Evidence map›Paper›PMID 39829835›Full record

ArticlebioRxiv : the preprint server for biology2025

Development of avian influenza A(H5) virus datasets for Nextclade enables rapid and accurate clade assignment.

Jordan T Ort, Sonja A Zolnoski, Tommy T-Y Lam, Richard Neher, Louise H Moncla

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In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Jordan T OrtDepartment of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-6977-4168
Sonja A ZolnoskiDepartment of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Tommy T-Y LamState Key Laboratory of Emerging Infectious Diseases, HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong SAR, China.ORCID 0000-0002-9769-1527
Richard NeherBiozentrum, University of Basel, Basel, Switzerland.ORCID 0000-0003-2525-1407
Louise H MonclaDepartment of Pathobiology, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0001-5722-1988

Funding

NIAID Centers of Excellence for Influenza Research and Response: Universal Influenza Vaccine Research Activities75N93021C00015 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI HENSLEY, SCOTT · 2021 to 2025
$50.7M
Quantifying the genetic and environmental factors driving avian influenza spilloverR00AI147029 · NIAID · UNIVERSITY OF PENNSYLVANIA · PI MONCLA, LOUISE HILLIER · 2022 to 2023
$498k
NIAID NIH HHS 75N93021C00015NIAID NIH HHS R00 AI147029
6 · The paper itself

Abstract

The ongoing panzootic of highly pathogenic avian influenza (HPAI) A(H5) viruses is the largest in history, with unprecedented transmission to multiple mammalian species. Avian influenza A viruses of the H5 subtype circulate globally among birds and are classified into distinct clades based on their hemagglutinin (HA) genetic sequences. Thus, the ability to accurately and rapidly assign clades to newly sequenced isolates is key to surveillance and outbreak response. Co-circulation of endemic, low pathogenic avian influenza (LPAI) A(H5) lineages in North American and European wild birds necessitates the ability to rapidly and accurately distinguish between infections arising from these lineages and epizootic HPAI A(H5) viruses. However, currently available clade assignment tools are limited and often require command line expertise, hindering their utility for public health surveillance labs. To address this gap, we have developed datasets to enable A(H5) clade assignments with Nextclade, a drag-and-drop tool originally developed for SARS-CoV-2 genetic clade classification. Using annotated reference datasets for all historical A(H5) clades, clade 2.3.2.1 descendants, and clade 2.3.4.4 descendants provided by the Food and Agriculture Organization/World Health Organization/World Organisation for Animal Health (FAO/WHO/WOAH) H5 Working Group, we identified clade-defining mutations for every established clade to enable tree-based clade assignment. We then created three Nextclade datasets which can be used to assign clades to A(H5) HA sequences and call mutations relative to reference strains through a drag-and-drop interface. Nextclade assignments were benchmarked with 19,834 unique sequences not in the reference set using a pre-released version of LABEL, a well-validated and widely used command line software. Prospective assignment of new sequences with Nextclade and LABEL produced very well-matched assignments (match rates of 97.8% and 99.1% for the 2.3.2.1 and 2.3.4.4 datasets, respectively). The all-clades dataset also performed well (94.8% match rate) and correctly distinguished between all HPAI and LPAI strains. This tool additionally allows for the identification of polybasic cleavage site sequences and potential N-linked glycosylation sites. These datasets therefore provide an alternative, rapid method to accurately assign clades to new A(H5) HA sequences, with the benefit of an easy-to-use browser interface.

Identifiers

PMID39829835
PMCPMC11741357

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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.