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\begin{document}

\title{Best practices in metabarcoding of fungi: from experimental design to
results}



\author[1]{Leho Tedersoo}%
\author[1]{Mohammad Bahram}%
\author[2]{Lucie Zinger}%
\author[3]{Henrik Nilsson}%
\author[4]{Peter Kennedy}%
\author[5]{Teng Yang}%
\author[6]{Sten Anslan}%
\author[7]{Vladimir Mikryukov}%
\affil[1]{University of Tartu}%
\affil[2]{Institut de Biologie de l’ENS (IBENS), Département de Biologie, École normale supérieure, CNRS, INSERM, Université PSL, Paris, France}%
\affil[3]{University of Gothenburg}%
\affil[4]{University of Minnesota}%
\affil[5]{Chinese Academy of Sciences}%
\affil[6]{Institution of Ecology and Earth Sciences}%
\affil[7]{Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences}%


\vspace{-1em}



  
  \date{June 07, 2024}


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\selectlanguage{english}
\begin{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 about current best practices in metabarcoding analysis of
fungal communities, from experimental design through molecular and
computational analyses. By re-analysing published datasets, we find that
operational taxonomic units (OTUs) outperform amplified sequence
variants (ASVs) in recovering fungal diversity, which 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 with the ITS2 subregion. We conclude that
metabarcoding analyses of fungi are especially promising for co-analyses
with the functional metagenomic or transcriptomic data, integrating
fungi in the entire microbiome, recovery of novel fungal lineages and
ancient organisms as well as barcoding of old specimens including type
material.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Fungi have traditionally been identified based on macro- and
micromorphological features of fruiting body specimens or pure cultures.
The introduction of molecular techniques established in the late 1980s
represented a significant leap forward in fungal identification.
Especially PCR amplification combined with Sanger sequencing of the
nuclear 18S (SSU) and 28S (LSU) ribosomal rRNA genes and the nuclear
ribosomal internal transcribed spacer (ITS) region from fungal tissue
(e.g., lichen thalli, lesions in tissue, cultures from environmental
samples and ectomycorrhizal root tips) quickly became popular and
offered unprecedented taxonomic resolution. Common uses included
species- and genus-level identification, analysis of cryptic species,
and phylogenetic assessment of larger fungal clades as well as the
kingdom Fungi at large (Gherbawy \& Voitk, 2010). Later on, the
identification of multiple fungi from more diverse substrates, including
soil, plant roots, and water became possible by including a cloning step
of amplicons prior to sequencing. However, these studies usually
operated with tens to low hundreds of reads, rarely numbering in the
thousands required to appropriately estimate fungal diversity in soils
(Taylor et al., 2014). Accordingly, sequences and operational taxonomic
units (OTUs) were usually handled manually or using specific programs,
with no need for bioinformatics tools.

The development of high-throughput sequencing (HTS) methods such as 454
pyrosequencing (454 Inc., obsolete), Illumina sequencing (Illumina Inc.,
www.illumina.com) and Ion Torrent (Thermo Fisher Scientific Inc.,
www.thermofisher.com) transformed fungal identification capacity in the
2000s (Jumpponen \& Jones, 2009). These so-called next- or
second-generation HTS methods increased the number of reads by 2-6
orders of magnitude and the number of simultaneously processable samples
by 1-2 orders of magnitude. These metabarcoding methods (cf. Taberlet et
al., 2012) enabled estimating fungal diversity exhaustively from
environmental DNA (eDNA) on an individual sample scale as well as
facilitating global scale comparisons (Tedersoo et al., 2014; Sun et
al., 2021). Yet, these second-generation platforms as well as the more
recent DNA nanoball sequencing (DNBseq; MGI-Tech Inc., www.mgitech.com)
were only able to address short (\textless{}550 bases) fragments of the
genetic markers, resulting in the loss of taxonomic resolution and
phylogenetic information as well as difficulties in identifying
technical artefacts compared with longer Sanger reads.

In the 2010s, long-read, third-generation HTS platforms such as PacBio
single-molecule real-time (SMRT) sequencing (Pacific BioSciences Inc.,
www.pacbio.com) and nanopore sequencing (Oxford Nanopore Technologies
Inc., https://nanoporetech.com) were introduced (van Dijk et al., 2019).
Due to low sequencing depth (tens of thousands of reads in total,
resulting in only hundreds rather than thousands of reads per sample)
and high raw error rates (12-20\%), these methods could not initially
compete with short-read HTS platforms. However, both technologies made a
great leap forward in 2020 when PacBio Sequel II instruments became
broadly available and new solutions were developed to greatly reduce
error rates in Nanopore sequencing (Karst et al., 2021; Tedersoo et al.,
2021a). These long-read technologies and synthetic long reads provide
high-quality sequence data for up to 5 kb amplicons, which enables
bridging variable and conserved fragments of one or more genes in a
single sequencing round as well as resolving alleles and haplotypes
(Callahan et al., 2021; Tedersoo et al., 2021a).

Along with the rapid development of HTS methods, bioinformatics
platforms and analytical resources have evolved to match the
computational needs imposed by large datasets. Metabarcoding approaches
have been extensively reviewed in several recent studies with a focus on
their conceptual foundation (Taberlet et al., 2018), pathogenic
organisms (Piombo et al., 2021; Tedersoo et al., 2019), applications in
mycology (Nilsson et al., 2018), eukaryotes more broadly (Ruppert et
al., 2019 ) as well as overall experimental planning (Zinger et al.,
2019a), trade-offs among technology generations (Kennedy et al., 2018;
Loit et al., 2019), and analytical pitfalls (Halwachs et al., 2017;
Critescu \& Hebert, 2018). Here we provide a review of available methods
and propose best practices for designing and performing studies using
metabarcoding in fungi. We also compare the performance of several
popular methods developed for bacteria to assess their suitability for
fungi. The vast majority of our recommendations are relevant to
prokaryotes, protists and metazoans alike.

\textbf{Planning a metabarcoding study}

To test scientific hypotheses, researchers should first consider a
proper methodological experimental design -- either observational,
experimental or combined -- including technical, analytical and
financial requirements. Experimental designs of broad representativeness
(e.g., geographical and ecological scope) and independence of replicates
(i.e., no spatiotemporal autocorrelation) are strongly recommended
(Gotelli \& Ellison, 2013; Zinger et al., 2019a). Indeed, metabarcoding
studies do not differ from traditional ecological studies in which the
number and distribution of study sites must be defined appropriately
depending on the initial question (Dickie et al., 2018). Additionally,
metabarcoding studies also require an optimal number of local,
biological replicates, ranging from at least three (for detecting strong
differences in composition) to around 10 (for richness assessment).
Intuitively, more replicates will be required when any expected
ecological differences are relatively small or when the studied location
exhibits strong spatial or environmental heterogeneity.

The size of individual environmental samples should be large enough to
secure enough material for DNA extraction and potential physicochemical
analysis (e.g., pH and C/N ratio). It is also important to consider the
amount of material from the perspectives of pre-treatment and storage.
Too much material will be difficult to mix, dry or freeze - and will
prove costly to preserve in a buffer. To ensure statistical independence
of samples within a site, samples should be located at least 5-10 m
apart from each other in a homogeneous environment. This distance
corresponds to the spatial autocorrelation range in soil fungi (Bahram
et al., 2013) and the average size of macrofungal individuals (Douhan et
al., 2011). In aquatic habitats, communities are likely to
compositionally autocorrelate for even larger distances (Matsuoka et
al., 2019). When assessing diversity patterns along ecological
gradients, transects (e.g., latitudinal, altitudinal and salinity
gradients) should be replicated. Spatial independence should also be
ascertained for plots and treatments. In field and laboratory
experiments, this is best achieved by a randomized block design
(Legendre \& Legendre, 2012), although a stratified block design may be
important in environments with known heterogeneity. It is advisable to
collect samples in as short a time period as possible to avoid seasonal
and weather effects such as freeze-thaw cycles and rainfall after a long
dry period (for soil and leaves) that may cause rapid turnover of
microbes and degradation of their DNA by molds. All sampling locations
(including positions in controlled experiments) and sampling dates
should be recorded precisely to permit controlling for spatiotemporal
effects in the following statistical analyses (Bahram et al., 2015;
Tedersoo et al., 2020a).

\textbf{Sampling and storing}

We strongly recommend wearing disposable gloves during sampling to avoid
contaminating samples with skin or forward microbiota. To reduce the
risk of cross-contamination between independent samples in the field,
sampling tools should be replaced or sterilized between sampling events
with oxidizing agents (e.g., bleach; Fischer et al., 2016) or flame but
not with alcohols, as the latter do not denature DNA. Samples should be
collected into clean containers such as paper bags (leaf and fruiting
body material), plastic bags (roots, soil and sediments) or screw-cap
vessels (soil, water and sediments). It is recommended to sample in the
field during dry weather to avoid contamination by water from rain and
wet gloves. To enable removal of site or sampling material contaminants
a posteriori, it is also recommended to include field controls (e.g.,
empty tubes left opened at the site or extraction of sample storage
buffer) in the experiment (Zinger et al., 2019a). Finally, it is
important to limit the biological activity within samples post-harvest
(i.e., growth of fast-growing molds), which can be done by maintaining
the samples at cold temperature during transport.

To obtain good quality DNA, the best option is to either extract DNA
right after sampling whenever possible or to rapidly freeze the
collected materials in liquid nitrogen and maintain them at an
ultra-cold temperature (-80 \selectlanguage{ngerman}°C; U'ren et al., 2014). Pooled subsamples
should be well mixed before freezing, because it may subsequently be
difficult to homogenize frozen material, which could lead to some
subsamples effectively being excluded from DNA extraction. When
freezing, it is important to avoid thawing, which may lead to sample
spoilage and significant changes in the detected communities (Anslan et
al., 2021, Clasen et al., 2020). Long-term storage (2-4 weeks) at 4 °C
may alter soil fungal diversity (Delavaux et al., 2020) and promote
proliferation of molds (Clasen et al., 2020). Rapid drying methods such
as freeze drying and cabinet drying are alternatives to freezing to
prevent DNA degradation (Castano et al., 2016). Rapid drying can also be
conducted under e.g., the windscreen of a car (at \textless{} 40 °C) if
a laboratory is inaccessible. Drying with silica gel is a viable option
for samples of a few grams (but see Guerrieri et al., 2021 for larger
amounts) and will also work well for plant material such as thin leaves
and fine roots. Conversely, and importantly, liquid preservatives such
as cetyltrimethylammonium bromide (CTAB), ethanol, and specific DNA/RNA
preservation solutions perform poorly for above-gram samples (e.g.,
Delavaux et al., 2020; Zaiko et al., 2021). Nevertheless, Longmire
buffer (100 mm Tris, 100 mm ethylenediaminetetraacetic acid, 10 mm NaCl,
0.5\% sodium dodecyl sulphate, 0.2\% sodium azide; 1:1 vol/vol) works
well for sediment and water samples, in which DNA otherwise would
degrade very rapidly (Kumar et al., 2020). Dried material and samples
fixed in buffers can be kept in the dark at room temperature. Samples
can be stored long-term (decades) if kept air-tight in the dark and at
constant temperature (Wang et al., 2021). It is also essential to store
DNA samples, preferably in frozen or lyophilised form, for potential
subsequent quality check, re-analyses for other research purposes or
simply for a re-analysis using more sophisticated HTS methods in
temporal studies (Jarman et al., 2018). Drying or lyophilisation is
essential for pooled samples of coarse fragmented materials (e.g., wood
chips and plant litter) which require grinding for adequate mixing.

\textbf{Molecular analyses}

DNA extraction

Prior to DNA extraction, it is important to homogenize the material by
using a mortar and pestle, micropestles or bead beating in
microcentrifuge tubes. In this process, keeping samples at a relatively
cool temperature by using ice cubes or liquid nitrogen is also
important. The required amount of material should be weighted to the DNA
extraction tube and the rest could be stored for backup or for e.g.,
stable isotopes or chemistry analyses. It is usually undesirable to
reach the full capacity of the DNA extraction kit, because several types
of samples (e.g., peat soils, dead wood, plant-debris rich sediments and
fleshy plant tissue) may absorb the liquid or co-extract inhibitors. For
well-homogenized soil samples, there are only minor differences in
richness when using DNA extracts from 0.25 g, 1 g or 10 g material (Song
et al., 2015), but increasing the volume (replicate extractions or more
material using `maxi' kits) provides more reproducible estimates (Dickie
et al., 2018). It is crucial to perform weighing and DNA extraction
under a dedicated laminar flow in a room separated from the PCR lab to
avoid cross-contamination and air contamination by amplicons. Such
potential contaminants can also be detected and removed in downstream
analyses through analysis of DNA extraction blank controls.

For DNA extraction, we recommend to follow the protocols elaborated for
relevant substrates, either manual methods or commercial kits. The CTAB
and phenol-chloroform protocols (multiple variants exist) are the most
broadly used manual methods for obtaining large quantities of long DNA
molecules. While the quantity of DNA from the aforementioned protocols
is usually relatively large, it is often less pure than kit-based kits
and so may require further dilution ahead of PCR to minimize the effect
of inhibitors present in the sample (see below). Because of functional
limitations in DNA extraction robots, the DNA purity and yield obtained
with these protocols tend to be greater with analogous non-robot kits.
As a rule of thumb, commercial non-robot and robot-based kits are
roughly 2 and 5 times more time-efficient, but 2-10 times more costly
compared with manual protocols.

Depending on the sample type and extraction method, the DNA may contain
impurities that hamper PCR amplification. These can be overcome by
pretreatment of samples during DNA extraction (e.g.,
Al\textsuperscript{3+} or Ca\textsuperscript{2+} flocculation of humic
substances), purification using specific kits (e.g.,
polyvinylpolypyrrolidone spin columns against humic and fulvic acids in
soil; universal Zymo Research OneStep PCR Inhibitor Removal Kit or
Macherey-Nagel NucleoSpin Inhibitor Removal Kit against polyphenolics,
humic and fulvic acids, tannins, and melanin) or equipment (e.g., SCODA
electrophoresis), or precipitation with ethanol. Importantly, under most
conditions, dilution of the DNA extracts may be sufficient to eliminate
PCR inhibition (Wang et al., 2017). DNA concentrations can be increased
by precipitation with ethanol, salts (e.g., sodium acetate) and carriers
(e.g., Pellet Paint Co-Precipitant, glycogen, or linear polyacrylamide).
If the DNA extract contains a large proportion of short fragments (e.g.,
degraded DNA due to poorly preserved samples or extracellular DNA),
which hamper amplification and may promote chimera formation, these can
be removed by elution using specific kits such as AMPure (Beckman
Coulter Inc., \emph{www.beckman.com}) and ProNex (Promega
Corp.,\emph{www.promega.com}). Extracellular DNA may account for 80\% of
total DNA, but it has little effect on estimates of diversity, as it
comes from the dead cells of indigenous biota (Lennon et al., 2018).
However, it is crucial to remove extracellular DNA for time series and
co-occurrence analyses on a microscale (Lennon et al., 2018), which can
be performed by sample treatment with ethidium or propidium monoazide
(Wagner et al., 2008). Substrates destined for mesh-bag experiments
(plant litter, wood) can be relieved from unnecessary microbial DNA with
gamma-irradiation (Brabcova et al., 2016) or dry heating at
\textgreater{}120 °\selectlanguage{russian}С before exposure.

Control samples

Control samples -- negative controls for sampling, DNA extraction and
PCR, and positive controls (including mock communities) improve
scientific reproducibility by offering means by which to estimate the
accuracy of the analyses (reviewed in Zinger et al., 2019a). Negative
and positive controls all inform about external and cross-contamination
as well as potential index-switching (Carlsen et al., 2012, Esling et
al., 2015). Amplification and sequencing of mock community analysis
provide additional insights into the qualitative (i.e., estimation of
PCR/sequencing error rates) and quantitative capacity (i.e., biased
amplification) to retrieve the original diversity. Positive controls and
mock communities may consist of artificial synthesised molecules or DNA
extracts of actual species known not to occur in the experimental system
(Ihrmark et al., 2012; Song et al., 2015). A sophisticated mock
community should comprise \textgreater{}10 species with variable G+C
content, amplicon length and quantity based on actual marker copy
numbers. Additionally, due to index switching issues, consideration of
the specific species composition of the mock community is desirable.
Specifically, if the mock community contains the same taxa present in
the samples being analyzed, it becomes impossible to determine whether
any switched reads in the samples come from the mock community or not.
One solution to this problem is to use a non-biological mock community,
containing multiple ``species'' that are synthetically constructed to
have properties equivalent to biological species but never that are
present in nature (Palmer et al., 2018). Mock community analyses
commonly fail to recover all species and usually reveal more OTUs than
the input because of variable DNA quality, PCR bias, trace contamination
and index-switching (Ihrmark et al., 2012; Bakker, 2018). Therefore,
failing to recover the initial mock community does not necessarily
indicate that the analyses have failed, but it sheds light on potential
biases and serves as a reference to correct the data through
bioinformatics processing.

Genetic markers

Obtaining high-quality amplicons is one of the most important steps in
metabarcoding analyses. This can be ensured by selecting a suitable
genetic marker, polymerase, relevant primers and appropriate
thermocycling conditions. Each PCR run requires a negative control to
rapidly detect contamination.

The ITS region of the rRNA cistron is the most broadly used marker for
fungi in both DNA barcoding and metabarcoding analyses due to its
multiple copy numbers, optimal species-level resolution in most groups
and the possibility to design both fungal-specific and universal primers
(Schoch et al., 2012; Nilsson et al., 2018). The ITS region is unsuited
to target certain fungi such as Microsporidia (intracellular animal
parasites) that may lack this fragment and certain Tulasnellaceae
(orchid root symbionts) that have mutations in primer sites (Tedersoo et
al., 2015, Rammitsu et al., 2021). Furthermore, ITS sequences lack
variability in some species in certain species-rich genera comprising
pathogens and saprotrophs such as \emph{Trichoderma} and\emph{Fusarium}
, and their analysis requires using additional taxonomic markers,
typically protein-coding genes (O'Donnell et al., 2015; Cai \&
Druzhinina, 2021). The arbuscular mycorrhizal \emph{Glomeromycota} have
multinucleate hyphae with highly variable ITS copies, which has rendered
the rRNA 28S and 18S gene fragments of broad use as well (Kolarikova et
al., 2021). Because of sequencing read length limits imposed by
second-generation HTS platforms, researchers have mostly focused on
either the ITS1 or ITS2 subregion, which taken separately have lower
taxonomic resolution and do not offer as suitable primer sites as the
full region (Tedersoo et al., 2015; Tedersoo et al., 2021a).

