Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants.
Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants.
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DOI：
10.1371/journal.pone.0109094
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Yang S
中科院分区：
文献类型：
--
作者：
Athamanolap P;Parekh V;Fraley SI;Agarwal V;Shin DJ;Jacobs MA;Wang TH;Yang S
高分辨率熔解（HRM）作为一种简单而稳健的序列变异基因分型方法正获得相当大的普及。然而，对可能存在大量可能变体的未知样品进行准确的基因分型将需要能够将未知物与大量已知序列变体进行比较的自动化HRM曲线鉴定方法。在这里，我们描述了一种基于机器学习方法和学习的反应条件偏差公差的自动HRM曲线分类的新方法。我们使用9种不同模拟实验条件生成的曲线对肺炎链球菌的92种已知血清型进行了多次交叉验证，并通过计算机模拟测试了该方法，证明了每个血清型8条训练曲线的准确率超过99%。使用癌症相关基因的序列变体测试了算法的体外验证，并证明每个序列变体具有3条训练曲线的100%准确度。机器学习算法实现了可靠、可扩展和自动化的HRM基因分型分析，具有广泛的潜在临床和流行病学应用。
High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.
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影响因子：
10.7
作者：
Castresana, J
通讯作者：
Castresana, J
影响因子：
3
作者：
Lassmann T;Sonnhammer EL
通讯作者：
Sonnhammer EL
影响因子：
14.9
作者：
Fraley SI;Hardick J;Masek BJ;Athamanolap P;Rothman RE;Gaydos CA;Carroll KC;Wakefield T;Wang TH;Yang S
通讯作者：
Yang S
影响因子：
5.8
作者：
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通讯作者：
Wittwer, Carl T.
影响因子：
3.7
作者：
Li, Bing-Sheng;Wang, Xin-Ying;Xu, An-Gao
通讯作者：
Xu, An-Gao