Evidence map›Paper›PMID 40076631›Full record

ArticleInternational journal of molecular sciences2025

Viral Network Analyzer (VirNA): A Novel Minimum Spanning Networks Algorithm for Investigating Viral Evolution.

Giorgia Mazzotti, Luca Bianco, Enrico Lavezzo, Martina Bado, Stefano Toppo, Paolo Fontana

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

6 authors.

Giorgia MazzottiDepartment of Molecular Medicine, University of Padua, 35131 Padua, Italy.
Luca BiancoEdmund Mach, 38098 Trento, Italy.ORCID 0000-0002-0196-8422
Enrico LavezzoDepartment of Molecular Medicine, University of Padua, 35131 Padua, Italy.ORCID 0000-0002-3902-8356
Martina BadoDepartment of Molecular Medicine, University of Padua, 35131 Padua, Italy.
Stefano ToppoDepartment of Molecular Medicine, University of Padua, 35131 Padua, Italy.ORCID 0000-0002-0246-3119
Paolo FontanaEdmund Mach, 38098 Trento, Italy.ORCID 0000-0002-4523-4818

Funding

European Union - Next Generation EU Mission 4, Component 2, CUP B93D21010860004 and Mission 1, Component 8 CUP C93C22002800006PRIN (Research Projects of National Interest) 2020XFNCP7 TOPP_PRIN22_01
6 · The paper itself

Abstract

Next Generation Sequencing technologies are essential in public health surveillance for tracking pathogen evolution, spread, and the emergence of new variants. However, the extensive sequencing of viral genomes during recent pandemics has highlighted the limitations of traditional molecular phylogenetic algorithms in capturing fine-grained evolutionary details when analyzed sequences are highly similar and datasets are large-scale. VirNA (Viral Network Analyzer) addresses this challenge by reconstructing detailed mutation patterns and tracing pathogen evolutionary routes in specific geographical regions through Minimum Spanning Networks. It enables users to analyze thousands of sequences, generating networks where nodes represent genomic sequences linked to their metadata, while edges represent potential evolutionary pathways. VirNA is a powerful tool for analyzing large, high-quality datasets, providing detailed insights into rapid pathogen evolution over short time periods, with potential applications in pandemic surveillance.

Indexed as

AlgorithmsComputational BiologyEvolution, MolecularGenome, ViralSoftwareVirusesHigh-Throughput Nucleotide SequencingHumansPhylogenyevolutionminimum spanning networksviruses

Identifiers

PMID40076631
PMCPMC11900457

What OpenQuestion holds

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Registered trials

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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.