For metabarcoding, ecologists use mostly primers designed decades ago
for Sanger sequencing analyses (Figure 1). These original primers are
not optimal for the many fungal groups that have one or more
primer-template mismatches. They can be improved by adding degenerate
positions to minimise primer bias (Tedersoo \& Lindahl, 2016) and
promote quantitative performance (Pinol et al., 2019). However, multiple
degeneracies may require altering the 1:1 ratio of primers and may
require extra PCR cycles, because not all variants match to templates.
The broadly used fungus-specific forward primer ITS1F is particularly
problematic because of several critical mismatches in certain groups of
molds and putative animal pathogens (Tedersoo \& Lindahl, 2016).
Researchers should also consider the common presence of an intron at the
end of 18S rRNA gene, which prevents sequencing of the taxa containing
this intron (Figure 1). It may be important to pair primers with similar
melting temperatures to secure optimal performance.

There are different amplicon library preparation strategies that require
consideration during the primer design step (Figure 2). The
metabarcoding primers may be equipped with both sample-specific index
and platform-specific adapters for sequencing. The alternative strategy
is to use shorter primers with only sample-specific indexes, which are
ca 30-40\% cheaper and easier to amplify, but require specific library
preparation depending on the sequencing platform. Approaches requiring
several PCR steps are also available (Figure 2; Bohmann et al., 2021),
but these are more prone to contamination and chimera formation.
Although vulnerable to contamination, the use of combinations of
Illumina flow cell indices in the second PCR step enables ultra-high
multiplexing of samples without index-switching bias (Holm et al.,
2020).

The sample-specific indexes are typically 6-14 bases in length and
differ from each other by at least 4 nucleotides (including indels) for
error correction (Buschmann \& Bystrykh, 2013). Their GC content should
be in the range of 25-75\% and homopolymers \textgreater{}2 nucleotides
should be avoided. An example of \textgreater{}300 indexes is listed in
Taberlet et al. (2018). To reduce amplification biases, there should be
a 2-3-base linker between the index and PCR primer, which should not
align to any of the targeted sequences. The quality of Illumina
sequencing benefits from heterogeneity spacers added to the indexes
(Figure 2; Fadrosh et al., 2014). To secure more equal library
preparation, indexes should start with the same nucleotide. The same
indexes (but not linkers) can be used with multiple primers, but each
primer-index combination should be tested for hairpin structure
formation \emph{in silico} using, e.g. EcoPCR (Ficetola et al., 2010).
Indexing both primers with unique tags is more expensive, but allows
users to greatly reduce index-switching artefacts (Schnell et al.,
2015), and is therefore strongly recommended.

Polymerases

With respect to DNA polymerases, it is important to select one with
proofreading capacity in spite of their much greater cost. Proofreading
polymerases have much-reduced error rates and therefore result in fewer
spurious OTUs (Oliver et al., 2015, Bakker, 2018). The 3' to 5'
exonuclease activity of proofreading polymerases performs primer editing
in the last 4-6 nucleotide positions, reducing primer bias (Gohl et al.,
2021). However, this activity varies by polymerase and the mismatching
nucleotide and probably concentration of inhibitors (Gohl et al., 2021),
and the effect on multiple near-terminal mismatches remains unexplored.
Hence, proofreading polymerases may also strongly reduce the specificity
of taxon-specific primers. Furthermore, the exonuclease activity of
proof-reading polymerases creates multiple short fragments, especially
at low ddNTP concentration and prolongs elongation times, which may
result in more chimeras already at early stages of the PCR process (Ahn
et al., 2012). For longer amplicons, it is crucial to select
high-fidelity polymerases to secure amplification completion and hence
reduce production of chimeric artefacts (Heeger et al., 2018). Thus, a
wise selection of primers and polymerases allows researchers to obtain
the same amount of high-quality data with lower sequencing depth.

Thermal cycling conditions

Regarding PCR cycling conditions, reducing annealing temperature may
promote amplification of targeted taxa that have one or more
primer-template mismatches, but it may also enhance non-specific
priming, resulting in amplification of random genomic fragments or
untargeted taxa. The number of PCR cycles should be kept at below 30 --
optimally resulting in a weak band on a gel (Lindahl et al., 2013;
D'Amore et al., 2016). Rather than losing samples with no visible
amplicons, it is advised to add a few extra cycles to problematic
samples, but users should keep in mind that these low-input or
inhibitor-rich samples have elevated risk of contamination or biased
diversity patterns (Eisenhofer et al., 2019). Adding BSA may be useful
for improving amplification success, but this process may distort the
retrieved community (Zaiko et al., 2021).

Biological samples may differ in several orders of magnitude in their
DNA content, quality, and abundance of inhibitors. For PCR, the DNA
content is rarely equalised, because typically 80-99\% of eDNA is
non-fungal, and the fungal fraction may vary significantly across
samples (Tedersoo et al., 2015; Bahram et al., 2018). There is no
consensus on whether or how the DNA quantity should be standardised,
although diluted samples may yield a higher proportion of contaminants
(Lindahl et al., 2013) as well as relatively lower diversity and greater
variability (Castle et al., 2018, but see Song et al., 2015 and Wang et
al., 2017). Therefore, at least two PCR replicates are needed to account
for the stochasticity. Such technical replicates can be pooled for
further analysis steps (Lindahl et al., 2013; Alberdi et al., 2018), but
this pooling step will prevent evaluation of the PCR replication and
exclusion of dysfunctional PCRs (Taberlet et al., 2018).

Alternatives to traditional eDNA amplicon-based methods

To focus on the active community, RNA instead of DNA can be used as a
target for sequencing (Singer et al., 2017, but see Blazewicz et al.,
2013 for limitations). One option is to amplify reverse transcribed
cDNA, which can be performed for ITS sequences in spite of the short
life of precursor RNA (Rajala et al., 2011). Interestingly, cDNA-based
HTS reveals multiple taxa not recovered using DNA and vice versa (Rajala
et al., 2011). Another option is direct RNA sequencing, which is
currently provided only by ONT (Oxford Nanopore Technology; Garalde et
al., 2018). Both methods produce more errors than state-of-the-art
DNA-based methods. As both PacBio and ONT sequencing make it possible to
record modified nucleotides such as various methylations, it may be
possible to record various artificial nucleotide analogues (e.g.,
3-bromo-deoxyuridine) incorporated into DNA in real time (Hanson,
Allison, Bradford, Wallenstein, \& Treseder, 2008; Georgieva et al.,
2020). Stable isotope probing is widely used for bacteria because of
their rapid metabolism of 13C-enriched substrates (Berry \& Loy, 2018),
but they have been little used in mycology, likely due to the high costs
of enriched C (but see Hannula et al., 2017; Lopez-Mondejar et al.,
2020). Nevertheless, RNA-based SIP applications may offer more promise
in fungi than for bacteria (Singer et al., 2017; Ghori et al., 2019).

Metagenomics and metatranscriptomics can alternatively be used for
large-scale identification of organisms. These methods are free from PCR
biases but may be affected by library preparation biases and add an
order of magnitude to the costs (Quince et al., 2017; Singer et al.,
2017). While these methods work reasonably well on bacteria and viruses
with small and densely packed genomes and for which a rich set of
reference genomes are available, analyses of fungi and other eukaryotes
are heavily biased because of highly different genome sizes, number of
rRNA gene copies and the striking lack of reference genomes for many
important groups (Geisen et al., 2015; Tedersoo et al., 2015). This may
change very soon within the ongoing Earth Biogenome project
(www.earthbiogenome.org) and use of taxonomically more informative long
reads. It may also be possible to use targeted capture for rRNA genes or
other taxonomically and functional genes to sequence these using
long-read protocols (Witek et al., 2016), but the analytical costs are
approximately five-fold the costs of regular metabarcoding.

As an alternative to taxon-specific primers, it is possible to use
blocking protein-nucleic acid complexes (PNAs) or locked nucleic acid
(LNA) oligonucleotides in conjunction with universal PCR primers
(Vestheim et al., 2011). PNAs are widely used in metabarcoding analyses
of plant-associated bacteria to block amplification and disable
subsequent sequencing of plastid and mitochondrial DNA (Lundberg et al.,
2013). Probably partly because of primer sites at the end of 18S rRNA
gene that allow discrimination against plant amplicons, blocking
elements have found limited use in metabarcoding of fungi (but see
Ikenaga et al., 2016), although plant-specific motifs exist in all of
the 18S, 5.8S, and 28S rRNA genes. Banos et al. (2018) developed
protist-targeting PNAs for fungal communities in aquatic environments.
The use of blocking elements requires optimisation of concentration and
annealing temperature for each primer pair and polymerase used (Vestheim
et al., 2011) and furthermore necessitates double-checking any shifts in
the perceived diversity of fungi or other target organisms.

DNA library preparation

Among-sample variability of amplicon quantity is high at a low number of
PCR cycles. Therefore, the amount of amplicons should be standardised
for improved comparability of sequencing depths. This can be achieved by
DNA capture on a solid phase with limited binding capacity (SequalPrep,
Thermo Fisher Technologies), DNA content measurement and normalisation,
or simple estimates of the band strength on agarose gel by eye.

The equimolarly pooled samples are subjected to library preparation
using HTS platform-specific kits. Aside from multiple kits for Illumina,
those free from amplification steps and biases of G+C content and
fragment length are recommended (Bowers et al., 2015; Sato et al.,
2019). Amplicons produced by different primers, even when of similar
length, should not be mixed into the same library because of great
differences in yield (Tedersoo et al., 2015). In-house library
preparation may be up to 5-fold cheaper compared to commercial services.

Sequencing platforms

For metabarcoding, both the second-generation and third-generation
platforms can be considered (reviewed in Tedersoo et al., 2021a).
Currently, the second-generation platforms allow sequencing up to ca.
550 base pair markers, but their throughput exceeds that of
third-generation platforms by 1-2 orders of magnitude and their costs
per base are at least an order of magnitude lower. Given their relative
accuracy, Illumina (HiSeq and NovaSeq instruments in 2 x 250 paired-end
mode and MiSeq) and MGI-Tech (DNBSEQ-G400RS in 2 x 200 paired-end mode)
are best suited for analyses of short barcodes such as ITS1, ITS2 or one
or two variable regions combined within 18S and 28S rRNA genes.

The average raw read length of PacBio and ONT instruments exceeds 20 kb.
The libraries of PacBio consist of circularised amplicons, which are
sequenced multiple times (circular consensus sequencing; CCS) and error
rates decrease from 10-15\% to \textless{}0.1\% at \textgreater{}10-fold
consensus. This allows high-quality sequencing of up to 3.5 kb fragments
that cover multiple markers at high quality. Such long reads offer much
improved taxonomic resolution and allow rigorous phylogenetic analyses
based on reasonably long alignments of conserved regions (Tedersoo et
al., 2020b). Furthermore, random PCR and sequencing errors are typically
ironed out during the clustering process (Tedersoo et al., 2018), and
much of the relatively more degraded extracellular DNA is excluded.

Currently, ONT sequencing does not offer sufficient read quality for
metabarcoding. Although unique molecular identifiers (UMIs) can be used
in the generation of consensus sequences (Figure 2e; Karst et al.,
2021), obtaining at least 20-fold consensus will reduce throughput and
increase the overall cost tremendously. UMIs can also be used for
producing synthetic long reads using any of the short-read platforms,
which results in principally error-free long reads (Callahan et al.,
2021). However, a new commercial LoopSeq service provided by Loop
Genomics, Inc. (\emph{www.loopgenomics.com}) is relatively costly
(43-100 USD/sample). Taken together, the choice of HTS strategy depends
on expected data quality, number of samples included, desired sequencing
depth and amplicon length as well as available financial resources
(Tedersoo et al., 2021a).

\textbf{Bioinformatics data analysis}

Quality-filtering

The raw output of sequencing instruments is converted to the FASTQ
format, which is compatible with all major quality-filtering tools. As
most bioinformatics platforms have been developed for bacterial 16S
data, these differ greatly in their capacity of handling fungal ITS
sequences that typically cannot be reliably aligned much beyond the
genus level (Anslan et al., 2018). Based on citations, the most broadly
used platforms include QIIME2, mothur, PIPITS, SEED2, SCATA and
PipeCraft. Features as well as pros and cons of the most popular and
recently developed platforms are presented in Table 1.

For Illumina and MGI-Tech instruments that produce paired-end reads, it
is recommended to assemble the paired-end reads, unless the amplicon is
longer than the paired reads combined (e.g., Bissett et al., 2016).
Generally it is advisable to disregard unpaired reads because of the
risk of low read quality in their distal end.

When it comes to quality-filtering, a major step is simultaneous
demultiplexing, trimming primer and index sequences and removal of
low-quality and non-target reads. Any ambiguous nucleotides and
mismatches to indexes or primers are indicative of potentially low read
quality and these reads could be excluded. Dual-indexed reads with
mismatching pairs or a missing index from one primer are indicative of
index switching and incomplete sequence data, respectively. The expected
length of full-length ITS ranges from 250 (some\emph{Saccharomycetales}
) to around 1,500 bases (e.g., some\emph{Cantharellales} and various
unicellular groups), but\emph{Microsporidea} may have only a few bases
of rudimentary ITS sequences. The ITS1 and ITS2 subregions taken
separately vary from 50 to around 1000 bases. There is also great length
variation in 18S and 28S rRNA genes, which is mostly ascribed to
introns.

Some 16S-based workflows recommend removal of homopolymers
\textgreater{}6 bases (e.g., default in QIIME2), but the ITS region of
many fungal and other eukaryotic taxa commonly harbour homopolymers
exceeding 10 bases. Hence, homopolymer length should not be used as an
indicator in quality-filtering for ITS sequences and other non-coding
regions.

For ITS metabarcoding, it is important to remove flanking 18S and 28S
rRNA genes, because these conserved ends display no species-level
resolution and random errors in these regions complicate clustering
(Lindahl et al., 2013). Furthermore, chimeric breakpoints may be common
in these regions but are nearly impossible to recognize from such short
fragments (e.g., 10-70 bases). ITS extraction can be performed using
programs cutadapt (Martin et al., 2011), ITSx (Bengtsson-Palme et al.,
2013) or ITSxpress (Rivers et al., 2018). Cutadapt is relatively more
universal, as it recognizes custom oligonucleotides and cuts from a
user-defined distance. ITSx and ITSxpress cut out ITS1, ITS2 and
full-length ITS region based on kingdom-wide Hidden Markov Models
(HMMs).

Chimeric molecules are mainly generated in the excessive cycles of PCR
and are therefore nearly always less abundant than their parent
molecules (Sze \& Schloss, 2019). They are usually represented by
singletons and doubletons restricted to a single sample. There are
multiple algorithms for chimera detection, of which UCHIME (Edgar et
al., 2011) is by far the most universal and widely used. It is
recommended to perform chimera filtering both in \emph{de novo} and
reference-based modes, which compare OTUs against each other (in ranked
abundance within a sample) and against a reference database (e.g.,
UNITE), respectively. According to our experience, a vast majority of
reference-based chimeras are true chimeras, whereas ca. half of
the\emph{de novo} chimeras may be false positives, as suggested for mock
communities (Aas et al., 2017). Not all chimeras are detected by the
programs, so it may be advisable to remove all singletons or OTUs with
\textless{}5 or \textless{}10 reads in the case of deep sequencing.
Chimeric molecules usually have only a partial match to the reference
sequence (coverage 55-98\%) at high sequence similarity
(\textgreater{}90\%), which allows additional algorithm-based removal or
rare, potentially artifactual OTUs.

Index-switches (also known as tag-switches, index-jumping and index
cross-talk) is the most deleterious phenomenon in HTS data, because it
results in technical cross-contamination among samples and may blur
especially patterns in host specificity, taxon networks and
biogeographical pattern (Carlsen et al., 2012). Index switches occur
during PCR, T4 blunt ending and cross-pairing of amplicons from
different libraries, and they are known from all sequencing platforms
(Schnell et al., 2005; Car\selectlanguage{ngerman}øe \& Bohmann, 2020). Careful indexing of
samples ameliorates this issue, but roughly 0.01-0.1\% of obvious
switches will remain. Index-switches can be assessed with an \emph{ad
hoc} score using the UNCROSS algorithm (Edgar, 2017), the unspread
python script (Larsson et al., 2018), by tracking non-biological
spike-ins (Palmer et al., 2018), or by a positive control sample. Based
on the distribution of spike-ins or positive control in biological
samples in vice versa, index-switch rates can be quantified. Sequence
abundances above the index-switching threshold are converted to zero.
Index-switches and other contamination should be checked for each
sequencing library separately. It is also useful to estimate occurrence
of taxa in pseudosamples represented by unused indexes (see Taberlet et
al., 2018) to assess the proportion of index-switch artefacts.

Clustering

Clustering is used for aggregating reads into OTUs based on user-defined
sequence similarity thresholds, usually ranging from 95-100\% sequence
similarity. There are multiple clustering algorithms, many of which are
based on a global alignment or expect equal read length, and are thus
unsuited for ITS data. Most algorithms enforce strict sequence
similarity and coverage thresholds, but some allow relaxed similarity
(e.g., swarm; Mahe et al., 2021) and do not consider end-gaps (indels in
the end; e.g., SCATA, https://scata.mykopat.slu.se). Some algorithms
allow truncation of homopolymers (e.g., the algorithms implemented in
SCATA and PipeCraft), which may be warranted for the PacBio, ONT and Ion
Torrent platforms where indels in homopolymers are the most common
sequencing errors.

Three types of clustering are commonly seen in metabarcoding efforts:
open-reference, closed-reference and \emph{de novo} clustering.
In\emph{de novo} clustering, the sequence data are clustered within the
project. For closed-reference clustering and open-reference clustering,
a reference database is used, but only in the latter method, unique
sequences are retained. Open-reference greedy clustering is recommended
as it provides the most stable OTUs by accounting for the taxonomic
structure in the reference data (He et al., 2015; Cline et al., 2017).

Following studies in bacterial ecology and Sanger sequencing-based
mycorrhizal ecology, a sequence similarity threshold of 97\% is most
commonly used, especially for short reads. Other studies have found that
98\% or 98.5\% sequence similarity is a better compromise between
sequencing errors and biological differences among closely related
species and intraspecific variation (Tedersoo et al., 2014; Kyaschenko
et al., 2017). Yet, many closely related species of ascomycete
saprotrophs and pathogens differ by no or only a few bases in
full-length ITS (e.g., O'Donnell et al., 2015). Based on 18S rRNA gene
sequences, ascomycete and basidiomycete species belonging to different
orders may display identical or near-identical sequences, which render
SSU unsuited for fungal DNA metabarcoding. However, unicellular fungal
lineages and \emph{Glomeromycota} display greater variation, although
species-level distinction in the latter group is not straightforward as
most virtual taxa and OTUs are \textgreater{}50 million years old (Bruns
\& Taylor, 2016; Perez-Lamarque et al., 2020), which roughly corresponds
to the age of genera in most other fungal groups. To improve taxonomic
resolution and reproducibility of identified taxa, amplicon sequence
variant (ASV) approaches have been developed (Box 2).

