A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 05 12 2023
accepted: 09 03 2024
medline: 26 4 2024
pubmed: 26 4 2024
entrez: 26 4 2024
Statut: epublish

Résumé

Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

Identifiants

pubmed: 38669259
doi: 10.1371/journal.pone.0301069
pii: PONE-D-23-40273
doi:

Substances chimiques

DNA, Viral 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0301069

Informations de copyright

Copyright: © 2024 Duchen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Dylan Duchen (D)

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.
Center for Biomedical Data Science, Yale School of Medicine, New Haven, CT, United States of America.

Steven J Clipman (SJ)

Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.

Candelaria Vergara (C)

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.

Chloe L Thio (CL)

Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.

David L Thomas (DL)

Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.

Priya Duggal (P)

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.

Genevieve L Wojcik (GL)

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America.

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Classifications MeSH