Related Experiment Video
Updated: Nov 17, 2025
10:27
Methods to Increase the Sensitivity of High Resolution Melting Single Nucleotide Polymorphism Genotyping in Malaria
Published on: November 10, 2015
11.9K
MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data.
Matteo Perini1, Gherard Batisti Biffignandi2, Domenico Di Carlo1
1Department of Biomedical and Clinical Sciences "L. Sacco", Pediatric Clinical Research Center "Romeo and Enrica Invernizzi", Università Di Milano, 20157, Milan, Italy.
BMC Bioinformatics
|February 19, 2021
Summary
MeltingPlot simplifies pathogen clone identification using High Resolution Melting (HRM) data for epidemiological surveillance. This tool aids rapid outbreak reconstruction and real-time monitoring in hospitals.
Area of Science:
- Molecular Biology
- Infectious Disease Epidemiology
- Bioinformatics
Background:
- Rapid identification of pathogen clones is crucial for effective hospital epidemiology.
- High Resolution Melting (HRM) offers a fast, cost-effective method for pathogen typing.
- Analyzing HRM data for typing requires specialized bioinformatics skills, limiting its widespread use.
Purpose of the Study:
- To introduce MeltingPlot, a novel bioinformatics tool designed to simplify HRM data analysis for epidemiological investigations.
- To facilitate the application of HRM typing in real-time surveillance and outbreak reconstruction within hospital settings.
Main Methods:
- Development of MeltingPlot, a web-based tool implementing a graph-based algorithm for pathogen clone discrimination using HRM data.
- Integration of isolate and patient metadata with HRM typing results for comprehensive analysis.
- Creation of graphical and tabular outputs for epidemiological insights.
Main Results:
- MeltingPlot successfully discriminates pathogen clones based on HRM data, generating portable typing results.
- The tool efficiently merges typing data with metadata, producing valuable outputs for epidemiological investigations.
- MeltingPlot processes large datasets (hundreds of isolates) within seconds.
Conclusions:
- MeltingPlot overcomes the analytical challenges of HRM typing, making it accessible for hospital settings.
- The tool supports the implementation of real-time, large-scale surveillance programs utilizing HRM-based methods.
- MeltingPlot aids in reconstructing epidemiological events by combining HRM clustering with isolate and patient metadata.