Taxonomic assignments

A representative sequence is chosen from each OTU for taxonomic
annotation. Most programs select one of the longest sequences, the
consensus sequence or the most common sequence type for comparison to
the reference corpus. In most cases, the latter is biologically the most
meaningful. The longest sequence may contain artifactual insertions,
untrimmed fragments of flanking genes or represent an unrecognized
chimera. Consensus sequences are prone to lending voice to rare and
perhaps compromised sequence data.

There are different approaches for taxonomic assignments, all of which
require a well-curated reference database. The most common approach is
custom BLASTn searches (Camacho et al., 2009), where all representative
sequences are compared pairwise to sequences in the reference database.
Users can specify blast parameters from slow and stringent to rapid and
discontinuous. We recommend using word size \textless{}10 to be able to
obtain long query-to-template alignments and hence the most precise
estimates of e-value and sequence similarity. Alternative to BLASTn,
programs performing k-mer search such as Naïve Bayesian classifier (Wang
et al., 2007; Porras-Alfaro et al., 2014) and SINTAX (Edgar, 2016) are
up to 100-fold faster, but the resulting similarity estimates are not as
straightforward to interpret. These algorithms work well in situations
where reference data are abundant and accurately identified to species
level, which is a somewhat atypical situation seen to the fungal kingdom
at large (Lücking et al., 2021). Phylogenetic placement algorithms such
as EPA-ng (Barbera et al., 2018) map rRNA gene-based OTUs to
pre-established phylogenies, but these methods are not suited for the
ITS region due to its hypervariability in both length and composition.
Some pipelines (e.g., amptk) return taxonomic assessments based on
results from multiple algorithms. While often slightly more
conservative, this approach gives greater confidence in the assigned
taxonomy when there is clear congruence across different algorithms.

If the samples include many undescribed/unbarcoded species, we recommend
relying on blast search against the UNITE database, focusing on the best
hit but also accounting for 4-9 next best hits. Based on multiple global
datasets, we have developed recommended taxon-specific e-value and
sequence similarity thresholds for 18S-V9, ITS2 and full-ITS reads at
the level of genera to phyla (Tedersoo et al., 2014; 2021b; updated in
Table S1). Manual BLAST-based double-checking of taxonomic affiliations
of at least the, say, 50 largest OTUs/ASVs are a part of those
recommendations.

With respect to reference databases, UNITE is the largest by containing
curated data obtained from International Nucleotide Sequence Databases
consortium (INSDc) as well as data submitted directly to UNITE (Nilsson
et al., 2018). Furthermore, UNITE provides species hypotheses (SHs) for
ITS-based OTUs of fungi and other eukaryotes to enable unambiguous
DOI-based cross-communication of taxa among studies and across time
(Kõljalg et al., 2016, 2020). Another curated dataset is the so-called
Warcup training set, which covers a smaller set of well-identified fungi
of mostly plant-associated \emph{Ascomycota} and
\emph{Basidiomycota}(Deshpande et al., 2016). We recommend
identification of fungal and other eukaryote ITS sequences based on the
UNITE reference data set, because it is the largest curated database and
it includes multiple non-fungal reads to facilitate separation of fungi
from other eukaryotes (Anslan et al., 2018). We recommend the SILVA
database for 18S and 28S rRNA gene reads (Quast et al., 2013). For
\emph{Glomeromycota} 18S and 28S rRNA gene reads, Maarj\emph{AM} (Öpik
et al., 2010) and the AM-LSU pipeline (Delavaux et al., 2021) can be
used, respectively.

Functional assignments

Functional assignments can be supplied directly to best-hitting
reference sequences and species, genera and orders based on their
identification. The FUNGuild database is largely focused on the genus
level (occasionally on the species level) and provides functional
assignment of lifestyle and life mode based on probabilistic estimates
(Nguyen et al., 2016). The FungalTraits database is focused on genus and
order-level functional estimates covering additional traits such as
fruiting body and hymenium type and capacities of performing certain
biotic functions (Põlme et al., 2020). FungalTraits also allows
complementary trait estimates (geographic distribution, isolation source
and mycorrhizal type) based on sequence accessions and SHs. FunFun
includes a large number of genomic and enzymatic functional traits, but
its taxonomic coverage is limited (Zanne et al., 2020).

From a functional perspective, we argue that it makes the most sense to
analyse diversity at the level of all fungi and major functional guilds
such as ectomycorrhizal, arbuscular mycorrhizal or putatively plant
pathogenic fungi. Phyla, classes and orders are of limited value,
because functionality of fungi is mainly conserved at the level of genus
and family (Zanne et al., 2020). Yet these ranks, particularly orders
that are generally well-delimited, are informative for understanding the
taxonomic structure. One notable challenge with functional assignments
becomes apparent when individual taxa are matched to multiple ecological
lifestyles (e.g., both saprotroph and endophyte). Lumping these taxa
into a ``multi-guild'' category for ecological analyses is not an
effective solution; instead it is recommended that users carefully
analyze the associated primary literature for those taxa and try to
determine which functional type is most likely represented based on the
study system or research question being addressed.

Curation of the OTU matrix

The LULU software can be used for assessing co-occurrences of closely
related taxa in the OTU-by-sample matrix (Froslev et al., 2017). This
program allows the removal of OTUs that potentially represent minority
haplotypes of common OTUs and remaining PCR and sequencing errors.

The OTU-by-sample matrix alone or tagged with sample metadata and
taxonomic and functional annotation requires some manual curation in a
spreadsheet program. Although many functions can be performed by Python
and Bash scripting, the process of checking taxonomic annotations based
on multiple best hits needs to be performed by manual browsing, because
the first hit may be incompletely annotated. Often, representative
sequences require BLASTn-based re-analysis against INSDc for taxonomic
determination or chimera control (Nilsson et al., 2012) or more complete
functional annotation (Fernandez et al., 2017). Manual BLASTn
examination of the 25-50 largest (most frequent or abundant) OTUs is a
good way to both identify compromised sequence data and to refine the
taxonomic annotation of OTUs. Implementing taxon-specific thresholds for
genera to phyla is easier done in a manual mode.

Checking the distribution of sequences in control samples and spill-over
of positive control in experimental samples is mostly manual work, but
relevant functions are implemented in the metabaR R package and its
associated tool vignette (Zinger et al., 2021). These steps are required
to estimate the rate of index-switching and require appropriate
measures. OTUs found in negative control samples should be assessed
carefully, because these may be derived from molecular reagents,
laboratory space or neighbouring samples (Eisenhofer et al., 2019; Loit
et al., 2019). There are several programs for removing/subtracting
contamination-affected OTUs from data cells (e.g., McKnight et al., 2019
and Zinger et al., 2021). However, among-sample cross-contamination not
affecting control samples (or if there are no controls) may be very
difficult to find. Ranking OTUs by abundance and inspecting Spearman
correlation matrices may be useful to detect cross-contamination. To
ensure recognition of contaminants, sample-specific spike-in molecules
inserted to extractable samples can be used and traced to reads
(Lagerborg et al., 2021).

Rarefaction is a commonly used option to standardise an OTU matrix to
equal sequencing depth based on random subsampling of reads. Samples are
typically rarefied to the lowest sequencing depth (after removing failed
or notably low abundance samples), but there is no consensus whether the
sequencing depth should reflect all reads, fungal reads or reads of each
functional group taken separately. It should be borne in mind that
sequencing depth per sample is not necessarily related to DNA content of
the original sample or biological abundance of certain taxa. The main
issue with rarefaction is the substantial loss of data that often
corresponds to 90\% of the sequencing depth that may carry important
information about diversity (McMurdie \& Holmes, 2014). Figure 3
illustrates that rarefaction provides relatively lower statistical power
in the multivariate analyses as well. Another possibility is rarefaction
to median sequencing depth (de Carcer et al., 2011) and accounting for
sparse samples in statistical models, or through modelling only (Weiss
et al., 2017). An alternative to rarefying is scaling with ranked
subsampling, which retains around 20\% more OTUs (Beule \& Karlovsky,
2020). Some technical methodological comparisons and non-covariate
models may still require rarefying.

\textbf{Statistical data analyses}

Alpha diversity metrics

Microbial diversity has been traditionally studied with OTU richness as
well as Shannon and Simpson diversity indices and the effective species
numbers (Hill numbers) derived from these (Chao et al., 2014; Alberdi \&
Gilbert, 2019). Diversity indices down-weigh the effect of rare OTUs and
therefore only weakly correlate with sequencing depth. OTU richness has
a cumulating function with sequencing depth that is particularly
prominent in diverse, pooled samples. Unless rarefaction is performed,
it is important to include square-root or log-transformed sequencing
depth, whichever is more informative, as a covariate. Although commonly
used, we do not recommend OTU richness extrapolations, because these
rely on the abundance of the rarest OTUs, which are commonly artefactual
(Bunge et al., 2014; Balint et al., 2016).

Adding phylogenetic information to taxonomic composition eliminates the
uncertainty regarding OTU calculations (Washburne et al., 2018) and
could also reduce the effect of remaining PCR/sequencing errors in the
data (Taberlet et al 2018). However, phylogenetic composition of fungal
communities has rarely been assessed (Horn et al., 2014), because the
ITS region is not amenable to robust multiple alignments and
phylogenetic reconstruction much beyond the genus level. There are
alternative methods available such as grafting phylogenies (Fouquier et
al., 2016) and using taxonomic ranks (Tedersoo et al., 2018,
Chalmandrier et al., 2019), which may offer some additional insights.
Testing phylogenetic conservatism, overdispersion and turnover across
phylogenetic scales (Tucker et al., 2016) may also be informative in
analyses of plant-fungal interactions (Chalmandrier et al., 2019),
fungal community assembly processes (Roy et al., 2019) and
phylogeographic patterns (Turon et al., 2020). These statistics can be
calculated in phylocom (Webb et al., 2008), the R packages picante
(Kembel et al., 2010) and S.phylomaker (Qian \& Jin, 2016) as well as
other open-access scripts (Stegen et al., 2015; Ning et al., 2019;
Chalmandrier et al., 2019).

Functional data, derived from genomics, metagenomics,
metatranscriptomics or functional annotation of OTUs complements
information about the taxonomic composition (Laliberte \& Legendre,
2010; Egidi et al., 2019; Escalas et al., 2019) because of widespread
redundancy and mostly family-level phylogenetic autocorrelation in
fungal functional profiles (Zanne et al., 2020). In functional diversity
measures, it is important to consider within-function diversity rather
than richness or relative abundance in a certain function (Escalas et
al., 2019).

Statistical methods

HTS analyses produce semiquantitative abundance data for OTUs, and many
rare taxa remain beyond the detection level. Fungal species differ
greatly in the number of cells per unit biomass, the number of nuclei
per cell and the number of ITS copies per nucleus (Lofgren et al.,
2019). The use of quantitative methods assumes that all samples are
influenced by these biases in a similar manner.

Because the sequencing data are compositional and reflect relative
rather than absolute abundances (Gloor et al., 2017; Lin \& Peddada,
2020b), it may be necessary to transform the entire OTU table
accordingly for testing factors affecting community shifts (this
excludes null model based approaches, e.g. as in Stegen et al., 2015,
which often rely on a sampling process of reads/individuals). Such
transformations can be performed by using additive or modified centered
log-ratio transformation (Quinn et al., 2019; Yoon et al., 2019; Ha et
al., 2020), although it should be kept in mind that these
transformations have limits when the data are sparse (Lovell et al.,
2020). Alternatively, programs for compositional data analyses (e.g.,
ANCOM-BC; Lin \& Peddada, 2020a) and the Aitchison distance metric can
be used (Quinn et al., 2019). With appropriate transformations,
different methods reveal roughly comparable results (Figure 3).

In large datasets, our experience is that log-transformation of fungal
OTU richness yields better-explained statistical models. Classical
transformations of metadata such as logarithmic (concentrations),
square-root (counts) and log-ratio transformations (proportions) are
necessary to shift the distribution of residuals towards normal
distribution and reduce heteroscedasticity -- principal assumptions of
most parametric tests (Legendre \& Legendre, 2012). Log-ratio
transformation also increases the independence of measurements of
various ratios summing up 100\%.

Phylogenetic relationships among associated plants, metazoans, or other
organisms can be accounted for by testing phylogenetic signals or
explicitly quantifying the phylogeny effect by using eigenvectors (e.g.,
adespatial package of R; Dray et al., 2018). Compared with using pure
phylogenetic distances, the use of eigenvectors has proved to extract
larger proportions of plant effects on fungal communities (Tedersoo et
al., 2013; Yang et al., 2019). Similar eigenvector approaches have also
been used to quantify and account for spatial and temporal and plant
phylogeny effects on fungal diversity (Zimmerman \& Vitousek, 2012;
Tedersoo et al., 2020a).

For statistical analyses, most of the useful methods that have been
elaborated in plant and microbial ecology are applicable for fungi, too
(for review, see Buttigieg \& Ramette, 2014; Balint et al., 2016;
Hugerth \& Andersson, 2017). In particular, the R packages vegan
(Oksanen et al., 2019), phyloseq (McMurdie \& Holmes, 2013) and microeco
(Liu et al., 2021) are useful for routine analyses besides commercial
statistical platforms.

When it comes to large amounts of metadata with potential
multicollinearity, it is feasible to pre-select the most important
variables by machine learning algorithms (Qu et al., 2019) such as
random forest implemented in the randomforest (Liaw and Wiener, 2002)
and VSURF (Genuer et al., 2019) R packages with subsequent model
selection based on forward selection or the AICc information criterion
(e.g., nlme R package; Pinheiro et al., 2011; mumin R package). The
party package allows estimating interactions among two and more
variables using machine learning (Strobl et al., 2009), which can be
used to assess conditional and synergistic effects among variables
(Rillig et al., 2019). For variables expected to exhibit unimodal (e.g.,
pH over a broad gradient) or cumulative (e.g., host richness and
rainfall) relationships, inclusion of second-order polynomials or
fitting generalized additive models (GAMs; mgcv package of R; Pedersen
et al., 2019) may be more appropriate.

For taxon-level analyses such as distribution modelling of OTUs, random
forest and univariate models are appropriate, but nonparametric tests
should be used because of multiple zero-values. In addition, indicator
species analyses can be used as implemented in the indicspecies R
package (Caceres \& Legendre, 2009). Specialist and generalist features
of OTUs can be tested in two community types using CLAM, which
outperforms other indicator statistics (Chazdon et al., 2011). Another
option is to decompose multivariate ANOVA models into OTU contributions,
but this works properly only for unifactorial analyses (Ricotta et al.,
2021).

For multivariate analyses, permutational multivariate ANOVA (permanova)
is currently the state of the art in that it outperforms common
ordination methods because of its explicit hypothesis testing (Anderson,
2001). The program PERMANOVA+ included in the Primer6/Primer7 packages
(Anderson et al., 2008) offers more functionalities than the adonis
routine of the vegan package. With respect to ordination methods,
nonmetric multidimensional scaling (NMDS), principal coordinates
analysis (PCoA) and redundancy analysis (RDA) are among the most popular
choices for two-dimensional visualisation of multivariate patterns
(Paliy \& Shankar, 2016). In particular, the ordiellipse function in the
vegan package supports analysis and display of within-group variance.
General dissimilarity modelling (GDM) facilitates the testing of
non-linear effects of multiple variables, which helps the user to
identify and understand critical biological thresholds (gdm R package;
Manion et al., 2018). Similar multivariate analyses can also be
performed for phylogenetic and functional data. All multivariate
analyses are sensitive to selection of transformation and distance
(dissimilarity) measures (Kuczynski et al., 2010; Buttigieg \& Ramette,
2014)). Hellinger transformation (square-root function) and Bray-Curtis
dissimilarity are among the best choices for quantitative data (Legendre
\& Gallagher, 2001). We do not recommend transforming the data to
presences and absences to avoid upweighting rare taxa that may be
artefacts.

Network analysis has become increasingly popular in microbial ecology
(Faust \& Raes, 2012; Mikryukov et al., 2021). Unipartite networks
(co-occurrence analyses) are often used to infer positive (mutualism and
facilitation) and negative (avoidance and competition) interactions
among co-occurring taxa (Weiss et al., 2016). Bipartite networks
estimate partner specificity and how these specific and non-specific
taxa are distributed (R package bipartite; Dormann et al., 2009). Both
approaches also provide information on the modularity, nestedness and
connectivity of the studied ecological network. However, both types of
networks have been commonly overinterpreted: unipartite networks may
yield incorrect implications based on compositional data (Rao et al.,
2021), fail to recover negative associations and exhibit a topology that
is sensitive to both network inference techniques and the number of
samples considered (Weiss et al., 2016, Matchado et al., 2021);
occasional presences are commonly misinterpreted as intimate
associations in bipartite networks, particularly for the plant and
root-associated fungal relationships. From a more theoretical
perspective, it has also been shown that spatial co-occurrences poorly
reflect ecological interactions (Blanchet et al., 2020). For addressing
biologically meaningful facilitation and avoidance strategies among
species, the samples should be unpooled and of relevant size to ensure
direct contact among organisms. Submillimeter scale is the most relevant
for assessing fungal interactions with other fungi and bacteria in an
abiotic environment. There are many network construction algorithms
(Weiss et al., 2016, Matchado et al., 2021) for estimating relationship
strength with correlation measures, indices of dissimilarity between
species pairs, proportionality, or measures of conditional dependence
omitting indirect connections or constructing consensus networks based
on different measures (e.g., CoNet; Faust et al., 2016). Some methods
take into account data compositionality and sparsity, for example SparCC
(Friedman \& Alm, 2011), SPIEC-EASI (Kurtz et al., 2015) and SPRING
(Yoon et al., 2019)). To reduce the number of false positive
associations, it is recommended to reduce matrix size (rare taxa) to
reach a fill level of ca. 50\% (Weiss et al., 2016). The main issue with
comparing networks is related to their lack of true replication (Bahram
et al., 2014) and dependence of results on the linkage metric, filtering
threshold and network construction algorithm (Weiss et al., 2016; Connor
et al., 2017). Nonetheless, consistent associations and integral
topological parameters usually remain unaffected (Toju et al., 2015;
Röttjers \& Faust, 2018).

Structural equation modelling (SEM), in particular path analysis, tests
the directionality as well as direct and indirect effects among
variables (Fan et al., 2016; Collier, 2020). These are important to
consider when the explanatory variables affect each other (e.g.,
vegetation and soil) or there are several related response variables
(e.g., richness of different functional guilds; Yang et al., 2021).
Nonetheless, the causal relationship identified by SEMs models strongly
relies on the hypothetical causalities tested, which should hence be
properly justified by other empirical observations or theoretical
foundations. In addition, SEM models have several commonly ignored
assumptions: multivariate normality, linear associations, no missing
data, no multicollinearity and large sample size -- at least 20 samples
per variable in the model (Collier, 2020). Taxonomic composition can
also be included in SEMs as principal components (Antoninka et al.,
2009). The program AMOS (www.ibm.com) and the R package piecewiseSEM
(Lefcheck, 2016) offer most functionalities needed for such analyses.

