“…In this study, sequence analysis of ITS datasets using Bayesian inference (BI) tree-based method gave the highest species resolution based on the topology with the highest product of posterior clade probabilities across all nodes followed by BM and BCM model of TaxonDNA, which too presented equally efficient results either in single or combination of barcodes. Similarly, patterned results have been obtained in different DNA barcoding studies in various plant groups (Yan et al, 2014; Giudicelli, Mäder & De, 2015; Xu et al, 2015; Yan et al, 2015). The clustering algorithm of the Bayesian framework provides a flexible way to model rate variation and obtain reliable estimates of speciation times, provided the assumptions of the models be adequate (Drummond et al, 2012).…”
Section: Discussionsupporting
confidence: 59%
See 1 more Smart Citation
“…In this study, sequence analysis of ITS datasets using Bayesian inference (BI) tree-based method gave the highest species resolution based on the topology with the highest product of posterior clade probabilities across all nodes followed by BM and BCM model of TaxonDNA, which too presented equally efficient results either in single or combination of barcodes. Similarly, patterned results have been obtained in different DNA barcoding studies in various plant groups (Yan et al, 2014; Giudicelli, Mäder & De, 2015; Xu et al, 2015; Yan et al, 2015). The clustering algorithm of the Bayesian framework provides a flexible way to model rate variation and obtain reliable estimates of speciation times, provided the assumptions of the models be adequate (Drummond et al, 2012).…”
Section: Discussionsupporting
confidence: 59%
“…The possible reason behind the results might be the inter-specific sharing of identical sequences or failure of conspecific individuals to group together. Besides, many other aspects have also been reported for unclear barcoding gap such as imperfect taxonomy, inter-specific hybridization, paralogy and incomplete lineage sorting (Yan et al, 2015). However, ITS region has proved to be a suitable marker in authentication of Cassia species in the commercial herbal market (Seethapathy et al, 2014).…”
Section: Discussionmentioning
confidence: 99%
“…Phylogenetic analyses by using DNA barcoding confirmed the monophyly of sect. Proliferae which could be used in narrowing the scope of identification in Primula (Yan et al 2015). Preliminary molecular phylogenetic analyses in this study supports the view that P. dongchuanensis is a member of sect.…”
Section: Discussionsupporting
confidence: 78%
“…However, molecular phylogenetic evidence posited the opposite conclusion and indicated that the sect. Proliferae represents relatively advanced members of Primula that exist today (Mast et al 2001, 2004, Yan et al 2015.…”
Section: Introductionmentioning
confidence: 99%
“…In this study, sequence analysis of ITS datasets using Bayesian inference (BI) tree-based method gave the highest species resolution based on the topology with the highest product of posterior clade probabilities across all nodes followed by BM and BCM model of TaxonDNA, which too presented equally efficient results either in single or combination of barcodes. Similarly, patterned results have been obtained in different DNA barcoding studies in various plant groups (Yan et al, 2014;Giudicelli et al, 2015;Xu et al, 2015;Yan et al, 2015). The clustering algorithm of Bayesian framework provides a flexible way to model rate variation and obtain reliable estimates of speciation times, provided the assumptions of the models be adequate (Drummond et al, 2012).…”
Section: Discrimination Successmentioning
confidence: 80%