Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing
3
Issued Date
2023-12-01
Resource Type
ISSN
22106340
eISSN
22106359
Scopus ID
2-s2.0-85170059053
Journal Title
IMA Fungus
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
IMA Fungus Vol.14 No.1 (2023)
Suggested Citation
Langsiri N., Worasilchai N., Irinyi L., Jenjaroenpun P., Wongsurawat T., Luangsa-ard J.J., Meyer W., Chindamporn A. Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing. IMA Fungus Vol.14 No.1 (2023). doi:10.1186/s43008-023-00125-6 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/90009
Title
Targeted sequencing analysis pipeline for species identification of human pathogenic fungi using long-read nanopore sequencing
Author's Affiliation
Siriraj Hospital
Curtin Medical School
UAMS College of Medicine
Chulalongkorn University
The University of Sydney
Thailand National Center for Genetic Engineering and Biotechnology
Faculty of Medicine and Health
Faculty of Medicine, Chulalongkorn University
Westerdijk Fungal Biodiversity Institute - KNAW
Curtin Medical School
UAMS College of Medicine
Chulalongkorn University
The University of Sydney
Thailand National Center for Genetic Engineering and Biotechnology
Faculty of Medicine and Health
Faculty of Medicine, Chulalongkorn University
Westerdijk Fungal Biodiversity Institute - KNAW
Other Contributor(s)
Abstract
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.