It needs to be remembered that in any ecological tests, the most
biologically informative information to the readers are the effect size
(either Z-score or adjusted R\textsuperscript{2}) as well as direction
and shape of the relationship. Estimates of variance and sample size are
also required for conducting meta-analyses. Values of statistical tests
and \emph{P} -values are context-dependent albeit necessary to report
for assessing the validity of the analysis. The rise of type I error
rate in multiple testing needs to be accounted for by using flexible
correction methods such as Benjamini-Hochberg correction (Noble, 2009).

Absolute and relative abundance

Absolute abundance of bacteria and fungi (as biomass) is an important
ecological measure, which indicates ecosystem nutrient cycles. In
addition, biomass estimates are important for correctly addressing
differences in composition (see below). As fungal biomass cannot be
directly measured, there are several proxies for its estimation, notably
phospholipid fatty acid (PLFA) profiles, ergosterol abundance, qPCR, and
metagenome composition associated with total DNA measurements (Baldrian
et al., 2013; Bahram et al., 2018; Yang et al., 2020). However,
unicellular fungi are poorly represented in these biomass estimates
because of different analogous compounds (Weete et al., 2010), primer
biases and the general paucity of reference genome data (Tedersoo et
al., 2015). Of the available options, qPCR is the simplest and least
laborious method, and it is expected to correlate the strongest with HTS
data since comparisons are related to the same DNA extracts. All these
methods assume that DNA, sterols and PLFAs are extracted from various
samples at equal efficiency, but this cannot be guaranteed for different
soils, plant species or tissues.

An alternative is to use a spike-in method, which relies on adding
artificial molecules or cells of one or preferably more species that are
not expected to occur in biological samples, before DNA extraction
(Palmer et al., 2018; Rao et al., 2021). For absolute quantification, it
is required that the number of rRNA gene copies of the spike-in taxa is
known. Index switches may disproportionally elevate the estimates of
spike-in abundance in samples with a high DNA content.

Visualisation of results

There are a large number of methods and software tools available for
visualising the statistical results, box plots and scatter plots being
the most commonly used. Violin plots are a specific type of box plots
that indicate distribution of measurements and deviation from normality.
Rarefaction curves (smoothed species accumulation curves) are useful for
graphical comparisons of species evenness and richness but also
evaluating sufficiency of sampling and sequencing depth within and among
samples (Colwell et al., 2004). Heat maps are useful for visualising
large correlation matrices or the results of multiple multifactorial
analyses (e.g., ClustVis web tool; Metsalu \& Vilo, 2015), although the
typical lack of within-treatment variation can make their ecological
interpretation challenging. Venn diagrams are useful for showing unique
and shared variation among factors or OTUs across factor levels or
combinations (e.g., venny; Oliveros, 2007), and rank-abundance plots can
provide useful information on what specific taxa underlie certain
ecological patterns. Similarly, overlying environmental vectors on
ordination plots can help to identify the abiotic and biotic variables
that are associated with specific taxon or sample abundances.

The overall taxonomic composition is best visualised in stacked plots,
which makes it easier to display multiple treatments. However, as error
bars are lacking (there is no space), such plots and heat maps are
examples of implicit pseudoreplication. Krona charts (Ondov et al.,
2011) and heat trees (metacoder package of R; Foster et al., 2017)
provide an efficient way of demonstrating the distribution of dominant
taxa by taxonomic ranks (e.g., Nilsson et al., 2017). Because of high
space requirements, the overall view or comparison of up to two levels
of a treatment can be effectively indicated for Krona charts, but
interactive versions can be provided as supplementary items. Heat trees
can handle two samples or a single gradient.

While phylogenetic trees are generally too large for visualising
taxonomic affiliation of OTUs in the main article, these are well suited
to supplementary items. Although family-level and higher-level
phylogenetic relationships cannot always be assessed based on ITS data,
these are suitable for demonstrating rough phylogenetic placement.
Phylogenetic trees make more sense for 18S and 28S rRNA gene data and
are furthermore helpful in detecting artifactual OTUs based on
ultra-long branches or branches with zero length next to a long-branch
taxon (Tedersoo et al., 2020b). Large circular phylogenies squeezed into
ca. 17 cm page width offers limited opportunities for interpretation if
taxon names are unreadable and branch support values are lacking. Using
iTOL (interactive tree of life; https://itol.embl.de/), more than 50,000
taxa in phylogenetic trees can be equipped with large amounts of
metadata for display in supplementary materials (Letunic \& Bork, 2021).

Data management

Funding requests should be written to cover not only the field and
laboratory parts, but also data processing, metadata annotation, and
public deposition and dissemination. Fungal metabarcoding data should be
submitted to any public archive such as short read archive (SRA),
European nucleotide archive (ENA) or UNITE. UNITE calculates
100\%-similarity OTUs for full-length ITS sequences for each biological
sample that are used for further reference and incorporated in SHs.
Direct submission of representative sequences to INSDc is discouraged,
because these poorly annotated and commonly low-quality data hamper
further analyses. We also ask researchers to upload their community
matrices, metadata and demultiplexing information (if relevant) in
public repositories or supplementary materials, which enables other
users to perform meta-analyses and databasing (Põlme et al., 2020;
Vetrovsky et al., 2020). Figshare (https://figshare.com/) and Dryad
(https://datadryad.org/) are among the most popular choices. Similarly,
scripts used for analyses should be released to secure reproducibility
and potential reuse in other applications. For the sake of clarity and
machine-readability, it is best to use standardised MIMARKS and MIxS
terminology (Yilmaz et al., 2011).

In the materials and methods section of scientific papers, it is
important to document all aspects of the analysis Lindahl et al., 2013).
This is likely to shorten the review process and help reviewers and
readers to evaluate the validity and novelty of the procedures.

\textbf{Future perspectives and conclusions}

Metabarcoding analyses have recently opened new frontiers in many fields
of mycology including assessment of functionality, integration into the
entire microbiome analyses, detection of novel fungal taxa and ancient
organisms and providing sequence data for taxonomically valuable
specimens. Metabarcoding analyses using SIP of labelled substrates
(Hannula et al., 2017) or coupled with metatranscriptomics (Zifcakova et
al., 2016) and proteomics have revealed functionally active fungal
organisms and their activity in situ. Parallel studies of fungi,
bacteria and protists have revealed antagonistic interactions (Bork et
al., 2015; Bahram et al., 2018), the structure of the micro- and
mycobiome web (Tipton et al., 2018) and mechanisms of community assembly
(Zinger et al., 2019b). HTS-derived data have revealed several groups of
previously undescribed (or unsequenced) order- and class-level fungal
lineages (Tedersoo et al., 2020b; Zhang et al., 2021). These reads offer
material for constructing taxon-specific primers and probes for
visualizing the cells (Chambouvet et al., 2019) and help discovery and
characterisation of fungal dark matter. Increasing read length and
precision of HTS methods enhance taxonomic precision and highlight a
venue for population-level studies based on eDNA (Byrne et al., 2017;
Turon et al., 2020). This may be of particular relevance to fungal taxa
that are difficult to culture and form no fruit-bodies (Lücking et al.,
2021). Metabarcoding of short fragments furthermore sheds light into
ancient DNA including potentially ancient fungi (Balint et al., 2018;
Talas et al., 2021), but methods distinguishing organisms representing
ancient, recently dead and active organisms should be refined (e.g.,
metabarcoding along a marker length series, RNA vs. DNA approaches and
analyses of chemically modified DNA fraction). Similarly, metabarcoding
approaches are useful in generating DNA barcodes from century-old
fruit-body specimens including valuable type material where extra care
is needed to identify and dismiss air-borne contaminants (Forin et al.,
2018).

Metabarcoding of fungi is a rapidly advancing research topic, which has
benefited from initial methods and development in clinical microbiology
and environmental microbiology - disciplines that are an order of
magnitude larger. Thus, fungal ecologists have been able to learn from
technological breakthroughs and pitfalls and develop unique solutions
for analysis of the ITS marker, the present mainstay of fungal taxonomy
and ecology. Through short-read metabarcoding, fungal diversity has been
analysed in nearly all habitats on Earth, including extreme environments
(Nilsson et al., 2018). We hope that with the assistance of growing
reference databases, studies involving fungal taxonomic, phylogenetic
and functional composition will flourish, because many principal aspects
in evolutionary and functional (e.g., trait-based) ecology remain poorly
known so far. These studies should be supplemented by rigorous
experiments to validate the findings and infer causality. It somehow
seems unacceptable that studies on one of the most important groups of
nutrient cyclers - the fungi - be based on anything but the best and
most up-to-date methodological recommendations, and we sincerely hope
that this review has contributed to that effect.

\textbf{Acknowledgments}

We thank Mengting Maggie Yuan, Jizhong Zhou and Brendan Furneaux for
sharing data and Liis Tiirmann for editing the manuscript.

\textbf{Author contributions}

LT, MB and VM re-analysed data. All authors contributed to ideas and
writing.

\textbf{Data availability statement}

This review produced no data and no code.

