title: Nested Machine Learning Facilitates Increased Sequence Content for Large-Scale Automated High Resolution Melt Genotyping.
abstract: High Resolution Melt (HRM) is a versatile and rapid post-PCR DNA analysis technique primarily used to differentiate sequence variants among only a few short amplicons. We recently developed a one-vs-one support vector machine algorithm (OVO SVM) that enables the use of HRM for identifying numerous short amplicon sequences automatically and reliably. Herein, we set out to maximize the discriminating power of HRM + SVM for a single genetic locus by testing longer amplicons harboring significantly more sequence information. Using universal primers that amplify the hypervariable bacterial 16 S rRNA gene as a model system, we found that long amplicons yield more complex HRM curve shapes. We developed a novel nested OVO SVM approach to take advantage of this feature and achieved 100% accuracy in the identification of 37 clinically relevant bacteria in Leave-One-Out-Cross-Validation. A subset of organisms were independently tested. Those from pure culture were identified with high accuracy, while those tested directly from clinical blood bottles displayed more technical variability and reduced accuracy. Our findings demonstrate that long sequences can be accurately and automatically profiled by HRM with a novel nested SVM approach and suggest that clinical sample testing is feasible with further optimization.
authors.affiliation: Bioengineering, The University of California San Diego, La Jolla, California, 92093, USA.|Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Stephanie I
authors.initials: SI
authors.lastname: Fraley
authors.affiliation: Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Pornpat
authors.initials: P
authors.lastname: Athamanolap
authors.affiliation: Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.|Infectious Disease, Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Billie J
authors.initials: BJ
authors.lastname: Masek
authors.affiliation: Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.|Infectious Disease, Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Justin
authors.initials: J
authors.lastname: Hardick
authors.affiliation: Medical Microbiology, Pathology, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Karen C
authors.initials: KC
authors.lastname: Carroll
authors.affiliation: Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Yu-Hsiang
authors.initials: YH
authors.lastname: Hsieh
authors.affiliation: Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Richard E
authors.initials: RE
authors.lastname: Rothman
authors.affiliation: Infectious Disease, Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Charlotte A
authors.initials: CA
authors.lastname: Gaydos
authors.affiliation: Biomedical Engineering, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.|Mechanical Engineering, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Tza-Huei
authors.initials: TH
authors.lastname: Wang
authors.affiliation: Emergency Medicine, Stanford University, Stanford, California, 94305, USA.|Emergency Medicine, The Johns Hopkins University, Baltimore, Maryland, 21218, USA.
authors.forename: Samuel
authors.initials: S
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D004247:DNA / Q000235:genetics; D017931:DNA Primers / Q000235:genetics; D014644:Genetic Variation; D005838:Genotype; D006801:Humans; D000069550:Machine Learning; D016133:Polymerase Chain Reaction / Q000379:methods*; D012336:RNA, Ribosomal, 16S / Q000235:genetics*
chemical_list: D017931:DNA Primers; D012336:RNA, Ribosomal, 16S; D004247:DNA