Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.

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dc.citation.volume14
dc.contributor.authorYang Z
dc.contributor.authorGuarracino A
dc.contributor.authorBiggs PJ
dc.contributor.authorBlack MA
dc.contributor.authorIsmail N
dc.contributor.authorWold JR
dc.contributor.authorMerriman TR
dc.contributor.authorPrins P
dc.contributor.authorGarrison E
dc.contributor.authorde Ligt J
dc.contributor.editorHane J
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-12-12T20:45:43Z
dc.date.available2024-12-12T20:45:43Z
dc.date.issued2023-08-10
dc.description.abstractWhole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease. Moreover, this innovative pipeline, leveraging pangenome graphs, can bridge variant analysis, genome assembly, population genetics, and evolutionary biology, expanding the reach of genomic understanding and applications.
dc.description.confidentialfalse
dc.edition.edition2023
dc.format.pagination1225248-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37636268
dc.identifier.citationYang Z, Guarracino A, Biggs PJ, Black MA, Ismail N, Wold JR, Merriman TR, Prins P, Garrison E, de Ligt J. (2023). Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.. Front Genet. 14. (pp. 1225248-).
dc.identifier.doi10.3389/fgene.2023.1225248
dc.identifier.eissn1664-8021
dc.identifier.elements-typejournal-article
dc.identifier.issn1664-8021
dc.identifier.number1225248
dc.identifier.pii1225248
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72300
dc.languageeng
dc.publisherFrontiers Media S.A.
dc.publisher.urihttps://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1225248/full
dc.relation.isPartOfFront Genet
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcomparative genomics
dc.subjectgenetic variation
dc.subjectgenome assembly
dc.subjectgenomic surveillance
dc.subjectinfectious diseases
dc.subjectlong-read sequencing
dc.subjectpangenome graphs
dc.titlePangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis leveraging Oxford Nanopore long reads.
dc.typeJournal article
pubs.elements-id479995
pubs.organisational-groupOther
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