\textbf{References}

Aas, A. B., Davey, M. L., \& Kauserud, H. (2017). ITS all right mama:
Investigating the formation of chimeric sequences in the ITS2 region by
DNA metabarcoding analyses of fungal mock communities of different
complexities. \emph{Molecular Ecology Resources, 17}, 730--741. doi:
10.1111/1755-0998.12622 Abdala Asbun, A., Besseling, M. A., Balzano, S.,
van Bleijswijk, J. D., Witte, H. J., Villanueva, L., \& Engelmann, J. C.
(2020). Cascabel: A scalable and versatile amplicon sequence data
analysis pipeline delivering reproducible and documented results.
\emph{Frontiers in Genetics, 11,} 1329. Albanese, D., Fontana, P., De
Filippo, C., Cavalieri, D., \& Donati, C. (2015). MICCA: A complete and
accurate software for taxonomic profiling of metagenomic data.
\emph{Scientific Reports,} \emph{5,} 9743. doi: 10.1038/srep09743
Alberdi, A., Aizpurua, O., Gilbert, M. T., \& Bohmann, K. (2018).
Scrutinizing key steps for reliable metabarcoding of environmental
samples. \emph{Methods in Ecology and Evolution, 9}, 134-147. doi:
10.1111/2041-210X.12849 Alberdi, A., \& Gilbert, M.T. (2019). A guide to
the application of Hill numbers to DNA\selectlanguage{english}-based diversity analyses.
\emph{Molecular Ecology Resources, 19}, 804-817.doi:
10.1111/1755-0998.13014 Amend, A., Burgaud, G., Cunliffe, M., Edgcomb,
V. P., Ettinger, C. L., Gutierrez, M. H., \ldots{} Gladfelter, A. S.
(2019). Fungi in the marine environment: open questions and unsolved
problems. \emph{mBio, 10}, e01189-18. doi: 10.1128/mBio.01189-18 Amir,
A., McDonald, D., Navas-Molina, J.A., Kopylova, E., Morton, J. T., Zech
Xu, Z., \ldots{} Knight, R. (2017). Deblur rapidly resolves
single-nucleotide community sequence patterns. \emph{mSystems, 2},
e00191-16. doi: 10.1128/mSystems.00191-16 Anderson, M. J., Gorley, R.
N., \& Clarke, R. K. (2008).\emph{Permanova+ for Primer: Guide to
Software and Statistical Methods}. Plymouth: Primer-E. Anderson, M. J.
(2001). A new method for non-parametric multivariate analysis of
variance. \emph{Austral Ecology, 26,} 32-46. doi:
10.1111/j.1442-9993.2001.01070.pp.x Anslan, S., Bahram, M., Hiiesalu,
I., \& Tedersoo, L. (2017). PipeCraft: Flexible open-source toolkit for
bioinformatics analysis of custom high-throughput amplicon sequencing
data. \emph{Molecular Ecology Resources, 17}, e234--e240. doi:
10.1111/1755-0998.12692 Anslan, S., Li, H., Kunzel, S., \& Vences, M.
(2021). Microbiomes from feces vs. gut in tadpoles: distinct community
compositions between substrates and preservation methods.
\emph{Salamandra, 57}, 96-104. doi: 10.1101/651612 Anslan, S., Nilsson,
R. H., Wurzbacher, C., Baldrian, P., Tedersoo, L., \& Bahram, M. (2018).
Great differences in performance and outcome of high-throughput
sequencing data analysis platforms for fungal metabarcoding.
\emph{MycoKeys, 39}, 29-40. doi: 10.3897/mycokeys.39.28109 Antoninka,
A., Wolf, J. E., Bowker. M., Classen, A. T., \& Johnson, N. C. (2009).
Linking above- and belowground responses to global change at community
and ecosystem scales. \emph{Global Change Biology, 15}, 914-929. doi:
10.1111/j.1365-2486.2008.01760.x Bahram, M., Harend, H., \& Tedersoo, L.
(2014). Network perspectives of ectomycorrhizal associations.
\emph{Fungal Ecology, 7}, 70-77. doi: 10.1016/j.funeco.2013.10.003
Bahram, M., Hildebrand, F., Forslund, S. K., Anderson, J. L.,
Soudzilovskaia, N. A., Bodegom, P. M., \ldots{} Bork P. (2018).
Structure and function of the global topsoil microbiome. \emph{Nature,
560}, 233-237. doi: 10.1038/s41586-018-0386-6 Bahram, M., Peay, K. G.,
\& Tedersoo, L. (2015). Local-scale biogeography and spatiotemporal
variability in communities of mycorrhizal fungi. \emph{New Phytologist,
205}, 1454--1463. doi: 10.1111/nph.13206 Bakker, M. G. (2018). A fungal
mock community control for amplicon sequencing experiments.
\emph{Molecular Ecology Resources, 18}, 541-556. doi:
10.1111/1755-0998.12760 Baldrian, P., Vetrovsky, T., Cajthaml, T.,
Dobiasova, P., Petrankova, M., Snajdr, J., \& Eichlerova, I. (2013).
Estimation of fungal biomass in forest litter and soil. \emph{Fungal
Ecology, 6}, 1-11. doi: 10.1016/j.funeco.2012.10.002 Balint, M., Bahram,
M., Eren, A. M., Faust, K., Fuhrman, J. A., Lindahl, B., \ldots{}
Tedersoo, L. (2016). Millions of reads, thousands of taxa: microbial
community structure and associations analyzed via marker genes.
\emph{FEMS Microbiology Reviews, 40}, 686-700. doi:
10.1093/femsre/fuw017 Balint, M., Pfenninger, M., Grossart, H. P.,
Taberlet, P., Vellend, M., Leibold, M. A., \ldots{} Bowler, D. (2018).
Environmental DNA time series in ecology. \emph{Trends in Ecology \&
Evolution, 33}, 945-957. doi: 10.1016/j.tree.2018.09.003 Banos, S.,
Lentendu, G., Kopf, A., Wubet, T., Glockner, F. O., \& Reich, M. A.
(2018). Comprehensive fungi-specific 18S rRNA gene sequence primer
toolkit suited for diverse research issues and sequencing
platforms.\emph{BMC Microbiology, 18,} 190. doi:
10.1186/s12866-018-1331-4 Barbera, P., Kozlov, A. M., Czech, L., Morel,
B., Darriba, D., Flouri, T., \& Stamatakis, A. (2019). EPA-ng: Massively
parallel evolutionary placement of genetic sequences. \emph{Systematic
Biology, 68}, 365--369. doi: 10.1093/sysbio/syy054 Bengtsson-Palme, J.,
Ryberg, M., Hartmann, M., Branco, S., Wang, Z., Godhe, A., \ldots{}
Nilsson, R. H. (2013). Improved software detection and extraction of
ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes
for analysis of environmental sequencing data.\emph{Methods in Ecology
and Evolution, 4}, 914-919. doi: 10.1111/2041-210X.12073 Berry, D., \&
Loy, A. (2018). Stable-isotope probing of human and animal microbiome
function. \emph{Trends in Microbiology, 26}, 999-1007. doi:
10.1016/j.tim.2018.06.004 Beule, L., \& Karlovsky, P. (2020). Improved
normalization of species count data in ecology by scaling with ranked
subsampling (SRS): Application to microbial communities. \emph{PeerJ,
8}, e9593. doi: 10.7717/peerj.9593 Bissett, A., Fitzgerald, A.,
Meintjes, T., Mele, P. M., Reith, F., Dennis, P. G., \ldots{} Byrne, M.
(2016). Introducing BASE: the Biomes of Australian Soil Environments
soil microbial diversity database.\emph{GigaScience, 5}, 21. doi:
10.1186/s13742-016-0126-5 Blanchet, F. G., Cazelles, K., \& Gravel, D.
(2020). Co\selectlanguage{english}-occurrence is not evidence of ecological interactions.
\emph{Ecology Letters 23}, 1050-1063. doi: 10.1111/ele.13525 Blazewicz,
S. J., Barnard, R. L., Daly, R. A., \& Firestone, M. K. (2013).
Evaluating rRNA as an indicator of microbial activity in environmental
communities: Limitations and uses. \emph{The ISME Journal, 7},
2061-2068. doi: 10.1038/ismej.2013.102 Bohmann, K., Elbrecht, V., Caroe,
C., Bista, I., Leese, F., Bunce, M., \ldots{} \& Creer, S. (in press).
Strategies for sample labelling and library preparation in DNA
metabarcoding studies. \emph{Molecular Ecology Resources}. Bolyen, E.,
Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C.,
Al-Ghalith, G. A., \ldots{} Caporaso, J. G. (2019). Reproducible,
interactive, scalable and extensible microbiome data science using QIIME
2. \emph{Nature Biotechnology, 37}, 852--857. doi:
10.1038/s41587-019-0209-9 Bork, P., Bowler, C., De Vargas, C., Gorsky,
G., Karsenti, E., \& Wincker, P. (2015). Tara Oceans studies plankton at
planetary scale.\emph{Science, 348,} 873. doi: 10.1126/science.aac5605
Boyer, F., Mercier, C., Bonin, A., Le Bras, Y., Taberlet, P., \&
Coissac, E. (2016). Obitools: A unix\selectlanguage{english}-inspired software package for DNA
metabarcoding. \emph{Molecular Ecology Resources, 16}, 176-182. doi:
10.1111/1755-0998.12428 Brabcova, V., Novakova, M., Davidova, A., \&
Baldrian, P. (2016). Dead fungal mycelium in forest soil represents a
decomposition hotspot and a habitat for a specific microbial community.
\emph{New Phytologist, 210}, 1369-1381. doi: 10.1111/nph.13849 Brandt,
M. I., Trouche, B., Quintric, L., Gunther, B., Wincker, P., Poulain, J.,
\& Arnaud\selectlanguage{english}-Haond S. (2021). Bioinformatic pipelines combining denoising
and clustering tools allow for more comprehensive prokaryotic and
eukaryotic metabarcoding. \emph{Molecular Ecology Resources, 21},
1904-1921. doi: 10.1111/1755-0998.13398 Bruns, T. D., \& Taylor, J. W.
(2016). Comment on ``Global assessment of arbuscular mycorrhizal fungus
diversity reveals very low endemism''.\emph{Science, 351},826-826. doi:
10.1126/science.aad2346 Bunge, J., Willis, A., \& Walsh, F. (2014).
Estimating the number of species in microbial diversity studies.
\emph{Annual Review of Statistics and Its Application, 1}, 427-445. doi:
10.1146/annurev-statistics-022513-115654 Buschmann, T., \& Bystrykh, L.
V. (2013). Levenshtein error-correcting barcodes for multiplexed DNA
sequencing. \emph{BMC Bioinformatics, 14,}272. doi:
10.1186/1471-2105-14-272 Buttigieg, P. L., \& Ramette, A. (2014). A
guide to statistical analysis in microbial ecology: a community-focused
living review of multivariate data analyses. \emph{FEMS Microbiology
Ecology, 90}, 543-550. doi: 10.1111/1574-6941.12437 Byrne, A. Q.,
Rothstein, A. P., Poorten, T. J., Erens, J., Settles, M. L., \&
Rosenblum, E. B. (2017). Unlocking the story in the swab: A new
genotyping assay for the amphibian chytrid fungus \emph{Batrachochytrium
dendrobatidis}. \emph{Molecular Ecology Resources, 17}, 1283-1292. doi:
10.1111/1755-0998.12675 Caceres, M. D., \& Legendre, P. (2009).
Associations between species and groups of sites: Indices and
statistical inference. \emph{Ecology, 90}, 3566-3574. doi:
10.1890/08-1823.1 Cai, F., \& Druzhinina, I. S. (2021). In honor of John
Bissett: Authoritative guidelines on molecular identification of
Trichoderma.\emph{Fungal Diversity, 107}, 1-69. doi:
10.1007/s13225-020-00464-4 Calderon\selectlanguage{english}-Sanou, I., Munkemuller, T., Boyer,
F., Zinger, L., \& Thuiller, W. (2020). From environmental DNA sequences
to ecological conclusions: How strong is the influence of methodological
choices? \emph{Journal of Biogeography, 47}, 193-206. doi:
10.1111/jbi.13681 Callahan, B. J., Grinevich, D., Thakur, S., Balamotis,
M. A., \& Yehezkel, T. B. (2021). Ultra-accurate microbial amplicon
sequencing with synthetic long reads. \emph{Microbiome, 9}, 130. doi:
10.1186/s40168-021-01072-3 Callahan, B. J., McMurdie, P. J., Rosen, M.
J., Han, A. W., Johnson, A. J., \& Holmes, S. P. (2016). DADA2:
High-resolution sample inference from Illumina amplicon data.
\emph{Nature Methods, 13}, 581-584. doi: 10.1038/nmeth.3869 Callahan, B.
J., Wong, J., Heiner, C., Oh, S., Theriot, C. M., Gulati, A. S.,
\ldots{} Dougherty, M. K. (2019). High-throughput amplicon sequencing of
the full-length 16S rRNA gene with single-nucleotide resolution.
\emph{Nucleic acids research, 47,} e103. doi: 10.1093/nar/gkz569
Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J.,
Bealer, K., \& Madden, T. L. (2009). BLAST+: Architecture and
applications. \emph{BMC Bioinformatics, 10,} 421. doi:
10.1186/1471-2105-10-421 Carlsen, T., Aas, A. B., Lindner, D., Vralstad,
T., Schumacher, T., \& Kauserud, H. (2012). Don't make a mista(g)ke: Is
tag switching an overlooked source of error in amplicon pyrosequencing
studies?\emph{Fungal Ecology, 5}, 747-749. doi:
10.1016/j.funeco.2012.06.003 Caroe, C., \& Bohmann, K. (2020).
Tagsteady: A metabarcoding library preparation protocol to avoid false
assignment of sequences to samples.\emph{Molecular Ecology Resources,
20}, 1620-1631. doi: 10.1111/1755-0998.13227 Castano, C., Parlade, J.,
Pera, J., De Aragon, J. M., Alday, J. G., \& Bonet, J. A. (2016). Soil
drying procedure affects the DNA quantification of Lactarius vinosus but
does not change the fungal community composition. \emph{Mycorrhiza, 26,}
799-808. doi: 10.1007/s00572-016-0714-3 Castle, S. C., Song, Z., Gohl,
D. M., Gutknecht, J. L., Rosen, C. J., Sadowsky, M. J., \ldots{} Kinkel,
L. L. (2018). DNA template dilution impacts amplicon sequencing-based
estimates of soil fungal diversity.\emph{Phytobiomes, 2}, 100-107. doi:
10.1094/PBIOMES-09-17-0037-R Chalmandrier, L., Pansu, J., Zinger, L.,
Boyer, F., Coissac, E., Genin, A., \ldots{} Taberlet, P. (2019).
Environmental and biotic drivers of soil microbial \selectlanguage{greek}β\selectlanguage{english}-\selectlanguage{english}diversity across
spatial and phylogenetic scales.\emph{Ecography, 42}, 2144-2156. doi:
10.1111/ecog.04492 Chambouvet, A., Monier, A., Maguire, F., Ito\selectlanguage{ngerman}ïz, S.,
del Campo, J., Elies, P., \ldots{} Richards, T. A. (2019). Intracellular
Infection of Diverse Diatoms by an Evolutionary Distinct Relative of the
Fungi.\emph{Current Biology, 29}, 4093-4101. doi:
10.1016/j.cub.2019.09.074 Chao, A., Gotelli, N. J., Hsieh, T. C.,
Sander, E. L., Ma, K. H., Colwell, R. K., \& Ellison, A. M. (2014).
Rarefaction and extrapolation with Hill numbers: A framework for
sampling and estimation in species diversity studies. \emph{Ecological
Monographs, 84}, 45-67. doi: 10.1890/13-0133.1 Chazdon, R. L., Chao, A.,
Colwell, R. K., Lin, S. Y., Norden, N., Letcher, S. G., \ldots{} Arroyo,
J. P. (2011). A novel statistical method for classifying habitat
generalists and specialists.\emph{Ecology, 92}, 1332-1343. doi:
10.1890/10-1345.1 Clasen, L. A., Detheridge, A. P., Scullion, J., \&
Griffith, G. W. (2020). Soil stabilisation for DNA metabarcoding of
plants and fungi. Implications for sampling at remote locations or via
third-parties.\emph{Metabarcoding and Metagenomics, 4}, e58365. doi:
10.3897/mbmg.4.58365 Cline, L. C., Song, Z., Al\selectlanguage{english}-Ghalith, G. A., Knights,
D., \& Kennedy, P. G. (2017). Moving beyond de novo clustering in fungal
community ecology.\emph{New Phytologist, 216}, 629-634. doi:
10.1111/nph.14752 Collier, J. E. (2020). \emph{Applied structural
equation modeling using AMOS: Basic to advanced techniques}. New York:
Routledge Publishing. Colwell, R. K., Mao, C. X., \& Chang, J. (2004).
Interpolating, extrapolating, and comparing incidence-based species
accumulation curves. \emph{Ecology, 85}, 2717--2727. doi:
10.1890/03-0557 Connor, N., Barberan, A., \& Clauset, A. (2017). Using
null models to infer microbial co-occurrence networks. \emph{PLoS ONE,
12}, e0176751. doi: 10.1371/journal.pone.0176751 Cristescu, M. E., \&
Hebert, P. D. (2018). Uses and misuses of environmental DNA in
biodiversity science and conservation. \emph{Annual Review of Ecology,
Evolution, and Systematics, 49}, 209-230. doi:
10.1146/annurev-ecolsys-110617-062306 Davis, N. M., Proctor, D. M.,
Holmes, S. P., Relman, D. A., \& Callahan, B. J. (2018). Simple
statistical identification and removal of contaminant sequences in
marker-gene and metagenomics data.\emph{Microbiome, 6}, 226. doi:
10.1186/s40168-018-0605-2 de Carcer, D. A., Denman, S. E., McSweeney,
C., \& Morrison, M. (2011). Evaluation of subsampling-based
normalization strategies for tagged high-throughput sequencing datasets
from gut microbiomes. \emph{Applied and Environmental Microbiology, 77},
8795-8798. doi: 10.1128/aem.05491-11 Delavaux, C. S., Bever, J. D.,
Karppinen, E. M., \& Bainard, L. D. (2020). Keeping it cool: Soil sample
cold pack storage and DNA shipment up to 1 month does not impact
metabarcoding results. \emph{Ecology and Evolution, 10}, 4652-4664. doi:
10.1002/ece3.6219 Delavaux, C. S., Sturmer, S. L., Wagner, M. R.,
Schutte, U., Morton, J. B., \& Bever, J. D. (2021). Utility of LSU for
environmental sequencing of arbuscular mycorrhizal fungi: A new
reference database and pipeline.\emph{New Phytologist, 229}, 3048-3052.
doi: 10.1111/nph.17080 Deng, Y., Jiang, Y-H., Yang, Y., He, Z., Luo, F.,
\& Zhou, J. (2012). Molecular ecological network analyses. \emph{BMC
Bioinformatics, 13}, 113. doi: 10.1186/1471-2105-13-113 Deshpande, V.,
Wang, Q., Greenfield, P., Charleston, M., Porras-Alfaro, A., Kuske, C.
R., \ldots{} Tran-Dinh, N. (2016). Fungal identification using a
Bayesian classifier and the Warcup training set of internal transcribed
spacer sequences. \emph{Mycologia, 108}, 1-5. doi: 10.3852/14-293
Dickie, I. A., Boyer, S., Buckley, H. L., Duncan, R. P., Gardner, P. P.,
Hogg, I. D., \ldots{} Weaver, L. (2018). Towards robust and repeatable
sampling methods in eDNA-based studies. \emph{Molecular Ecology
Resources, 18}, 940--952. doi: 10.1111/1755-0998.12907 Dormann, C. F.,
Frund, J., Bluthgen, N., \& Gruber, B. (2009). Indices, graphs and null
models: analyzing bipartite ecological networks.\emph{Open Ecology
Journal, 2}, 7-24. doi: 10.2174/1874213000902010007 Douhan, G. W.,
Vincenot, L., Gryta, H., \& Selosse, M-A. (2011). Population genetics of
ectomycorrhizal fungi: From current knowledge to emerging directions.
\emph{Fungal Biology, 115}, 569--597. doi: 10.1016/j.funbio.2011.03.005
Dray, S., Blanchet, G., Borcard, D., Guenard, G., Jombart, T., Larocque,
G., \ldots{} Wagner, H. H. (2018). \emph{Package
`adespatial'.}Available\emph{:}https://cran.microsoft.com/web/packages/adespatial/adespatial.pdf
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C., \& Knight, R.
(2011). UCHIME improves sensitivity and speed of chimera
detection.\emph{Bioinformatics, 27}, 2194-2200. doi:
10.1093/bioinformatics/btr381 Edgar, R. C. (2010). Search and clustering
orders of magnitude faster than BLAST. \emph{Bioinformatics, 26},
2460-2461. doi: 10.1093/bioinformatics/btq461 Edgar, R. C. (2016).
UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon
sequencing. \emph{BioRxiv, 2016}, 081257. doi: 10.1101/081257 Edgar, R.
C. (2017). Accuracy of microbial community diversity estimated by
closed- and open-reference OTUs. \emph{PeerJ, 5}, e3889. doi:
10.7717/peerj.3889 Egan, C. P., Rummel, A., Kokkoris, V., Klironomos,
J., Lekberg, Y., \& Hart, M. (2018). Using mock communities of
arbuscular mycorrhizal fungi to evaluate fidelity associated with
Illumina sequencing. \emph{Fungal Ecology, 33}, 52-64. doi:
10.1016/j.funeco.2018.01.004 Eisenhofer, R., Minich, J. J., Marotz, C.,
Cooper, A., Knight, R., \& Weyrich, L. S. (2019). Contamination in low
microbial biomass microbiome studies: Issues and recommendations.
\emph{Trends in Microbiology, 27}, 105-117. doi:
10.1016/j.tim.2018.11.003 Escalas, A., Hale, L., Voordeckers, J. W.,
Yang, Y., Firestone, M. K., Alvarez\selectlanguage{english}-Cohen, L., \& Zhou, J. (2019).
Microbial functional diversity: From concepts to applications.
\emph{Ecology and Evolution, 9}, 12000-12016. doi: 10.1002/ece3.5670
Escudie, F., Auer, L., Bernard, M., Mariadassou, M., Cauquil, L., Vidal,
K., \ldots{} Pascal, G. (2018). FROGS: Find, rapidly, OTUs with galaxy
solution. \emph{Bioinformatics, 34,} 1287-1294. doi:
10.1093/bioinformatics/btx791 Estensmo, E. L., Maurice, S., Morgado, L.,
Martin\selectlanguage{english}-Sanchez, P. M., Skrede, I., \& Kauserud, H. (2011). The influence
of intraspecific sequence variation during DNA metabarcoding: A case
study of eleven fungal species. \emph{Molecular Ecology Resources, 21},
1141-1148. doi: 10.1111/1755-0998.13329 Fadrosh, D. W., Ma, B., Gajer,
P., Sengamalay, N., Ott, S., Brotman, R. M., \& Ravel, J. (2014). An
improved dual-indexing approach for multiplexed 16S rRNA gene sequencing
on the Illumina MiSeq platform.\emph{Microbiome, 2}, 6. doi:
10.1186/2049-2618-2-6 Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S.
R., Park, H., \& Shao, C. (2016). Applications of structural equation
modeling (SEM) in ecological studies: An updated review.
\emph{Ecological Processes, 5}, 19. doi: 10.1186/s13717-016-0063-3
Faust, K., \& Raes, J. (2016). CoNet app: Inference of biological
association networks using Cytoscape. \emph{F1000Research, 5}, 1519.
doi: 10.12688/f1000research.9050.2 Faust, K., \& Raes, J. (2012).
Microbial interactions: From networks to models. \emph{Nature Reviews
Microbiology, 10}, 538-549. doi: 10.1038/nrmicro2832 Fernandez, C. W.,
Nguyen, N. H., Stefanski, A., Han, Y., Hobbie, S. E., Montgomery, R. A.,
\ldots{} Kennedy, P. G. (2017). Ectomycorrhizal fungal response to
warming is linked to poor host performance at the boreal\selectlanguage{english}-temperate
ecotone. \emph{Global Change Biology, 23}, 1598-1609. doi:
10.1111/gcb.13510 Ficetola, G. F., Coissac, E., Zundel, S., Riaz, T.,
Shehzad, W., Bessiere, J., \ldots{} Pompanon, F. (2010). An in silico
approach for the evaluation of DNA barcodes. \emph{BMC Genomics, 11},
434. doi: 10.1186/1471-2164-11-434 Fischer, M., Renevey, N., Thur, B.,
Hoffmann, D., Beer, M., \& Hoffmann, B. (2016). Efficacy assessment of
nucleic acid decontamination reagents used in molecular diagnostic
laboratories. \emph{PLoS ONE, 11}, e0159274. doi:
10.1371/journal.pone.0159274 Foster, Z. S. L., Sharpton, T. J., \&
Grunwald, N. J. (2017). Metacoder: An R package for visualization and
manipulation of community taxonomic diversity data. \emph{PLoS
Computational Biology, 13}, e1005404. doi: 10.1371/journal.pcbi.1005404
Fouquier, J., Rideout, J. R., Bolyen, E., Chase, J., Shiffer, A.,
McDonald, D., \ldots{} Kelley, S. T. (2016). Ghost-tree: Creating
hybrid-gene phylogenetic trees for diversity analyses. \emph{Microbiome,
4}, 11. doi: 10.1186/s40168-016-0153-6 Friedman, J., \& Alm, E. J.
(2011). Inferring correlation networks from genomic survey data.
\emph{PLoS Computational Biology, 8}, e1002687. doi:
10.1371/journal.pcbi.1002687 Furneaux, B., Bahram, M., Rosling, A.,
Yorou, N. S., \& Ryberg, M. (2021). Long\selectlanguage{english}-and short\selectlanguage{english}-read metabarcoding
technologies reveal similar spatiotemporal structures in fungal
communities. \emph{Molecular Ecology Resources, 21}, 1833-1849. doi:
10.1111/1755-0998.13387 Geisen, S., Tveit, A. T., Clark, I. M., Richter,
A., Svenning, M. M., Bonkowski, M., \& Urich, T. (2015).
Metatranscriptomic census of active protists in soils. \emph{The}
\emph{ISME Journal, 9}, 2178-2190. doi: 10.1038/ismej.2015.30 Georgieva,
D., Liu, Q., Wang, K., \& Egli, D. (2020). Detection of base analogs
incorporated during DNA replication by nanopore sequencing.\emph{Nucleic
Acids Research, 48}, e88. doi: 10.1093/nar/gkaa517 Gherbawy, Y., \&
Voigt, K. (eds). (2010). \emph{Molecular identification of fungi.}
Berlin: Springer. Glassman, S. I., \& Martiny, J. B. (2018). Broadscale
ecological patterns are robust to use of exact sequence variants versus
operational taxonomic units. \emph{mSphere, 3}, e00148-18. doi:
10.1128/mSphere.00148-18 Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn,
V., \& Egozcue, J. J. (2017). Microbiome datasets are compositional: And
this is not optional.\emph{Frontiers in Microbiology, 8}, 2224. doi:
10.3389/fmicb.2017.02224 Gohl, D. M., Auch, B., Certano, A., LeFrancois,
B., Bouevitch, A., Doukhanine, E., \ldots{} Beckman, K. B. (2021).
Dissecting and tuning primer editing by proofreading polymerases.
\emph{Nucleic Acids Research, 49}, e87. doi: 10.1093/nar/gkab471 Gohl,
D. M., Vangay, P., Garbe, J., MacLean, A., Hauge, A., Becker, A.,
\ldots{} Knights, D. (2016). Systematic improvement of amplicon marker
gene methods for increased accuracy in microbiome studies. \emph{Nature
Biotechnology, 34}, 942-949. doi: 10.1038/nbt.3601 Gotelli, N. J., \&
Ellison, A. M. (2004). \emph{A primer of ecological statistics}.
Sunderland: Sinauer Associates. Grossart, H. P., Van den Wyngaert, S.,
Kagami, M., Wurzbacher, C., Cunliffe, M., \& Rojas-Jimenez, K. (2019).
Fungi in aquatic ecosystems.\emph{Nature Reviews Microbiology, 17},
339--354. doi: 10.1038/s41579-019-0175-8 Guerrieri, A., Bonin, A.,
Munkemuller, T., Gielly, L., Thuiller, W., \& Ficetola, G. F. (2021).
Effects of soil preservation for biodiversity monitoring using
environmental DNA. \emph{Molecular Ecology, 30,}3313-3325. doi:
10.1111/mec.15674 Gweon, H. S., Oliver, A., Taylor, J., Booth, T.,
Gibbs, M., Read, D. S., \ldots{} Schonrogge, K. (2015). PIPITS: An
automated pipeline for analyses of fungal internal transcribed spacer
sequences from the I llumina sequencing platform. \emph{Methods in
Ecology and Evolution, 6,}973-980. doi: 10.1111/2041-210x.12399 Ha, M.
J., Kim, J., Galloway-Pena, J., Do, K. A., \& Peterson, C. B. (2020).
Compositional zero-inflated network estimation for microbiome data.
\emph{BMC Bioinformatics, 21}, 581. doi: 10.1186/s12859-020-03911-w
Halwachs, B., Madhusudhan, N., Krause, R., Nilsson, R. H.,
Moissl-Eichinger, C., Hogenauer, C., \& Gorkiewicz, G. (2017). Critical
issues in mycobiota analysis. \emph{Frontiers in microbiology, 8}, 180.
doi: 10.3389/fmicb.2017.00180 Hamady, M., Lozupone, C., \& Knight, R.
(2010). Fast Unifrac: Facilitating high-throughput phylogenetic analyses
of microbial communities including analysis of pyrosequencing and
PhyloChip data.\emph{The ISME Journal, 4}, 17-27. doi:
10.1038/ismej.2009.97 Hannula, S. E., Morrien, E., de Hollander, M., van
der Putten, W. H., van Veen, J. A., \& de Boer, W. (2017). Shifts in
rhizosphere fungal community during secondary succession following
abandonment from agriculture. \emph{The ISME Journal, 11}, 2294--2304.
doi: 10.1038/ismej.2017.90 Hanson, C. A., Allison, S. D., Bradford, M.
A., Wallenstein, M. D., \& Treseder, K. K. (2008). Fungal taxa target
different carbon sources in forest soil. \emph{Ecosystems, 11},
1157-1167. doi: 10.1007/s10021-008-9186-4 He, Y., Caporaso, J. G.,
Jiang, X-T., Sheng, H-F., Huse, S. M., Rideout, J. R., \ldots{} Zhou,
H-W. (2015). Stability of operational taxonomic units: An important but
neglected property for analyzing microbial diversity. \emph{Microbiome,
3}, 20. doi: 10.1186/s40168-015-0081-x Heeger, F., Bourne, E. C.,
Baschien, C., Yurkov, A., Bunk, B., Sproer, C., \ldots{} Monaghan, M. T.
(2018). Long-read DNA metabarcoding of ribosomal rRNA in the analysis of
fungi from aquatic environments.\emph{Molecular Ecology Resources, 18},
1500-1514. doi: 10.1111/1755-0998.12937 Hildebrand, F., Tadeo, R.,
Wright, A. Y., Bork, P., \& Raes, J. (2014). LotuS: An efficient and
user-friendly OTU processing pipeline.\emph{Microbiome, 2}, 30.doi:
10.1186/2049-2618-2-30 Holm, J. B., Humphrys, M. S., Robinson, C. K.,
Settles, M. L., Ott, S., Fu, L., \& Ravel, J. (2020).
Ultrahigh-throughput multiplexing and sequencing of
\textgreater{}500-base-pair amplicon regions on the Illumina HiSeq 2500
platform. \emph{mSystems, 4}, e00029-19. doi: 10.1128/mSystems.00029-19
Horn, S., Caruso, T., Verbruggen, E., Rillig, M. C., \& Hempel, S.
(2014). Arbuscular mycorrhizal fungal communities are phylogenetically
clustered at small scales. \emph{The ISME Journal, 8}, 2231-2242. doi:
10.1038/ismej.2014.72 Hugerth, L. W., \& Andersson, A. F. (2017).
Analysing microbial community composition through amplicon sequencing:
From sampling to hypothesis testing. \emph{Frontiers in Microbiology,
8}, 1561. doi: 10.3389/fmicb.2017.01561 Ihrmark, K., Bodeker, I. T. M.,
Cruz-Martinez, K., Friberg, H., Kubartova, A., Schenk, J., \ldots{}
Lindahl, B. D. (2012). New primers to amplify the fungal ITS2 region -
evaluation by 454-sequencing of artificial and natural communities.
\emph{FEMS Microbiology Ecology, 83,} 666-677. doi:
10.1111/j.1574-6941.2012.01437.x Ikenaga, M., Tabuchi, M., Kawauchi, T.,
\& Sakai, M. (2016). Application of Locked Nucleic Acid (LNA) primer and
PCR clamping by LNA oligonucleotide to enhance the amplification of
Internal Transcribed Spacer (ITS) regions in investigating the community
structures of plant--associated fungi. \emph{Microbes and Environments,
31}, 339-348. doi: 10.1264/jsme2.me16085 Jalili, V., Afgan, E., Gu, Q.,
Clements, D., Blankenberg, D., Goecks, J., \ldots{} Nekrutenko, A.
(2020). The Galaxy platform for accessible, reproducible and
collaborative biomedical analyses: 2020 update.\emph{Nucleic Acids
Research, 48}, W395-W402. doi: 10.1093/nar/gkaa434 Jarman, S. N., Berry,
O., \& Bunce, M. (2018). The value of environmental DNA biobanking for
long-term biomonitoring. \emph{Nature Ecology \& Evolution, 2},
1192-1193. doi: 10.1038/s41559-018-0614-3 Joos, L., Beirinckx, S.,
Haegeman, A., Debode, J., Vandecasteele, B., Baeyen, S., \& De Tender,
C. (2020). Daring to be differential: Metabarcoding analysis of soil and
plant-related microbial communities using amplicon sequence variants and
operational taxonomical units.\emph{BMC Genomics, 21}, 733. doi:
10.1186/s12864-020-07126-4 Jumpponen, A., \& Jones, K. L. (2009).
Massively parallel 454 sequencing indicates hyperdiverse fungal
communities in temperate Quercus macrocarpa phyllosphere. \emph{New
Phytologist, 184}, 438-448. doi: 10.1111/j.1469-8137.2009.02990.x Karst,
S. M., Ziels, R. M., Kirkegaard, R. H., Sorensen, E. A., McDonald, D.,
Zhu, Q., \ldots{} Albertsen, M. (2021). High-accuracy long-read amplicon
sequences using unique molecular identifiers with nanopore or PacBio
sequencing. \emph{Nature Methods, 18}, 165-169. doi:
10.1038/s41592-020-01041-y Kembel, S. W., Cowan, P. D., Helmus, M. R.,
Cornwell, W. K., Morlon, H., Ackerly, D. D., \ldots{} Webb, C. O.
(2010). Picante: R tools for integrating phylogenies and ecology.
\emph{Bioinformatics, 26}, 1463-1464. doi: 10.1093/bioinformatics/btq166
Kennedy, P. G., Cline, L. C., \& Song, Z. (2018). Probing promise versus
performance in longer read fungal metabarcoding. \emph{New Phytologist,
217}, 973--976. doi: 10.1111/nph.14883 Kolarikova, Z., Slavikova, R.,
Kruger, C., Kruger, M., \& Kohout, P. (2021). PacBio sequencing of
\emph{Glomeromycota} rDNA: A novel amplicon covering all widely used
ribosomal barcoding regions and its applicability in taxonomy and
ecology of arbuscular mycorrhizal fungi.\emph{New Phytologist, 231},
490--499. doi: 10.1111/nph.17372 Koljalg, U., Nilsson, H. R., Schigel,
D., Tedersoo, L., Larsson, K. H., May, T. W., \ldots{} Abarenkov, K.
(2020). The Taxon Hypothesis paradigm---on the unambiguous detection and
communication of taxa.\emph{Microorganisms, 8}, 1910. doi:
10.3390/microorganisms8121910 Koljalg, U., Tedersoo, L., Nilsson, R. H.,
\& Abarenkov, K. (2016). Digital identifiers for fungal species.
\emph{Science, 352}, 1182-1183. doi: 10.1126/science.aaf7115 Kuczynski,
J., Liu, Z., Lozupone, C., McDonald, D., Fierer, N., \& Knight, R.
(2010). Microbial community resemblance methods differ in their ability
to detect biologically relevant patterns. \emph{Nature Methods, 7},
769-775. doi: 10.1038/nmeth.1499 Kumar, G., Eble, J. E., \& Gaither, M.
R. (2020). A practical guide to sample preservation and pre\selectlanguage{english}-PCR
