Improving Species-Level Resolution of Harmful Algae Using PacBio HiFi Long-Read Metagenomic Sequencing: A Case Study on Pseudo-nitzschia
ID:25 View Protection:ATTENDEE Updated Time:2026-08-31 11:59:23 Hits:16 Oral Presentation

Start Time:2027-01-13 14:15(Asia/Shanghai)

Duration:15min

Session:S2 Session 2 - Harmful Algal Blooms in the Asia Pacific: Recent Advances in Taxonomy, Biodiversity, Ecophysiology, Rapid Detection, and Early Warning to Mitigate Impacts of Coastal Ecosystem Health and Seafood Safety » S2-5Harmful Algal Blooms in the Asia Pacific: Recent Advances in Taxonomy, Biodiversity, Ecophysiology, Rapid Detection, and Early Warning to Mitigate Impacts of Coastal Ecosystem Health and Seafood Safety

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Abstract
Accurate species-level identification is essential in harmful algal research, as closely related harmful algal species may differ in toxicity, bloom-forming potential and ecological impact. Illumina short-read sequencing is widely used for harmful algae detection, but partial markers such as SSU/18S, ITS or LSU/28S may require multi-marker confirmation and can produce inconsistent or incomplete taxonomic signals. PacBio HiFi long-read sequencing provides a useful alternative by generating highly accurate reads that can span multiple informative rDNA regions within a single sequence. In this study, Pseudo-nitzschia was used as a representative harmful algal genus to evaluate the ability of PacBio HiFi metabarcoding to improve species-level identification. A total of 600 seawater samples collected across the coastal waters of China between 2019 and 2024 were analysed using PacBio HiFi long-read metabarcoding of the ITS1–5.8S–ITS2 and LSU(D1–D2) rDNA regions. Taxonomic assignments were evaluated using a BLAST-based framework based on covered marker regions, percentage identity, query coverage and alignment length. After excluding potential reverse-complement duplicate matches, most unique top-hit Pseudo-nitzschia OTUs covered the full ITS1–5.8S–ITS2–LSU(D1–D2) region, while smaller proportions were represented by ITS-only, LSU-only or other partial-region matches. Full ITS–LSU hits provided the strongest species-level evidence because this region combines the highly variable ITS regions with LSU phylogenetic information. However, LSU-only and partial-region hits were also informative, supporting the detection of additional Pseudo-nitzschia lineages, particularly when full-region reference sequences were limited. Lower-coverage assignments were further evaluated and distinguished using ITS1–ITS2 secondary-structure information, including compensatory base changes (CBC), hemi-CBC and sequence–structure phylogenetic analyses, to refine species or species-complex boundaries. Using this confidence-based framework, multiple Pseudo-nitzschia species were strongly supported, while lower-coverage matches were retained as species-complex, candidate lineage or nearest-match assignments. Overall, this study demonstrates that PacBio HiFi long-read metabarcoding improves harmful algal species identification by integrating information from multiple rDNA regions in a single sequencing approach. This framework provides stronger species-level resolution from full ITS–LSU reads while retaining useful taxonomic signals from LSU-only and partial ITS-region matches, offering a more reliable approach for resolving closely related harmful algal taxa than conventional short-marker approaches alone.
 
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Speaker
Kieng Soon Hii
Senior scientist Sarawak Infectious Disease Centre, Bachok Marine Research Station, Institute of Ocean and Earth Sciences, Universiti Malaya

Submission Author
Kieng Soon Hii Sarawak Infectious Disease Centre, Bachok Marine Research Station, Institute of Ocean and Earth Sciences, Universiti Malaya
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Important Date
  • Conference Date

    Jan 12

    2027

    to

    Jan 15

    2027

  • Jul 21 2026

    Draft paper submission deadline

  • Jan 15 2027

    Registration deadline

Sponsored By
State Key Laboratory of Marine Environmental Science, Xiamen University (MEL)
Department of Earth Sciences, National Natural Science Foundation of China (NSFC)
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