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What Quality Suffices for Nanopore Metabarcoding? Reconsidering Methodology and Ectomycorrhizae in Decaying Fagus sylvatica Bark as Case Study

Summary

This study shows that Nanopore DNA sequencing can reliably identify fungi in decaying wood. Researchers developed guidelines for quality filtering of Nanopore data to ensure accurate identification of fungal species. They found that specific mycorrhizal fungi, particularly Laccaria amethystina and Tomentella sublilacina, colonize young beech trees growing on decaying logs and help them obtain nutrients.

Background

Nanopore sequencing has improved to over 99% raw read accuracy, making it a potential tool for fungal metabarcoding using full-length ITS regions. However, guidelines on quality filtering are lacking for reliable taxonomic unit recovery. This study addresses the need for quality filtering standards in fungal metabarcoding and applies them to ectomycorrhizal fungi in decaying beech bark.

Objective

To establish guidelines for quality filtering of Nanopore metabarcoding data using two approaches: reference-based mapping with UNITE species hypotheses and de novo 98% OTU clustering. To apply these methods to identify ectomycorrhizal fungi colonizing Fagus sylvatica saplings growing on decaying logs.

Results

For the OTU approach, filtering at ≥Q25 is recommended; for the SH approach, strict mapping criteria without Phred-based filtering is recommended. Decay gradient was the primary determinant of fungal community composition. Laccaria amethystina and Tomentella sublilacina were identified as key ectomycorrhizae of saplings on decaying logs.

Conclusion

Both metabarcoding approaches are effective with improved Nanopore data. The SH approach using UNITE’s species hypothesis system enhances reproducibility and cross-study communication. Nanopore sequencing provides valuable ecological insights for fungal community studies and supports broader adoption in fungal metabarcoding.
  • Published in:Journal of Fungi,
  • Study Type:Methodological Study with Case Study Application,
  • Source: 10.3390/jof10100708, PMID: 39452660
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