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唐银琳, 刘镛, 姚辉, 陈振东, 吴耀生, 徐鹏, 蒋东. DNA条形码(ITS2)在葫芦科鉴定中应用价值的评估[J]. 植物科学学报, 2012, 30(6): 631-638. DOI: 10.3724/SP.J.1142.2012.60631
引用本文: 唐银琳, 刘镛, 姚辉, 陈振东, 吴耀生, 徐鹏, 蒋东. DNA条形码(ITS2)在葫芦科鉴定中应用价值的评估[J]. 植物科学学报, 2012, 30(6): 631-638. DOI: 10.3724/SP.J.1142.2012.60631
TANG Yin-Lin, LIU Yong, Yao Hui, CHEN Zhen-Dong, WU Yao-Sheng, XU Peng, JIANG Dong. Evaluation on the Application Value of Identification in Cucurbitaceae by ITS2[J]. Plant Science Journal, 2012, 30(6): 631-638. DOI: 10.3724/SP.J.1142.2012.60631
Citation: TANG Yin-Lin, LIU Yong, Yao Hui, CHEN Zhen-Dong, WU Yao-Sheng, XU Peng, JIANG Dong. Evaluation on the Application Value of Identification in Cucurbitaceae by ITS2[J]. Plant Science Journal, 2012, 30(6): 631-638. DOI: 10.3724/SP.J.1142.2012.60631

DNA条形码(ITS2)在葫芦科鉴定中应用价值的评估

Evaluation on the Application Value of Identification in Cucurbitaceae by ITS2

  • 摘要: 探讨在纳入分析数据时,数据信息的选择对ITS2序列作为DNA条形码在葫芦科植物中鉴定能力的影响。首先,建立由葫芦科植物ITS2序列组成的3个资料组,其中Dataset1为实验样本,Dataset2由实验样本及GenBank数据库样本组合,Dataset3为从Dataset2中去除部分序列后所得。通过比较3个资料组的种间、种内的变异、Barcoding Gap及鉴定成功率,评估纳入分析的数据选择差异对ITS2鉴定能力的影响。结果显示ITS2序列在3个资料组属水平上的鉴定成功率均达到100%;种水平上,用BLAST1法鉴定成功率分别为100%、 67.8%、 90.6%,Nearest Distance法鉴定成功率分别为100%、 52.5%、 66.5%。可见纳入分析的数据选择有差异时,会导致鉴定成功率的较大变化。3个资料组中,ITS2分析仅有Dataset2的Barcoding Gap不够显著。因此对于DNA条形码分析中的数据纳入标准,值得进一步研究。

     

    Abstract: We discussed the influence of analytical data selection of the ITS2 sequences as DNA barcode identification ability in the Cucurbitaceae. Three data sets were built which contained the ITS2 sequence from different Cucurbitaceae samples: Dataset 1 (Experimental group), Dataset 2 (sequences both from experiment and GenBank group), and Dataset 3 (portion of Dataset 2 group). By comparing intra- and inter-specific variation, barcoding gap, and identification efficiency among the three data sets with BLAST 1 and Nearest Distance methods, the influence on ITS2 identification capacity among different data selection was eva-luated. Results showed that the rate of successful identification using ITS2 sequence among the three datasets reached 100% at the genus level and 100%, 67.8%, and 90.6%, respectively, with the BLAST 1 method at the species level, and 100%, 52.5%, and 66.5%, respectively, with the Nearest Distance method. Clearly, the different selection of data led to the large discrepancy of the identification success rate. Among the three data sets, only the barcoding gap in Dataset 2 was not obvious. Therefore, the inclusion criteria of data in the DNA barcode analysis deserves further investigation.

     

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