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Fungal community analysis by high‐throughput sequencing of amplified markers – a user's guide
Author(s) -
Lindahl Björn D.,
Nilsson R. Henrik,
Tedersoo Leho,
Abarenkov Kessy,
Carlsen Tor,
Kjøller Rasmus,
Kõljalg Urmas,
Pennanen Taina,
Rosendahl Søren,
Stenlid Jan,
Kauserud Håvard
Publication year - 2013
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.12243
Subject(s) - biology , dna sequencing , identification (biology) , computational biology , selection (genetic algorithm) , arbuscular mycorrhizal fungi , data science , ecology , genetics , computer science , dna , machine learning , immunology , inoculation
Summary Novel high‐throughput sequencing methods outperform earlier approaches in terms of resolution and magnitude. They enable identification and relative quantification of community members and offer new insights into fungal community ecology. These methods are currently taking over as the primary tool to assess fungal communities of plant‐associated endophytes, pathogens, and mycorrhizal symbionts, as well as free‐living saprotrophs. Taking advantage of the collective experience of six research groups, we here review the different stages involved in fungal community analysis, from field sampling via laboratory procedures to bioinformatics and data interpretation. We discuss potential pitfalls, alternatives, and solutions. Highlighted topics are challenges involved in: obtaining representative DNA / RNA samples and replicates that encompass the targeted variation in community composition, selection of marker regions and primers, options for amplification and multiplexing, handling of sequencing errors, and taxonomic identification. Without awareness of methodological biases, limitations of markers, and bioinformatics challenges, large‐scale sequencing projects risk yielding artificial results and misleading conclusions.