A fuzzy‐set‐theory‐based approach to analyse species membership in DNA barcoding
A fuzzy‐set‐theory‐based approach to analyse species membership in DNA barcoding
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DOI：
10.1111/j.1365-294x.2011.05235.x
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发表时间：
2012-04
影响因子：
4.9
通讯作者：
A. Zhang;C. Muster;H.-B. Liang;Chaodong Zhu;R. Crozier;P. Wan;J. Feng;R. Ward
中科院分区：
文献类型：
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作者：
A. Zhang;C. Muster;H.-B. Liang;Chaodong Zhu;R. Crozier;P. Wan;J. Feng;R. Ward
对于不断发展的 DNA 条形码领域来说，将未知查询序列可靠地分配给正确的物种仍然是一个方法学问题。尽管最近取得了巨大进步，但如果相关生物多样性采样不充分，通过条形码进行物种识别仍然不可靠。我们在此提出了 DNA 条形码物种隶属关系的新概念——基于模糊集合理论的模糊隶属关系——并通过 5000 多次随机模拟说明了其在四个真实数据集（蝙蝠、鱼类、蝴蝶和苍蝇）中的成功应用。其中两个数据集包含特别密集的物种/种群水平样本。与当前的 DNA 条形码方法相比，新提出的最小距离 (MD) 加模糊集方法以及另一种计算简单的方法“最佳紧密匹配”优于两种计算复杂的贝叶斯方法和 BootstrapNJ 方法。当参考数据库中不存在查询的同种时，与其他方法相比，本文提出的新方法在减少假阳性物种识别方面具有强大的能力。
Reliable assignment of an unknown query sequence to its correct species remains a methodological problem for the growing field of DNA barcoding. While great advances have been achieved recently, species identification from barcodes can still be unreliable if the relevant biodiversity has been insufficiently sampled. We here propose a new notion of species membership for DNA barcoding—fuzzy membership, based on fuzzy set theory—and illustrate its successful application to four real data sets (bats, fishes, butterflies and flies) with more than 5000 random simulations. Two of the data sets comprise especially dense species/population‐level samples. In comparison with current DNA barcoding methods, the newly proposed minimum distance (MD) plus fuzzy set approach, and another computationally simple method, ‘best close match’, outperform two computationally sophisticated Bayesian and BootstrapNJ methods. The new method proposed here has great power in reducing false‐positive species identification compared with other methods when conspecifics of the query are absent from the reference database.