Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing.
Fungal identification
Internal transcribed spacer (ITS)
Nanopore technology
Targeted long-read sequencing
Journal
IMA fungus
ISSN: 2210-6340
Titre abrégé: IMA Fungus
Pays: England
ID NLM: 101557546
Informations de publication
Date de publication:
06 Sep 2023
06 Sep 2023
Historique:
received:
05
12
2022
accepted:
22
08
2023
medline:
7
9
2023
pubmed:
7
9
2023
entrez:
6
9
2023
Statut:
epublish
Résumé
Among molecular-based techniques for fungal identification, Sanger sequencing of the primary universal fungal DNA barcode, the internal transcribed spacer (ITS) region (ITS1, 5.8S, ITS2), is commonly used in clinical routine laboratories due to its simplicity, universality, efficacy, and affordability for fungal species identification. However, Sanger sequencing fails to identify mixed ITS sequences in the case of mixed infections. To overcome this limitation, different high-throughput sequencing technologies have been explored. The nanopore-based technology is now one of the most promising long-read sequencing technologies on the market as it has the potential to sequence the full-length ITS region in a single read. In this study, we established a workflow for species identification using the sequences of the entire ITS region generated by nanopore sequencing of both pure yeast isolates and mocked mixed species reads generated with different scenarios. The species used in this study included Candida albicans (n = 2), Candida tropicalis (n = 1), Nakaseomyces glabratus (formerly Candida glabrata) (n = 1), Trichosporon asahii (n = 2), Pichia kudriavzevii (formerly Candida krusei) (n = 1), and Cryptococcus neoformans (n = 1). Comparing various methods to generate the consensus sequence for fungal species identification, the results from this study indicate that read clustering using a modified version of the NanoCLUST pipeline is more sensitive than Canu or VSEARCH, as it classified species accurately with a lower abundance cluster of reads (3% abundance compared to 10% with VSEARCH). The modified NanoCLUST also reduced the number of classified clusters compared to VSEARCH, making the subsequent BLAST+ analysis faster. Subsampling of the datasets, which reduces the size of the datasets by approximately tenfold, did not significantly affect the identification results in terms of the identified species name, percent identity, query coverage, percentage of reads in the classified cluster, and the number of clusters. The ability of the method to distinguish mixed species within sub-populations of large datasets has the potential to aid computer analysis by reducing the required processing power. The herein presented new sequence analysis pipeline will facilitate better interpretation of fungal sequence data for species identification.
Identifiants
pubmed: 37674240
doi: 10.1186/s43008-023-00125-6
pii: 10.1186/s43008-023-00125-6
pmc: PMC10483712
doi:
Types de publication
Journal Article
Langues
eng
Pagination
18Subventions
Organisme : National Research Council of Thailand
ID : N11A650143
Organisme : Matching fund, Rachadapisek Sompoch
ID : RA-MF 47/64
Informations de copyright
© 2023. International Mycological Association.
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