processing of aquatic environmental DNA.\emph{Molecular Ecology
Resources, 20}, 29-39. doi: 10.1111/1755-0998.13107 Kurtz, Z. D.,
Muller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., \&
Bonneau, R. A. (2015). Sparse and compositionally robust inference of
microbial ecological networks. \emph{PLoS Computational Biology, 11},
e1004226. doi: 10.1371/journal.pcbi.1004226 Kyaschenko, Y., Clemmensen,
K., Hagenbo, A., Karltun, E., \& Lindahl, B. (2017). Shift in fungal
communities and associated enzyme activities along an age gradient of
managed \emph{Pinus sylvestris} stands.\emph{The ISME Journal, 11},
863--874. doi: 10.1038/ismej.2016.184 Lagerborg, K. A., Normandin, E.,
Bauer, M. R., Adams, G., Figueroa, K., Loreth, C., \ldots{} Hooper, D.
(2021). DNA spike-ins enable confident interpretation of SARS-CoV-2
genomic data from amplicon-based sequencing. \emph{bioRxiv, 2021},
435654. doi: 10.1101/2021.03.16.435654 Laliberte, E., \& Legendre, P.
(2010). A distance\selectlanguage{english}-based framework for measuring functional diversity
from multiple traits. \emph{Ecology, 91}, 299-305. doi:
10.1890/08-2244.1 Larsson, A. J., Stanley, G., Sinha, R., Weissman, I.
L., \& Sandberg, R. (2018). Computational correction of index switching
in multiplexed sequencing libraries. \emph{Nature Methods, 15}, 305-307.
doi: 10.1038/nmeth.4666 Legendre, P., \& Gallagher, E. D. (2001).
Ecologically meaningful transformations for ordination of species data.
\emph{Oecologia, 129}, 271-280. doi: 10.1007/s004420100716 Legendre, P.,
\& Legendre, L. (2012). \emph{Numerical Ecology}. New York: Elsevier.
Lennon, J. T., Muscarella, M. E., Placella, S. A., \& Lehmkuhl, B. K.
(2018). How, when, and where relic DNA affects microbial
diversity.\emph{mBio, 9}, e00637-18. doi: 10.1128/mBio.00637-18 Letunic,
I., \& Bork, P. (2021). Interactive Tree Of Life (iTOL) v5: An online
tool for phylogenetic tree display and annotation. \emph{Nucleic Acids
Research, 49,} W293-W296. doi: 10.1093/nar/gkab301 Li, S., Du, X., Feng,
K., Wu, Y., He, Q., Wang, Z., \ldots{} Escalas, A. (2021). Assessment of
microbial \selectlanguage{greek}α-\selectlanguage{english}diversity in one meter squared topsoil. \emph{Soil Ecology
Letters}, \emph{2021}, 1-3. doi: 10.1007/s42832-021-0111-5 Lin, H., \&
Peddada, S. D. (2020a). Analysis of compositions of microbiomes with
bias correction. \emph{Nature Communications, 11}, 3514. doi:
10.1038/s41467-020-17041-7 Lin, H., \& Peddada, S. D. (2020b). Analysis
of microbial compositions: A review of normalization and differential
abundance analysis. \emph{NPJ biofilms and microbiomes, 6}, 60. doi:
10.1038/s41522-020-00160-w Lindahl, B. D., Nilsson, R. H., Tedersoo, L.,
Abarenkov, K., Carlsen, T., Kj\selectlanguage{ngerman}øller, R., \ldots{} Kauserud, H. (2013).
Fungal community analysis by high-throughput sequencing of amplified
markers -- a user's guide.\emph{New Phytologist, 199}, 288--299. doi:
10.1111/nph.12243 Lindner, D. L., Carlsen, T., Nilsson, R. H., Davey,
M., Schumacher, T., \& Kauserud, H. (2013). Employing 454 amplicon
pyrosequencing to reveal intragenomic divergence in the internal
transcribed spacer rDNA region in fungi. \emph{Ecology and Evolution,
3,} 1751--1764. doi: 10.1002/ece3.586 Liu, C., Cui, Y., Li, X., \& Yao,
M. (2021). Microeco: An R package for data mining in microbial community
ecology. \emph{FEMS Microbiology Ecology, 97}, fiaa255. doi:
10.1093/femsec/fiaa255 Lofgren, L. A., Uehling, J. K., Branco, S.,
Bruns, T. D., Martin, F., \& Kennedy, P. G. (2019). Genome\selectlanguage{english}-based
estimates of fungal rDNA copy number variation across phylogenetic
scales and ecological lifestyles.\emph{Molecular Ecology, 28}, 721-730.
doi: 10.1111/mec.14995 Loit, K., Adamson, K., Bahram, M., Puusepp, R.,
Anslan, S., Kiiker, R., \ldots{} Tedersoo, L. (2019). Relative
Performance of MinION (Oxford Nanopore Technologies) versus Sequel
(Pacific Biosciences) third-generation sequencing instruments in
identification of agricultural and forest fungal pathogens.
\emph{Applied and Environmental Microbiology, 85}, e01368-19. doi:
10.1128/AEM.01368-19 Loos, D., Zhang, L., Beemelmanns, C., Kurzai, O.,
\& Panagiotou, G. (2021). DAnIEL: A user-friendly web server for fungal
ITS amplicon sequencing data. \emph{Frontiers in Microbiology, 12},
720513. doi: 10.3389/fmicb.2021.720513 Lopez-Mondejar, R., Tlaskal, V.,
V\selectlanguage{polish}ě\selectlanguage{english}trovsk\selectlanguage{ngerman}ý, T., \selectlanguage{polish}Š\selectlanguage{english}tursov\selectlanguage{ngerman}á, M., Toscan, R., da Rocha, U. N., \& Baldrian,
P. (2020). Metagenomics and stable isotope probing reveal the
complementary contribution of fungal and bacterial communities in the
recycling of dead biomass in forest soil.\emph{Soil Biology and
Biochemistry, 148}, 107875. doi: 10.1016/j.soilbio.2020.107875 Lovell,
D. R., Chua, X. Y., \& McGrath, A. (2020). Counts: an outstanding
challenge for log-ratio analysis of compositional data in the molecular
biosciences. \emph{NAR Genomics and Bioinformatics, 2}, lqaa040.
Lücking, R., Aime, M. C., Robbertse, B., Miller, A. N., Aoki, T.,
Ariyawansa, H. A., \ldots{} \& Schoch, C. L. (2021). Fungal taxonomy and
sequence-based nomenclature. \emph{Nature Microbiology}, \emph{6},
540-548. Lundberg, D. S., Yourstone, S., Mieczkowski, P., Jones, C. D.,
\& Dangl, J. L. (2013). Practical innovations for high-throughput
amplicon sequencing. \emph{Nature Methods, 10}, 999--1002. doi:
10.1038/nmeth.2634 Mahe, F., Czech, L., Stamatakis, A., Quince, C., de
Vargas, C., Dunthorn, M., \& Rognes, T. (in press). Swarm v3: Towards
tera-scale amplicon clustering. \emph{Bioinformatics}. doi:
10.1093/bioinformatics/btab493 Manion, G., Lisk, M., Ferrier, S.,
Nieto-Lugilde, D., Mokany, K., \& Fitzpatrick, M. C. (2018). \emph{Gdm:
Generalized Dissimilarity Modeling. Available:}
https://CRAN.R-project.org/package=gdm Martin, M. (2011). Cutadapt
removes adapter sequences from high-throughput sequencing reads.
\emph{EMBnet, 17}, 10-12. doi: 10.14806/ej.17.1.200 Matchado, M. S.,
Lauber, M., Reitmeier, S., Kacprowski, T., Baumbach, J., Haller, D., \&
List, M. (2021). Network analysis methods for studying microbial
communities: A mini review. \emph{Computational and Structural
Biotechnology Journal, 19}, 2687-2698. doi: 10.1016/j.csbj.2021.05.001
Matsuoka, S., Sugiyama, Y., Sato, H., Katano, I., Harada, K., \& Doi, H.
(2019). Spatial structure of fungal DNA assemblages revealed with eDNA
metabarcoding in a forest river network in western
Japan.\emph{Metabarcoding and Metagenomics, 3}, e36335. doi:
10.3897/mbmg.3.36335 McKnight, D. T., Huerlimann, R., Bower, D. S.,
Schwarzkopf, L., Alford, R. A., \& Zenger, K. R. (2019). MicroDecon: A
highly accurate read\selectlanguage{english}-subtraction tool for the post\selectlanguage{english}-sequencing removal of
contamination in metabarcoding studies. \emph{Environmental DNA, 1},
14-25. doi: 10.1002/edn3.11 McMurdie, P. J., \& Holmes, S. (2013).
Phyloseq: An R package for reproducible interactive analysis and
graphics of microbiome census data. \emph{PloS ONE, 8,} e61217. doi:
10.1371/journal.pone.0061217 McMurdie, P. J., \& Holmes, S. (2014).
Waste not, want not: Why rarefying microbiome data is inadmissible.
\emph{PLoS Computational Biology, 10,} e1003531. doi:
10.1371/journal.pcbi.1003531 Metsalu, T., \& Vilo, J. (2015). ClustVis:
A web tool for visualizing clustering of multivariate data using
Principal Component Analysis and heatmap. \emph{Nucleic acids research,
43}, W566-W570. doi: 10.1093/nar/gkv468 Mikryukov, V. S., Dulya, O. V.,
Likhodeevskii, G. A., \& Vorobeichik, E. L. (2021). Analysis of
ecological networks in multicomponent communities of microorganisms:
Possibilities, limitations, and potential errors.\emph{Russian Journal
of Ecology, 52}, 188-200. doi: 10.1134/S1067413621030085 Miyauchi, S.,
Kiss, E., Kuo, A., Drula, E., Kohler, A., Sanchez-Garcia, M., \ldots{}
Buee, M. (2020). Large-scale genome sequencing of mycorrhizal fungi
provides insights into the early evolution of symbiotic traits.
\emph{Nature Communications, 11,} 5125. doi: 10.1038/s41467-020-18795-w
Moreira-Grez, B., Vuong, P., Waite, I., Morald, T., Wise, M., \&
Whiteley, A. S. (2019). RNA Stable Isotope Probing
(RNA-SIP).\emph{Methods in Molecular Biology, 2046}, 31-44. doi:
10.1007/978-1-4939-9721-3\_3 Nguyen, N. H., Smith, D., Peay, K., \&
Kennedy, P. (2015). Parsing ecological signal from noise in next
generation amplicon sequencing.\emph{New Phytologist, 205}, 1389--1393.
doi: 10.1111/nph.12923 Nguyen, N. H., Song, Z., Bates, S. T., Branco,
S., Tedersoo, L., Menke, J., \ldots{} Kennedy, P. G. (2016). FUNGuild:
An open annotation tool for parsing fungal community data sets by
ecological guild. \emph{Fungal Ecology, 20}, 241--248. doi:
10.1016/j.funeco.2015.06.006 Nilsson, R. H., Anslan, S., Bahram, M.,
Wurzbacher, C., Baldrian, P., \& Tedersoo, L. (2018). Mycobiome
diversity: High-throughput sequencing and identification of fungi.
\emph{Nature Reviews in Microbiology. 17,}95--109. doi:
10.1038/s41579-018-0116-y Nilsson, R. H., Larsson, K. H., Taylor, A. F.,
Bengtsson-Palme, J., Jeppesen, T. S., Schigel, D., \ldots{} Abarenkov,
K. (2019). The UNITE database for molecular identification of fungi:
Handling dark taxa and parallel taxonomic classifications. \emph{Nucleic
Acids Research, 47,}D259-D264. doi: 10.1093/nar/gky1022 Nilsson, R. H.,
Taylor, A. F., Adams, R. I., Baschien, C., Bengtsson-Palme, J., Cangren,
P., \ldots{} \& Abarenkov, K. (2017). Taxonomic annotation of public
fungal ITS sequences from the built environment--a report from an April
10--11, 2017 workshop (Aberdeen, UK).\emph{MycoKeys}, 28, 65. Nilsson,
R. H., Tedersoo, L., Abarenkov, K., Ryberg, M., Kristiansson, E.,
Hartmann, M., \ldots{} Koljalg, U. (2012). Five simple guidelines for
establishing basic authenticity and reliability of newly generated
fungal ITS sequences. \emph{MycoKeys, 4}, 37--63. doi:
10.3897/mycokeys.4.3606 Noble, W. S. (2009). How does multiple testing
correction work?\emph{Nature biotechnology, 27}, 1135-1137. doi:
10.1038/nbt1209-1135 O'Donnell, K., Ward, T. J., Robert, V. A., Crous,
P. W., Geiser, D. M., \& Kang, S. (2015). DNA sequence-based
identification of Fusarium: Current status and future directions.
\emph{Phytoparasitica, 43}, 583-595. doi: 10.1007/s12600-015-0484-z
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P.,
McGlinn, D., \ldots{} Wagner, H. (2019). \emph{Vegan: Community Ecology
Package.} Available\emph{:} https://CRAN.R-project.org/package=vegan
Oliver, A. K., Brown, S. P., Callaham, M. A., \& Jumpponen, A. (2015).
Polymerase matters: Non-proofreading enzymes inflate fungal community
richness estimates by up to 15\%. \emph{Fungal Ecology, 15}, 86-89. doi:
10.1016/j.funeco.2015.03.003 Oliveros, J. (2007). \emph{An Interactive
Tool for Comparing Lists with Venn Diagrams}. Available:
http://bioinfogp.cnb.csic.es/tools/venny/index.html Ondov, B. D.,
Bergman, N. H., \& Philippy, A. M. (2011). Interactive metagenomic
visualization in a Web browser. \emph{BMC Bioinformatics, 12,} 385. doi:
10.1186/1471-2105-12-385 Opik, M., Vanatoa, A., Vanatoa, E., Moora, M.,
Davison, J., Kalwij, J. M., \ldots{} Zobel, M. (2010). The online
database MaarjAM reveals global and ecosystemic distribution patterns in
arbuscular mycorrhizal fungi (\emph{Glomeromycota}). \emph{New
Phytologist, 188,} 223-241. doi: 10.1111/j.1469-8137.2010.03334.x Paliy,
O., \& Shankar, V. (2016). Application of multivariate statistical
techniques in microbial ecology. \emph{Molecular Ecology, 25},
1032--1057. doi: 10.1111/mec.13536 Palmer, J. M., Jusino, M. A., Banik,
M. T., \& Lindner, D. L. (2018). Non-biological synthetic spike-in
controls and the AMPtk software pipeline improve mycobiome data.
\emph{PeerJ, 6}, e4925. doi: 10.7717/peerj.4925 Pedersen, E. J., Miller,
D. L., Simpson, G. L., \& Ross, N. (2019). Hierarchical generalized
additive models in ecology: An introduction with mgcv. \emph{PeerJ, 7},
e6876. doi: 10.7717/peerj.6876 Pinheiro, J., Bates, D., \& DebRoy, S.
(2011). \emph{Nlme: Linear and nonlinear mixed effects models}
Available: http://cran.rproject.org/web/packages/nlme/ Pinol, J., Senar,
M. A., \& Symondson, W. O. (2019). The choice of universal primers and
the characteristics of the species mixture determine when DNA
metabarcoding can be quantitative. \emph{Molecular Ecology, 28},
407-419. doi: 10.1111/mec.14776 Piombo, E., Abdelfattah, A., Droby, S.,
Wisniewski, M., Spadaro, D., \& Schena, L. (2021). Metagenomics
approaches for the detection and surveillance of emerging and recurrent
plant pathogens.\emph{Microorganisms, 9}, 188. doi:
10.3390/microorganisms9010188 Porras-Alfaro, A., Liu, K. L., Kuske, C.
R., \& Xie, G. (2014). From genus to phylum: Large-subunit and internal
transcribed spacer rRNA operon regions show similar classification
accuracies influenced by database composition. \emph{Applied and
Environmental Microbiology, 80,}829-840. doi: 10.1128/aem.02894-13
Polme, S., Abarenkov, K., Nilsson, R. H., Lindahl, B. D., Clemmensen, K.
E., Kauserud, H., \ldots{} Tedersoo, L. (2020). FungalTraits: A
user-friendly traits database of fungi and fungus-like
stramenopiles.\emph{Fungal Diversity, 105}, 1--16. doi:
10.1007/s13225-020-00466-2 Qian, H., \& Jin, Y. (2016). An updated
megaphylogeny of plants, a tool for generating plant phylogenies and an
analysis of phylogenetic community structure. \emph{Journal of Plant
Ecology, 9,} 233-239. doi: 10.1093/jpe/rtv047 Quast, C., Pruesse, E.,
Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., \ldots{} Glockner, F. O.
(2013). The SILVA ribosomal RNA gene database project: Improved data
processing and web-based tools. \emph{Nucleic Acids Research, 41},
D590--D596. doi: 10.1093/nar/gks1219 Quince, C., Walker, A. W., Simpson,
J. T., Loman, N. J., \& Segata, N. (2017). Shotgun metagenomics, from
sampling to analysis. \emph{Nature Biotechnology, 35}, 833-844. doi:
10.1038/nbt.3935 Quinn, T. P., Erb, I., Gloor, G., Notredame, C.,
Richardson, M. F., \& Crowley, T. M. (2019). A field guide for the
compositional analysis of any-omics data. \emph{GigaScience, 8,} giz107.
doi: 10.1093/gigascience/giz107 Radajewski, S., McDonald, I. R., \&
Murrell, J. C. (2003). Stable-isotope probing of nucleic acids: A window
to the function of uncultured microorganisms. \emph{Current Opinion in
Biotechnology, 14}, 296-302. doi: 10.1016/S0958-1669(03)00064-8 Rajala,
T., Peltoniemi, M., Hantula, J., Makipaa, R., \& Pennanen, T. (2011).
RNA reveals a succession of active fungi during the decay of Norway
spruce logs. \emph{Fungal Ecology, 4}, 437--448. doi:
10.1016/j.funeco.2011.05.005 Rammitsu, K., Kajita, T., Imai, R., \&
Ogura-Tsujita, Y. (2021). Strong primer bias for Tulasnellaceae fungi in
metabarcoding: Specific primers improve the characterization of the
mycorrhizal communities of epiphytic orchids. \emph{Mycoscience, 62},
MYC551. doi: 10.47371/mycosci.2021.06.005 Rao, C., Coyte, K. Z.,
Bainter, W., Geha, R. S., Martin, C. R., \& Rakoff-Nahoum, S. (2021).
Multi-kingdom ecological drivers of microbiota assembly in preterm
infants. \emph{Nature, 591}, 633-638. doi: 10.1038/s41586-021-03241-8
Ricotta, C., Pavoine, S., Cerabolini, B. E., \& Pillar, V. D. (2021). A
new method for indicator species analysis in the framework of
multivariate analysis of variance. \emph{Journal of Vegetation Science,
32}, e13013. doi: 10.1111/jvs.13013 Rillig, M. C., Ryo, M., Lehmann, A.,
Aguilar-Trigueros, C. A., Buchert, S., Wulf, A., \ldots{}Yang, G.
(2019). The role of multiple global change factors in driving soil
functions and microbial biodiversity.\emph{Science, 366}, 886-890. doi:
10.1126/science.aay2832 Rivers, A. R., Weber, K. C., Gardner, T. G.,
Liu, S., \& Armstrong, S. D. (2018). ITSxpress: Software to rapidly trim
internally transcribed spacer sequences with quality scores for marker
gene analysis.\emph{F1000Research, 7}, 1418. doi:
10.12688/f1000research.15704.1 Rognes, T., Flouri, T., Nichols, B.,
Quince, C., \& Mahe, F. (2016). VSEARCH: A versatile open source tool
for metagenomics. \emph{PeerJ, 4,}e2584. doi: 10.7717/peerj.2584 Roy,
J., Mazel, F., Sosa\selectlanguage{english}-Hernandez, M. A., Duenas, J. F., Hempel, S., Zinger,
L., \& Rillig, M. C. (2019). The relative importance of ecological
drivers of arbuscular mycorrhizal fungal distribution varies with taxon
phylogenetic resolution. \emph{New Phytologist, 224,}936-948. doi:
10.1111/nph.16080 Rottjers, L., \& Faust, K. (2018). From hairballs to
hypotheses -- biological insights from microbial networks. \emph{FEMS
Microbiology Reviews, 42}, 761-780. doi: 10.1093/femsre/fuy030 Sato, H.,
Sogo, Y., Doi, H., \& Yamanaka, H. (2017). Usefulness and limitations of
sample pooling for environmental DNA metabarcoding of freshwater fish
communities. \emph{Scientific Reports, 7}, 14860. doi:
10.1038/s41598-017-14978-6 Sato, M. P., Ogura, Y., Nakamura, K.,
Nishida, R., Gotoh, Y., Hayashi, M., \ldots{} Hayashi, T. (2019).
Comparison of the sequencing bias of currently available library
preparation kits for Illumina sequencing of bacterial genomes and
metagenomes. \emph{DNA Research, 26,} 391-398. doi:
10.1093/dnares/dsz017 Schloss, P. D. (2020). Reintroducing mothur: 10
years later.\emph{Applied and Environmental Microbiology, 86,}
e02343-19. doi: 10.1128/AEM.02343-19 Schnell, I. B., Bohmann, K., \&
Gilbert, M. T.P. (2015). Tag jumps illuminated -- reducing
sequence-to-sample misidentifications in metabarcoding studies.
\emph{Molecular Ecology Resources, 15}, 1289--1303. doi:
10.1111/1755-0998.12402 Schwarzenbach, K., Enkerli, J., \& Widmer, F.
(2007). Objective criteria to assess representativity of soil fungal
community profiles.\emph{Journal of Microbiological Methods, 68},
358-366. doi: 10.1016/j.mimet.2006.09.015 Singer, E., Wagner, M., \&
Woyke, T. (2017). Capturing the genetic makeup of the active microbiome
in situ. \emph{The ISME Journal, 11}, 1949-1963. doi:
10.1038/ismej.2017.59 Song, Z., Schlatter, D., Kennedy, P., Kinkel, L.
L., Kistler, H. C., Nguyen, N., \& Bates, S. T. (2015). Effort versus
reward: Preparing samples for fungal community characterization in
high-throughput sequencing surveys of soils. \emph{PLoS ONE, 10,}
e0127234. doi: 10.1371/journal.pone.0127234 Sze, M. A., \& Schloss, P.
D. (2019). The impact of DNA polymerase and number of rounds of
amplification in PCR on 16S rRNA gene sequence data.\emph{mSphere, 26},
e00163-19. doi: 10.1128/mSphere.00163-19 Strobl, C., Hothorn, T., \&
Zeileis, A. (2009). Party on! A new, conditional variable-importance
measure for random forests available in the party package. \emph{R
Journal, 1,} 14--17. doi: 10.32614/RJ-2009-013 Sun, X., Hu, Y. H., Wang,
J., Fang, C., Li, J., Han, M., \ldots{} Gong, M. (2021). Efficient and
stable metabarcoding sequencing data using a DNBSEQ-G400 sequencer
validated by comprehensive community analyses.\emph{Gigabyte, 2021},
1-15. doi: 10.1101/2020.07.02.185710 Taberlet, P., Bonin, A., Zinger,
L., \& Coissac, E. (2018).\emph{Environmental DNA: For biodiversity
research and monitoring}. London: Oxford University Press. Taberlet, P.,
Coissac, E., Pompanon, F., Brochmann, C., \& Willerslev, E. (2012).
Towards next\selectlanguage{english}-generation biodiversity assessment using DNA metabarcoding.
\emph{Molecular Ecology, 21}, 2045-2050. doi:
10.1111/j.1365-294x.2012.05470.x Talas, L., Stivrins, N., Veski, S.,
Tedersoo, L., \& Kisand, V. (2021). Sedimentary ancient DNA (sedaDNA)
reveals fungal diversity and environmental drivers of community changes
throughout the holocene in the present boreal Lake Lielais Sv\selectlanguage{polish}ē\selectlanguage{english}ti\selectlanguage{polish}ņ\selectlanguage{english}u
(Eastern Latvia).\emph{Microorganisms, 9}, 719. doi:
10.3390/microorganisms9040719 Tanabe, A. S., \& Toju, H. (2013). Two new
computational methods for universal DNA Barcoding: A benchmark using
barcode sequences of bacteria, archaea, animals, fungi, and land plants.
\emph{PLoS ONE, 8}, e76910. doi: 10.1371/journal.pone.0076910 Taylor, D.
L., Hollingsworth, T. N., McFarland, J. W., Lennon, N. J., Nussbaum, C.,
\& Ruess, R. W. (2014). A first comprehensive census of fungi in soil
reveals both hyperdiversity and fine-scale niche partitioning.
\emph{Ecological Monographs, 84}, 3-20. doi: 10.1890/12-1693.1 Tedersoo,
L., Albertsen, M., Anslan, S., \& Callahan, B. (2021a). Perspectives and
benefits of high-throughput long-read sequencing in microbial ecology.
\emph{Applied and Environmental Microbiology, 87}, e00626-21. doi:
10.1128/aem.00626-21 Tedersoo, L., Anslan, S., Bahram, M., Drenkhan, R.,
Pritsch, K., Buegger, F., \ldots{} Abarenkov, K. (2020). Regional-scale
in-depth analysis of soil fungal diversity reveals strong pH and plant
species effects in Northern Europe. \emph{Frontiers in Microbiology,
11,} 1953. doi: 10.3389/fmicb.2020.01953 Tedersoo, L., Anslan, S.,
Bahram, M., K\selectlanguage{ngerman}õljalg, U., \& Abarenkov, K. (2020). Identifying the
`unidentified'fungi: A global-scale long-read third-generation
sequencing approach. \emph{Fungal Diversity, 103,}273-293. doi:
10.1007/s13225-020-00456-4 Tedersoo, L., Anslan, S., Bahram, M., Põlme,
S., Riit, T., Liiv, I., \ldots{} Abarenkov, K. (2015). Shotgun
metagenomes and multiple primer pair-barcode combinations of amplicons
reveal biases in metabarcoding analyses of fungi. \emph{MycoKeys, 10},
1-43. doi: 10.3897/mycokeys.10.4852 Tedersoo, L. et al. 2021b. GSMc.
\emph{Fungal Diversity}, pending revision. Tedersoo, L., Bahram, M.,
Põlme, S., Kõljalg, U., Yorou, N. S., Wijesundera, R., \ldots{}
Abarenkov, K. (2014). Global diversity and geography of soil fungi.
\emph{Science, 346}, 1078. doi: 10.1126/science.1256688 Tedersoo, L., \&
Lindahl, B. (2016). Fungal identification biases in microbiome projects.
\emph{Environmental Microbiology Reports, 8}, 774-779. doi:
10.1111/1758-2229.12438 Tipton, L., Müller, C. L., Kurtz, Z. D., Huang,
L., Kleerup, E., Morris, A., \ldots{} Ghedin, E. (2018). Fungi stabilize
connectivity in the lung and skin microbial ecosystems.
\emph{Microbiome, 6}, 12. doi: 10.1186/s40168-017-0393-0 Tucker, C. M.,
Cadotte, M. W., Carvalho, S. B., Davies, T. J., Ferrier, S., Fritz, S.
A., \ldots{} Pavoine, S. (2017). A guide to phylogenetic metrics for
conservation, community ecology and macroecology.\emph{Biological
Reviews, 92,} 698-715. doi: 10.1111/brv.12252 Turon, X., Antich, A.,
Palacín, C., Præbel, K., \& Wangensteen, O. S. (2020). From
metabarcoding to metaphylogeography: Separating the wheat from the
chaff. \emph{Ecological Applications, 30}, e02036. doi: 10.1002/eap.2036
U'Ren, J. M., Lutzoni, F., Miadlikowska, J., Zimmerman, N. B., Carbone,
I., May, G., \& Arnold, A. E. (2019). Host availability drives
distributions of fungal endophytes in the imperilled boreal
realm.\emph{Nature Ecology \& Evolution, 3,} 1430-1437. doi:
10.1038/s41559-019-0975-2 U'Ren, J. M., Riddle, J. M., Monacell, J. T.,
Carbone, I., Miadlikowska, J., \& Arnold, A. E. (2014). Tissue storage
and primer selection influence pyrosequencing\selectlanguage{english}-based inferences of
diversity and community composition of endolichenic and endophytic
fungi. \emph{Molecular Ecology Resources, 14}, 1032-1048. doi:
10.1111/1755-0998.12252 van Dijk, E. L., Jaszczyszyn, Y., Naquin, D., \&
Thermes, C. (2019). The third revolution in sequencing technology.
\emph{Trends in Genetics, 34}, 666-681. doi: 10.1016/j.tig.2018.05.008
Vasar, M., Davison, J., Neuenkamp, L., Sepp, S. K., Young, J. P. W.,
Moora, M., \& Opik, M. (2021). User\selectlanguage{english}-friendly bioinformatics pipeline
gDAT (graphical downstream analysis tool) for analysing rDNA
sequences.\emph{Molecular Ecology Resources, 21}, 1380-1392. Vestheim,
H., Deagle, B. E., \& Jarman, S. N. (2011). Application of blocking
oligonucleotides to improve signal-to-noise ratio in a PCR.\emph{Methods
in Molecular Biology, 687}, 265-274. doi: 10.1007/978-1-60761-944-4\_19
Vetrovsky, T., Baldrian, P., Morais, D., \& Berger, B. (2018). SEED 2: A
user-friendly platform for amplicon high-throughput sequencing data
analyses. \emph{Bioinformatics, 34}, 2292--2294. doi:
10.1093/bioinformatics/bty071 Vetrovsky, T., Morais, D., Kohout, P.,
Lepinay, C., Algora, C., Awokunle Holla, S., \ldots{} Baldrian, P.
(2020). GlobalFungi, a global database of fungal occurrences from
high-throughput-sequencing metabarcoding studies. \emph{Scientific Data,
7}, 228. doi: 10.1038/s41597-020-0567-7 Wagner, A. O., Malin, C., Knapp,
B. A., \& Illmer, P. (2008). Removal of free extracellular DNA from
environmental samples by ethidium monoazide and propidium monoazide.
\emph{Applied and Environmental Microbiology, 74}, 2537-2539. doi:
10.1128/AEM.02288-07 Wang, F., Che, R., Deng, Y., Wu, Y., Tang, L., Xu,
Z., \ldots{} Cui, X. (2021). Air-drying and long time preservation of
soil do not significantly impact microbial community composition and
structure.\emph{Soil Biology and Biochemistry, 157}, 108238.
10.1016/j.soilbio.2021.108238 Wang, H., Qi, J., Xiao, D., Wang, Z., \&
Tian, K. (2017). A re-evaluation of dilution for eliminating PCR
inhibition in soil DNA samples. \emph{Soil Biology and Biochemistry,
106}, 109-118. doi: 10.1016/j.soilbio.2016.12.011 Wang, Q., Garrity, G.
M., Tiedje, J. M., \& Cole, J. R. (2007). Naive Bayesian classifier for
rapid assignment of rRNA sequences into the new bacterial taxonomy.
\emph{Applied and Environmental Microbiology, 73}, 5261-5267. doi:
10.1128/aem.00062-07 Washburne, A. D., Morton, J. T., Sanders, J.,
McDonald, D., Zhu, Q., Oliverio, A. M., \& Knight, R. (2018). Methods
for phylogenetic analysis of microbiome data. \emph{Nature Microbiology,
3}, 652-661. doi: 10.1038/s41564-018-0156-0 Webb, C. O., Ackerly, D. D.,
\& Kembel, S. W. (2008). \emph{Phylocom: Software for the Analysis of
Community Phylogenetic Structure and Trait Evolution, Version 4.0.1}
{[}software{]}. Retrieved from http://www.phylodiversity.net/phylocom/
Weete, J. D., Abril, M., \& Blackwell, M. (2010). Phylogenetic
distribution of fungal sterols. \emph{PloS ONE, 5,} e10899. doi:
10.1371/journal.pone.0010899 Weiss, S., Van Treuren, W., Lozupone, C.,
Faust, K., Friedman, J., Deng, Y., \ldots{} Birmingham, A. (2016).
Correlation detection strategies in microbial data sets vary widely in
sensitivity and precision. \emph{The ISME Journal, 10}, 1669-1681. doi:
10.1038/ismej.2015.235 White, J. R., Maddox, C., White, O., Angiuoli, S.
V., \& Fricke, W. F. (2013). CloVR-ITS: automated internal transcribed
spacer amplicon sequence analysis pipeline for the characterization of
fungal microbiota. \emph{Microbiome, 1}, 6. doi: 10.1186/2049-2618-1-6
Witek, K., Jupe, F., Witek, A. I., Baker, D., Clark, M. D., \& Jones, J.
D. (2016). Accelerated cloning of a potato late blight--resistance gene
using RenSeq and SMRT sequencing. \emph{Nature Biotechnology,
34,}656-660. doi: 10.1038/nbt.3540 Yang, T., Adams, J. M., Shi, Y., He,
J-S., Jing, X., Chen, L., Tedersoo, L., \& Chu, H. (2017). Plant
diversity and productivity drive soil fungal richness in natural
grasslands of the Tibetan Plateau. \emph{New Phytologist, 215},
756--765. doi: 10.1111/nph.14606 Yoon, G., Gaynanova, I., \& Muller, C.
L. (2019). Microbial networks in SPRING - semi-parametric rank-based
correlation and partial correlation estimation for quantitative
microbiome data. \emph{Frontiers in Genetics, 10}, 516. doi:
10.3389/fgene.2019.00516 Zafeiropoulos, H., Viet, H. Q., Vasileiadou,
K., Potirakis, A., Arvanitidis, C., Topalis, P., \ldots{} Pafilis, E.
(2020). PEMA: a flexible Pipeline for Environmental DNA Metabarcoding
Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes.
\emph{GigaScience, 9}, giaa022. doi: 10.1093/gigascience/giaa022 Zaiko,
A., Greenfield, P., Abbott, C., von Ammon, U., Bilewitch, J., Bunce, M.,
Cristescu, M. E., Chariton, A., Dowle, E., Geller, J., \& Ardura
Gutierrez, A. (in press). Towards reproducible metabarcoding
data\selectlanguage{english}-lessons from an international cross\selectlanguage{english}-laboratory
experiment.\emph{Molecular Ecology Resources}. doi:
10.1111/1755-0998.13485 Zanne, A. E., Abarenkov, K., Afkhami, M. E.,
Aguilar-Trigueros, C. A., Bates, S., Bhatnagar, J. M., \ldots{} Moreno,
H. F. (2020). Fungal functional ecology: Bringing a trait-based approach
to plant-associated fungi. \emph{Biological Reviews, 95}, 409-433. doi:
10.1111/brv.12570 Zhang, Z. F., Pan, Y. P., Liu. Y., \& Li, M. (2021).
High diversity of basal fungal lineages and stochastic processes
controlled fungal community assembly in mangrove sediments.
\emph{Applied and Environmental Microbiology, 87,} e00928-21. doi:
10.1128/AEM.00928-21 Zhou, J., Deng, Y., Shen, L., Wen, C., Yan, Q.,
Ning, D., \ldots{} Brown, J. H. (2016). Temperature mediates
continental-scale diversity of microbes in forest soils. \emph{Nature
Communications, 7}, 12083. doi: 10.1038/ncomms12083 Zifcakova, L.,
Vetrovsky, T., Howe, A., \& Baldrian, P. (2016). Microbial activity in
forest soil reflects the changes in ecosystem properties between summer
and winter. \emph{Environmental Microbiology, 18}, 288-301. doi:
10.1111/1462-2920.13026 Zimmerman, N. B., \& Vitousek, P. M. (2012).
Fungal endophyte communities reflect environmental structuring across a
Hawaiian landscape. \emph{Proceedings of the National Academy of
Sciences, 109}, 13022-13027. doi: 10.1073/pnas.1209872109 Zinger, L.,
Bonin, A., Alsos, I. G., Balint, M., Bik, H., Boyer, F., \ldots{} De
Barba, M. (2019a). DNA metabarcoding---Need for robust experimental
designs to draw sound ecological conclusions.\emph{Molecular Ecology,
28}, 1857-1862. doi: 10.1111/mec.15060 Zinger, L., Lionnet, C.,
Benoiston, A. S., Donald, J., Mercier, C., \& Boyer, F. (2021). metabaR:
an R package for the evaluation and improvement of DNA metabarcoding
data quality. \emph{Methods in Ecology and Evolution, 12,} 586-592. doi:
10.1111/2041-210X.13552 Zinger, L., Taberlet, P., Schimann, H., Bonin,
A., Boyer, F., De Barba, M., \ldots{} Chave, J. (2019b). Body size
determines soil community assembly in a tropical forest. \emph{Molecular
Ecology, 28}, 528-543. doi: 10.1111/mec.14919

