Microbial Typing by Machine Learned DNA Melt Signatures

Nadya Andini, Bo Wang, Pornpat Athamanolap, Justin Hardick, Billie J. Masek, Simone Thair, Anne Hu, Gideon Avornu, Stephen Peterson, Steven Cogill, Richard E. Rothman, Karen C. Carroll, Charlotte A. Gaydos, Jeff Tza Huei Wang, Serafim Batzoglou, Samuel Yang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


There is still an ongoing demand for a simple broad-spectrum molecular diagnostic assay for pathogenic bacteria. For this purpose, we developed a single-plex High Resolution Melt (HRM) assay that generates complex melt curves for bacterial identification. Using internal transcribed spacer (ITS) region as the phylogenetic marker for HRM, we observed complex melt curve signatures as compared to 16S rDNA amplicons with enhanced interspecies discrimination. We also developed a novel Naïve Bayes curve classification algorithm with statistical interpretation and achieved 95% accuracy in differentiating 89 bacterial species in our library using leave-one-out cross-validation. Pilot clinical validation of our method correctly identified the etiologic organisms at the species-level in 59 culture-positive mono-bacterial blood culture samples with 90% accuracy. Our findings suggest that broad bacterial sequences may be simply, reliably and automatically profiled by ITS HRM assay for clinical adoption.

Original languageEnglish (US)
Article number42097
JournalScientific reports
StatePublished - Feb 6 2017

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Microbial Typing by Machine Learned DNA Melt Signatures'. Together they form a unique fingerprint.

Cite this