Evidence map›Paper›PMID 42213080›Full record

ArticleBioinformatics (Oxford, England)2026

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

Pierre Lechat, Aurelia Kwasiborski, Rémi Vincent, Jessica Vanhomwegen, Jean-Claude Manuguerra, Valérie Caro, Véronique Hourdel

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Pierre LechatInstitut Pasteur, Bioinformatics and Biostatistics Hub, 75015, Paris, France.
Aurelia KwasiborskiInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.
Rémi VincentInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.
Jessica VanhomwegenInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.ORCID 0000-0002-7879-1485
Jean-Claude ManuguerraInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.
Valérie CaroInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.
Véronique HourdelInstitut Pasteur, Université de Paris, Environment and Infectious Risks Unit (ERI), Laboratory for Urgent Response to Biological Threats (CIBU), 75015, Paris, France.ORCID 0000-0003-0618-4552

Funding

Ministry of Health, France 101102733
6 · The paper itself

Abstract

motivationInfectious 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.

resultsHere, 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.

Indexed as

Communicable DiseasesHigh-Throughput Nucleotide SequencingMultiplex Polymerase Chain ReactionSoftwareAnimalsComputational BiologyHumansNanopore SequencingSequence Analysis, DNA

Identifiers

PMID42213080
PMCPMC13290484

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.