\textbf{Table 1.} Properties of bioinformatics pipelines used for fungal
metabarcoding. Interface: command line (CL).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Software & Interface\textsuperscript{1} & Type & Target group & ITS
extraction & Unique features & Reference\tabularnewline
QIIME2 & graphical, CL & offline, multiple included dependencies &
prokaryote, short-read & ITSxpress & interactive data visualisation;
Deblur filtering & Bolyen et al., 2019\tabularnewline
mothur & CL & offline & prokaryote, short-read & - & OptiClust
clustering & Schloss, 2020\tabularnewline
Claident & CL & offline & universal, short-read & ITSx & noise removal
options & Tanabe \& Toju, 2013\tabularnewline
PIPITS & CL & offline & fungi, ITS1, ITS2 & ITSx & - & Gweon et al.,
2015\tabularnewline
SEED2 & graphical & offline & universal, short-read & ITSx compatible &
read visualisation & Vetrovsky et al., 2018\tabularnewline
PEMA & CL & offline, multiple included dependencies & universal,
short-read & - & parameter file for automated pipeline & Zafeiropoulos
et al., 2020\tabularnewline
PipeCraft & graphical & offline, multiple included dependencies &
universal, short/long-read & ITSx & multiple HTS platform- and
data-specific options & Anslan et al., 2017\tabularnewline
DADA2 & CL (R-Studio) & offline & universal, short/long-read & - & ASV
calculation & Callahan et al., 2016\tabularnewline
LOTUS2 & CL & offline & universal, short/long-read & ITSx & low RAM
usage, false positives removal, & Hildebrand et al., 2014\tabularnewline
Galaxy & graphical & online & prokaryote, short-read & - & links to
other Galaxy functions & Jalili et al., 2020\tabularnewline
FROGS & graphical & online & prokaryote, short-read & ITSx & Galaxy
functions & Escudie et al., 2018\tabularnewline
AMPtk & CL & offline & universal, short-read & - & hybrid method for
taxonomy assignment; spike-in control & Palmer et al.,
2018\tabularnewline
OBITools & CL & offline & universal, short-read & - & - & Boyer et al.,
2016\tabularnewline
SCATA & graphical & online & fungi, short/long-read & ITSx & unique
clustering options & https://scata.mykopat.slu.se/\tabularnewline
gDAT & graphical & offline & universal, short-read & - & - & Vasar et
al., 2021\tabularnewline
Cascabel & CL & offline & universal, short-read & - & graphical report-
& Abdala Asbun et al., 2020\tabularnewline
MICCA & CL & offline & universal, short-read & - & OTUCLUST clustering
algorithm & Albanese et al., 2015\tabularnewline
SWARM & CL & offline & universal, short-read & - & local single-linkage
clustering & Mahe et al., 2021\tabularnewline
CloVR-ITS & CL & offline/online & fungi, short-read & - & Virtual
machine-based with the possibility to run on Amazon EC2 Cloud & White et
al., 2013\tabularnewline
DAnIEL & graphical & online & fungi, short-read & ITSx (via PIPITS) &
raw data download from SRA & Loos et al., 2021\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{1}CL, command line

