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Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing

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Titel: Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing
Autoren: Ilaria Massaiu, Vincenza Valerio, Valentina Rusconi, Francesca Bertolini, Donato De Giorgi, Veronika A. Myasoedova, Paolo Poggio
Quelle: Frontiers in Medicine, Vol 12 (2025)
Verlagsinformationen: Frontiers Media S.A., 2025.
Publikationsjahr: 2025
Bestand: LCC:Medicine (General)
Schlagwörter: third-generation sequencing, nanopore sequencing, single nucleotide variation, variant calling, PCSK9, cardiovascular disease, Medicine (General), R5-920
Beschreibung: BackgroundGenetic testing is essential for disease screening, diagnosis, prognosis, and pharmacotherapy guidance. Oxford Nanopore Technologies (ONT) offers a cost-effective platform for long-read sequencing, yet its routine use in clinical diagnostics remains under evaluation. We tested different nanopore sequencing pipelines aimed at accurately detecting single-nucleotide variants (SNV) in a gene locus spanning ⁓25 kb.MethodsAs a proof of concept, PCSK9 was selected for its relevance to cardiovascular disease and suitable sequence structure. Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. Sanger sequencing served as the reference for performance validation. Sequencing throughput per flow cell was also estimated.ResultsThe combination of super high accuracy (SUP) basecalling with Longshot variant calling demonstrated the highest performance across flow cells. MinION flow cell reached a perfect F1-score of 100%, while the more cost-effective Flongle flow cell remained a viable alternative (mean F1-score: 98.2% ± 4.2). Throughput analysis indicated that a single MinION flow cell could process up to 96 samples and ⁓40 long sequencing regions, whereas a Flongle flow cell could support sequencing of 96 samples and one long region.ConclusionThe proposed nanopore-based SNV identification workflows may support the development of long-read, targeted gene panels, offering a promising tool for both diagnostic and discovery applications, particularly in multi-gene settings such as oncology and cardiology.
Publikationsart: article
Dateibeschreibung: electronic resource
Sprache: English
ISSN: 2296-858X
Relation: https://www.frontiersin.org/articles/10.3389/fmed.2025.1620405/full; https://doaj.org/toc/2296-858X
DOI: 10.3389/fmed.2025.1620405
Zugangs-URL: https://doaj.org/article/8c2adccec93d47dd8ea21880b6c7bdfb
Dokumentencode: edsdoj.8c2adccec93d47dd8ea21880b6c7bdfb
Datenbank: Directory of Open Access Journals
Beschreibung
Abstract:BackgroundGenetic testing is essential for disease screening, diagnosis, prognosis, and pharmacotherapy guidance. Oxford Nanopore Technologies (ONT) offers a cost-effective platform for long-read sequencing, yet its routine use in clinical diagnostics remains under evaluation. We tested different nanopore sequencing pipelines aimed at accurately detecting single-nucleotide variants (SNV) in a gene locus spanning ⁓25 kb.MethodsAs a proof of concept, PCSK9 was selected for its relevance to cardiovascular disease and suitable sequence structure. Twelve subjects were analyzed using different sequencing flow cells, basecalling models, and SNV calling algorithms. Sanger sequencing served as the reference for performance validation. Sequencing throughput per flow cell was also estimated.ResultsThe combination of super high accuracy (SUP) basecalling with Longshot variant calling demonstrated the highest performance across flow cells. MinION flow cell reached a perfect F1-score of 100%, while the more cost-effective Flongle flow cell remained a viable alternative (mean F1-score: 98.2% ± 4.2). Throughput analysis indicated that a single MinION flow cell could process up to 96 samples and ⁓40 long sequencing regions, whereas a Flongle flow cell could support sequencing of 96 samples and one long region.ConclusionThe proposed nanopore-based SNV identification workflows may support the development of long-read, targeted gene panels, offering a promising tool for both diagnostic and discovery applications, particularly in multi-gene settings such as oncology and cardiology.
ISSN:2296858X
DOI:10.3389/fmed.2025.1620405