A Method of Osmanthus fragrans Cultivars Identification Based on Random Forest Algorithm and SRAP Molecular Markers

被引：0
|
作者：
Qiu S. [1 ]
Shen B. [1 ]
Li T. [2 ]
Guo J. [1 ]
Wang J. [1 ]
Sun L. [1 ]
Chen X. [1 ]
Hu S. [3 ]
机构：
[1] Hangzhou Landscaping Incorporated, Hangzhou
[2] Zhejiang Forestry Academy, Hangzhou
[3] Zhejiang Sci-Tech University, Hangzhou
来源：
Linye Kexue/Scientia Silvae Sinicae | 2018年 / 54卷 / 01期
关键词：
Classification model; Cultivar identification; Osmanthus fragrans; Random forest algorithm; SRAP marker;
D O I：
10.11707/j.1001-7488.20180104
中图分类号：
学科分类号：
摘要：
Objective: To solve the problem that Osmanthus fragrans cultivars being hardly identified in nursery stock production and landscape application, this study proposed a classification method based on random forest algorithm and SRAP molecular markers, which can be used for easily, quickly and accurately identifying varieties. Method: DNA of 45 O. fragrans cultivars were extracted, which were applied to PCR amplification, using 90 SRAP primer pairs. The fragments were examined by Capillary Electrophoresis to screen the primer pairs with high polymorphism level and steady amplification. The amplification data were used to calculate polymorphism information content (PIC), numbers of patterns, numbers of effective patterns, the discriminating power (D), chi-square value of patterns distribution (χ2), and pairs of indistinguishable samples (x). The locus data of combination of primer pairs that can discriminate all cultivars were used as training set for construction of classification modes based on random forest algorithm. The models with best classifying ability were selected depending on their generalization ability and classifying quality. Result: A total of 10 SRAP primer pairs were selected, with mean PIC of 0.26, mean numbers of patterns of 33.9, mean numbers of effective patterns of 26.6, mean D of 0.97, mean χ2 of 21.07 and mean x of 28.2. Eight classification models were constructed using 8 combination of 2 prime pairs that can discriminate all cultivars (rf1-rf8). The OOB (out of bag) error rate of these models ranged from 0.004 4-0.013 9. Among of them, rf5 and rf3 had the strongest generalization ability, while rf8 had the weakest. And rf1 had the best classifying quality, rf3, rf4, rf5 and rf7 had better, while rf8 had the worst. Conclusion: Classification models rf1, rf3, rf4, rf5 and rf7 have the strongest classifying ability, with the combination of SRAP primer pairs of me1/em3+me9/em6, me4/em5+me9/em6, me4/em8+me9/em6, me6/em9+me9/em6 and me5/em5+me9/em6, separately. The weaker correlation of selected primer pairs brings the stronger classifying ability of models. The method proposed in this study can be applied to identity O. fragrans cultivars quickly and accurately. © 2018, Editorial Department of Scientia Silvae Sinicae. All right reserved.
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页码：32 / 45
页数：13
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