\textbf{Figure 1.} Primer map of the rRNA operon internal transcribed
spacer (ITS) region. Primers developed for metabarcoding studies are in
bold; fungi-specific primers are underlined. Primers used for global
mycobiome projects are indicated in red (all fungi), purple
(\emph{Glomeromycota} ) and blue (Earth Microbiome project).
\textless{}I\textgreater{}, common intron sites. Updated from Nilsson et
al. (2018).

\textbf{Figure 2.} Common and perspective library preparation strategies
for Illumina sequencing: (a) adding indexes and adapters using
amplification with fusion primers; (b) adding indexes with amplification
and adapters by ligation; (c) amplification and then adding indexes and
adapters in second PCR step; (d) indexing samples with combinations of
Illumina indices (Holm et al., 2020); and (e) incorporating unique
molecular identifiers (UMIs) in the first amplification step
(modification of Karst et al., 2021). Libraries of other HTS platforms
require more specific protocols.

\textbf{Figure 3.} Non-metric multidimensional scaling (NMDS) graphs
illustrating relative performance of various methods and dissimilarity
(B-C, Bray-Curtis or Aitchison) measures in recovering trends in
microbial eukaryote composition using untransformed and
Hellinger-transformed data matrices in plant roots (filled circles) and
leaves (open circles) in terrestrial (orange) and aquatic (blue)
habitats: (a, b) non-rarefied data; (c, d) rarefied data; (e, f) scaling
with ranked subsampling (SRS) normalised data; (g, h) centered log-ratio
(CLR) transformed data. Numbers on symbols indicate plant species
(separate numbering for terrestrial and aquatic plants); ellipses depict
95\% CI for tissue and habitat combinations. Explained variation (\%) as
revealed from Permanova+ analysis is indicated (t x h, tissue and
habitat interaction; seqs, sequencing depth). Plant species effects are
not analysed here for simplicity. Data from A. Azadnia, V. Mikryukov, L.
Tedersoo (unpublished).

\textbf{Box1.} Trade-offs in Sample Pooling

To improve representativeness of the samples at minimum extra cost,
pooling statistically non-independent subsamples is a widely used
option. The number and spatial distance of subsamples may be of great
importance to provide a representative view of the microbial diversity
in heterogeneous habitats; less inclusive subsampling designs are likely
to result in underestimating diversity (Figure Box1). The number of
subsamples to be pooled depends on the research question and the size of
the area, with 7-25 being optimal in most cases (Schwarzenbach et al.,
2007). Both physical and analytical pooling improve richness and
composition assessments of soil fungi (Schwarzenbach et al., 2007, Song
et al., 2015) and reduce estimate variance (Dickie et al., 2018).
However, pooling of physical samples may result in the loss of patchily
occurring rare taxa (e.g., in extremely dilute fish eDNA samples with a
detection threshold of 0.05\% of total relative abundance at deep
sequencing; Sato et al., 2017). These results may be relevant for fungal
groups of relatively low DNA content and/or rRNA copy numbers,
e.g.\emph{Glomeromycota} and unicellular taxa. It is likely that the
pooling effect depends on habitat heterogeneity, including pH, organic
matter content, salinity and plant species present - all of which are
factors known to affect fungal composition in different environments
(Amend et al., 2019; Grossart et al., 2019; Nilsson et al., 2019; U'Ren
et al., 2019). Therefore, pooling samples with potentially different
microbial composition (e.g., leaves of different plant species) is not
recommended. Theoretically, pooling does not work optimally in
situations where the samples contain different amounts of DNA and where
the low-DNA samples feature unique, rare species. Given the greater
overall richness, pooled samples also require deeper sequencing to
detect rare taxa. Furthermore, pooling is unsuited for co-occurrence
analyses assessing biotic interactions (Bahram et al., 2014). Pooling
individual samples at the site level (at the phase of DNA extraction,
PCR, library preparation or sequence data) may be the most useful when
these samples cannot be used as independent replicates (local- or
landscape-scale spatial autocorrelation), e.g. for regional- to
global-scale analyses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure Box1.} Potential underestimation of biodiversity and high
variance at low number of (sub)samples: (a) OTU richness, (b) Shannon
index of diversity and (c) Effective number of species (here as the
exponential of the Shannon index). Rarefied datasets from six sites
(Zhou et al., 2016) were randomized 100 times to generate subsets of
various sample sizes representing composite, analytically pooled
samples. These samples were further rarefied at the same level for
calculating diversity values. Note that the differences would be greater
without both rarefaction steps. Symbols represent means of analytically
pooled samples, and error bars indicate 95\% CI. The differences between
a single sample and 21 randomly selected samples average 18.7\%
(+-3.6\%), 4.4\% (+-0.9\%) and 35.7\% (+-7.9\%) for (a), (b) and (c),
respectively.

\textbf{Box 2.} Amplified sequence variant (ASV) methods

ASV approaches represent a specific type of greedy \emph{de
novo}clustering and several alternative methods - notably DADA2
(Callahan et al., 2016), UNOISE (Edgar, 2016) and deblur (Amir et al.,
2017) - have recently become popular in microbiology including fungal
ecology (Glassman et al., 2018). In DADA2, ASVs correspond to
100\%-similarity OTUs, where sample-wise rare variants are assigned to
dominant haplotypes based on an error model with stringent settings
(Callahan et al., 2016). Deblur is relatively less conservative (Amir et
al., 2017) and relatively inefficient in removing rare haplotypes (Li et
al., 2021). The ASV approaches are certainly useful for separating as
many taxa as possible based on conserved genes, but their utility for
ITS and protein-encoding genes is unclear. They may outperform
traditional approaches in distinguishing the aforementioned saprotrophic
and pathogenic \emph{Ascomycota} with haploid genomes. However, it
severely biases diversity estimates of metazoans based on the Cytochrome
Oxidase 1 (CO1) gene (Brandt et al., 2021) and it is expected to perform
poorly for fungal groups with dikaryotic (\emph{Basidiomycota} ),
diploid (most unicellular groups), and polyploid (\emph{Glomeromycota} )
genomes that commonly exhibit two or multiple different rRNA gene and
ITS copies per genome or even within haploid nuclei (Lindner et al.,
2013; Egan et al., 2018). Estensmo et al. (2021) demonstrated that in
polypores, the ASVs significantly overestimated species richness. Using
a re-analysis of a dataset from Furneaux et al. (2021), we show that the
ASV approach reduces phylogenetic richness by disproportionately
eliminating rare members of the unicellular fungal groups,
\emph{Glomeromycota} and non-fungal eukaryotes (Figure Box2). In terms
of beta diversity, the results are similar between ASV and OTU-based
approaches (Glassman et al., 2018). If one is interested in using the
denoising algorithm of DADA2, a subsequent post-clustering of ASVs
(implemented in pipelines such as LotuS2 and amptk) may be a solution
(Furneaux et al., 2021; Estensmo et al., 2021), but this approach does
not ameliorate the loss of true unique species.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure Box2.} Comparison of OTU-based (OTU\textbf{-s} ,
singletons removed) and ASV-based approaches for inferring taxonomic
richness using the ITS2 subregion, full ITS region, and ITS + 28S based
on the dataset of Furneaux et al. (2021), which was taxonomically
re-analysed following Tedersoo et al. (in press). (a) average number of
reads retained; (b) richness of OTUs/ASVs; (c) richness of fungal
orders; and (d) kingdom and phylum-level distribution of OTUs/ASVs
across the entire dataset. In (d), letters indicate statistically
significant differences among groups based on Scheffe PostHoc tests
following sample-wise testing of log-ratio transformed proportions using
two-way ANOVAs including the two factors marker length and matrix type -
OTU, OTUs without singletons (OTU-s) and ASV. We conclude that 1) the
ASV approach and OTU-based approach with singletons removed recover
lower proportions of non-Dikarya and non-fungal taxa compared with the
OTU-based approach including singletons; 2) analyses based on the ITS2
region alone revealed a higher proportion of OTUs/ASVs that could not be
identified to a fungal phylum or eukaryote kingdom compared with longer
marker fragments due to less taxonomic information; 3) longer markers
had fewer sequences passing the quality control and revealed relatively
more singletons, suggesting accumulation of artefacts across the entire
1500-base amplicon; and 4) the ASV approach performs suboptimally for
metabarcoding of fungi and particularly poorly for the full ITS region
and longer markers.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}

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