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, Samuel Yang

Journal: PloS one 2015;9(9):e109094

PMID: 25275518

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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.

Address: Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.; Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America; The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland, United States of America.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Emergency Medicine, Johns Hopkins Medicine, Baltimore, Maryland, United States of America.; The Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Medicine, Baltimore, Maryland, United States of America; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, Maryland, United States of America.; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.; Department of Emergency Medicine, Johns Hopkins Medicine, Baltimore, Maryland, United States of America.

DOI: 10.1371/journal.pone.0109094

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MeSH Terms: Algorithms, Artificial Intelligence, Base Sequence, Computer Simulation, DNA, DNA Primers, Genetic Variation, Genotyping Techniques, Humans, Magnesium, Nucleic Acid Denaturation, Potassium, Reproducibility of Results, Serotyping, Sodium, Streptococcus pneumoniae, Tumor Suppressor Proteins