Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants.

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 sero

SUBMITTER: Athamanolap P 

PROVIDER: S-EPMC4183555 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature