CATHAI: cluster analysis tool for healthcare-associated infections

Thom Cuddihy; Patrick N A Harris; Budi Permana; Scott A Beatson; Brian M Forde

doi:10.1093/bioadv/vbac040

CATHAI: cluster analysis tool for healthcare-associated infections

Bioinform Adv. 2022 May 27;2(1):vbac040. doi: 10.1093/bioadv/vbac040. eCollection 2022.

Authors

Thom Cuddihy¹, Patrick N A Harris^{1

2

3}, Budi Permana⁴, Scott A Beatson^{2

4}, Brian M Forde^{1

2}

Affiliations

¹ University of Queensland, UQ Centre for Clinical Research, Brisbane, QLD 4029, Australia.
² Australian Infectious Diseases Research Centre, University of Queensland, Brisbane, QLD 4072, Australia.
³ Central Microbiology, Pathology Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane, QLD 4029, Australia.
⁴ School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD 4072, Australia.

Abstract

Motivation: Whole genome sequencing (WGS) is revolutionizing disease surveillance where it facilitates high-resolution clustering of related organism and outbreak detection. However, visualizing and efficiently communicating genomic data back to clinical staff is crucial for the successful deployment of a targeted infection control response.

Results: CATHAI (cluster analysis tool for healthcare-associated infections) is an interactive web-based visualization platform that couples WGS informed clustering with associated metadata, thereby converting sequencing data into informative and accessible clinical information for the management of healthcare-associated infections (HAI) and nosocomial outbreaks.

Availability and implementation: All code associated with this application are free available from https://github.com/FordeGenomics/cathai. A demonstration version of CATHAI is available online at https://cathai.fordelab.com.