Best practices in metabarcoding of fungi: From experimental design to results

Leho Tedersoo, Mohammad Bahram, Lucie Zinger, R. Henrik Nilsson, Peter G. Kennedy, Teng Yang, Sten Anslan, Vladimir Mikryukov

Research output: Contribution to journalReview articlepeer-review

83 Scopus citations

Abstract

The development of high-throughput sequencing (HTS) technologies has greatly improved our capacity to identify fungi and unveil their ecological roles across a variety of ecosystems. Here we provide an overview of current best practices in metabarcoding analysis of fungal communities, from experimental design through molecular and computational analyses. By reanalysing published data sets, we demonstrate that operational taxonomic units (OTUs) outperform amplified sequence variants (ASVs) in recovering fungal diversity, a finding that is particularly evident for long markers. Additionally, analysis of the full-length ITS region allows more accurate taxonomic placement of fungi and other eukaryotes compared to the ITS2 subregion. Finally, we show that specific methods for compositional data analyses provide more reliable estimates of shifts in community structure. We conclude that metabarcoding analyses of fungi are especially promising for integrating fungi into the full microbiome and broader ecosystem functioning context, recovery of novel fungal lineages and ancient organisms as well as barcoding of old specimens including type material.

Original languageEnglish (US)
Pages (from-to)2769-2795
Number of pages27
JournalMolecular ecology
Volume31
Issue number10
DOIs
StatePublished - May 2022

Bibliographical note

Funding Information:
We thank Ning Daliang, Jizhong Zhou and Brendan Furneaux for sharing data and Liis Tiirmann for editing the manuscript. We are indebted to three anonymous referees who helped us to improve the manuscript substantially. The core funding is provided from the Horizon Europe EuropaBon project and Estonian Science Foundation (grant PRG632).

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Keywords

  • biodiversity
  • bioinformatics
  • community ecology
  • experiment setup
  • molecular identification
  • statistical analyses

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