VisPan: Real-time visualisation of multiplex amplicon-based sequencing panels for rapid syndromic surveillance and pathogen detection

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ID: 315249
2026
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Abstract
Abstract Motivation Infectious diseases persist as a major global public health challenge. Diverse factors, including climate change, globalization, deforestation, human-animal interactions, lifestyle choices, and various biological factors, can contribute to their emergence and reemergence. Rapid detection and characterization of (re)emerging pathogens are therefore critical for effective outbreak management and for enhancing our understanding of epidemics by monitoring the transmission, spread, evolution, and genomics of pathogens. In this context, next-generation sequencing technologies (NGS), particularly long-read platforms such as Oxford Nanopore Technologies (ONT), have opened new avenues for real-time pathogen monitoring. However, the bioinformatics bottleneck remains a challenge, emphasizing the need for efficient, accessible, and user-friendly analysis tools. Results Here, we present a tool adapted from the RAMPART software that enables real-time data visualisation of multiplex PCR syndromic panels combined with Oxford Nanopore sequencing. This real-time analysis enables rapid pathogen detection, from raw data acquisition to taxonomic assignment, within minutes. The interface offers dynamic visual tracking of the sequencing run and amplicon coverage, facilitating immediate insights during diagnostic workflows. Validation experiments confirmed the system’s reliability, accurately identifying all pathogens present in complex clinical or environmental samples. This tool provides an integrated, user-friendly solution for genomic pathogen surveillance in field or clinical settings. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7162805921 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors P Lechat, Aurélia Kwasiborski, Rémi Vincent, Jessica Vanhomwegen, Jean-Claude Manuguerra, Valérie Caro, Véronique Hourdel
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag351
URL
https://doi.org/10.1093/bioinformatics/btag351
Keywords Keywords not found

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