Matteo Perini1, Gherard Batisti Biffignandi2, Domenico Di Carlo1, Ajay Ratan Pasala1, Aurora Piazza2, Simona Panelli1, Gian Vincenzo Zuccotti1,3, Francesco Comandatore4. 1. Department of Biomedical and Clinical Sciences "L. Sacco", Pediatric Clinical Research Center "Romeo and Enrica Invernizzi", Università Di Milano, 20157, Milan, Italy. 2. Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italia. 3. Department of Pediatrics, Children's Hospital Vittore Buzzi, Università Di Milano, Milan, Italy. 4. Department of Biomedical and Clinical Sciences "L. Sacco", Pediatric Clinical Research Center "Romeo and Enrica Invernizzi", Università Di Milano, 20157, Milan, Italy. francesco.comandatore@unimi.it.
Abstract
BACKGROUND: The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings. RESULTS: MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. AVAILABILITY: https://skynet.unimi.it/index.php/tools/meltingplot/ . CONCLUSIONS: The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.
BACKGROUND: The rapid identification of pathogen clones is pivotal for effective epidemiological control strategies in hospital settings. High Resolution Melting (HRM) is a molecular biology technique suitable for fast and inexpensive pathogen typing protocols. Unfortunately, the mathematical/informatics skills required to analyse HRM data for pathogen typing likely limit the application of this promising technique in hospital settings. RESULTS: MeltingPlot is the first tool specifically designed for epidemiological investigations using HRM data, easing the application of HRM typing to large real-time surveillance and rapid outbreak reconstructions. MeltingPlot implements a graph-based algorithm designed to discriminate pathogen clones on the basis of HRM data, producing portable typing results. The tool also merges typing information with isolates and patients metadata to create graphical and tabular outputs useful in epidemiological investigations and it runs in a few seconds even with hundreds of isolates. AVAILABILITY: https://skynet.unimi.it/index.php/tools/meltingplot/ . CONCLUSIONS: The analysis and result interpretation of HRM typing protocols can be not trivial and this likely limited its application in hospital settings. MeltingPlot is a web tool designed to help the user to reconstruct epidemiological events by combining HRM-based clustering methods and the isolate/patient metadata. The tool can be used for the implementation of HRM based real time large scale surveillance programs in hospital settings.
Entities:
Keywords:
Bacterial typing; Epidemiology; High Resolution Melting; Nosocomial infection; Outbreak reconstruction; Real time surveillance; Web interface
Authors: Sophia David; Sandra Reuter; Simon R Harris; Corinna Glasner; Theresa Feltwell; Silvia Argimon; Khalil Abudahab; Richard Goater; Tommaso Giani; Giulia Errico; Marianne Aspbury; Sara Sjunnebo; Edward J Feil; Gian Maria Rossolini; David M Aanensen; Hajo Grundmann Journal: Nat Microbiol Date: 2019-07-29 Impact factor: 17.745