Trainable High Resolution Melt Curve Machine Learning Classifier for Large-Scale Reliable Genotyping of Sequence Variants

Pornpat Athamanolap, Vishwa Parekh, Stephanie I Fraley, Vatsal Agarwal, Dong J Shin, Michael A Jacobs, Tza-Huei Wang and Samuel Yang

PLOS ONE, 2014, vol. 9, issue 10, 1-10

Abstract: 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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0109094

DOI: 10.1371/journal.pone.0109094

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