ITS Amplicon Sequencing · Fungal Community Profiling

Profile fungal communities. From soil to clinic.

End-to-end Internal Transcribed Spacer (ITS) amplicon sequencing and analysis for fungal community characterisation across environmental, agricultural and clinical samples — targeting the standard fungal barcoding marker.

Plan Your Project → See the Workflow
97.2%
Mean processed-data Q30
~410
Mean ASVs per sample
~95
Mean genera per sample
ASV
Denoising-based resolution
About the Service

Fungal barcoding via ITS1 / ITS2

Nucleome Informatics offers end-to-end ITS amplicon sequencing and data analysis services for fungal community characterisation across environmental, agricultural and clinical samples.

The ITS region is the standard fungal barcoding marker. Sequencing of the ITS1 or ITS2 sub-region provides taxonomic resolution of fungal communities in a targeted, cost-effective manner.

Our analysis combines sequencing quality control, primer and adapter trimming, denoising into amplicon sequence variants (ASVs), chimera removal and taxonomic classification against curated fungal reference databases, along with diversity and comparative community analysis.

  • Amplicon sequencing data quality assessment and preprocessing
  • Primer trimming and quality filtering of ITS1/ITS2 reads
  • Denoising into amplicon sequence variants (ASVs)
  • Chimera detection and removal
  • Taxonomic classification against curated fungal reference databases
  • Alpha- and beta-diversity analysis
  • Comparative and differential abundance analysis across groups
  • Secure delivery of raw, intermediate and final analysis files

ASV-Based Resolution

Denoising-based resolution of amplicon sequence variants delivers improved taxonomic accuracy over traditional OTU clustering — resolving true biological variants rather than arbitrary clusters.


Flexible Marker Choice

Choose ITS1 or ITS2 targeting to suit your study objectives and the taxonomic resolution your questions require.

Technology Highlights

Built for fungal community analysis

From marker selection through diversity statistics — a workflow designed for reproducible, high-resolution fungal profiling.

ITS1 / ITS2 Targeting

Flexible marker-region selection to suit study objectives and taxonomic resolution requirements.

ASV-Based Analysis

Denoising-based resolution of amplicon sequence variants for improved accuracy over traditional OTU clustering.

Curated Fungal Databases

Taxonomic assignment using established, curated fungal ITS reference databases.

Chimera-Checked Workflow

Systematic detection and removal of chimeric sequences prior to taxonomic assignment.

Diversity Analysis

Alpha- and beta-diversity metrics with group-wise statistical comparisons.

Comparative Analysis

Community composition and abundance compared across samples or experimental groups.

Learning Path

From first principles to expert-level analysis

New to fungal community sequencing? Start with Foundations. Planning a study or reviewing results? Jump to Practitioner or Expert. Each level builds on the one before it, so you can read this page as a short course or dip in where you need to.

The big idea in plain language: every fungus carries a stretch of DNA called ITS that works like a species barcode. Reading that barcode from a sample tells you which fungi are present and in what proportions.

LEVEL 1

Fungi are everywhere, mostly unseen

Soil, plant roots, indoor air, skin and gut all hold diverse fungal communities. Most fungi are microscopic and cannot be grown easily, so DNA is the practical way to identify them.

LEVEL 1

ITS is the fungal barcode

The internal transcribed spacer (ITS) sits between ribosomal genes. It changes quickly enough to separate species, which is why it is the standard fungal barcoding marker.

LEVEL 1

ITS1 or ITS2

The ITS has two variable sub-regions, ITS1 and ITS2, separated by the short 5.8S gene. Primers copy one of them from every fungus in the sample at once.

LEVEL 1

Reads become variants, then names

The sequencer reads millions of copies. Software removes errors, groups identical sequences into amplicon sequence variants (ASVs), and matches each to a curated fungal reference database to give it a name.

LEVEL 1

Diversity in two questions

Alpha diversity asks how rich and even one sample is. Beta diversity asks how different two samples are from each other.

LEVEL 1

Which fungi differ?

Comparative analysis checks groups, such as treated versus control soil, and lists the fungi that are consistently more or less abundant.

For scientists planning a project or reading a report: how design choices and quality control decide whether a fungal profile can be trusted.

LEVEL 2

ITS length varies

Unlike 16S, ITS products differ in length between species. Reads should therefore not be cut to one fixed length. Primers are trimmed instead, and reads are filtered on quality and expected errors.

LEVEL 2

Choosing ITS1 or ITS2

Primer sets for each sub-region favour different groups, and no single pair captures every fungus equally. The choice depends on the taxa and sample types you care about.

LEVEL 2

Primer removal comes first

Primers must be clipped before denoising, otherwise leftover primer bases confuse error learning and chimera checks. Reads without primers are usually discarded.

LEVEL 2

Curated fungal databases

Fungal names change often, so assignment relies on curated resources. UNITE groups ITS sequences into species hypotheses, with nearly 10 million reference sequences in the 2024 description.

LEVEL 2

Depth and rarefaction

A rarefaction curve shows whether more sequencing would still reveal new ASVs. Fungal soil communities are rich, so curves often rise for longer than in simple communities.

LEVEL 2

Controls and replicates

Extraction blanks, a fungal mock community and enough biological replicates tell you how much of what you see is signal, contamination or batch effect.

For method developers and reviewers: the biological and statistical caveats that decide whether an apparent result holds up.

LEVEL 3

ASV or OTU?

ASVs resolve exact sequences, but fungal ITS can differ between copies within one genome. A 2022 best-practice review argued OTUs can recover fungal diversity better than ASVs for long markers, so the choice should match the question.

LEVEL 3

Copy-number bias

Across 91 fungi with sequenced genomes, rDNA copy number ranged from about 14 to 1,442. Read counts therefore reflect gene copies, not cell numbers, and should not be read as biomass.

LEVEL 3

Intragenomic variation

In a survey of 2,414 fungal genomes, 641 species carried more than one ITS copy and 419 of those had copies that differed. One organism can then appear as several ASVs.

LEVEL 3

Compositional statistics

Sequencing returns proportions, so a bloom in one fungus makes others look lower. Compositionally aware methods such as ANCOM-BC, ALDEx2 and CLR-based models are designed for this.

LEVEL 3

Primer and length bias

In a gut mycobiome comparison, ITS primers showed amplification bias and longer products above about 600 bp were under-represented. Mock communities reveal such biases.

LEVEL 3

Full-length ITS

The same best-practice review reported that the full-length ITS region places fungi more accurately than ITS2 alone, which is relevant to long-read designs.

ITS
Internal transcribed spacer: the fungal barcode region between the ribosomal genes.
ASV
Amplicon sequence variant: an exact, error-corrected sequence.
OTU
Operational taxonomic unit: a cluster of similar sequences, often at 97% identity.
Species hypothesis
A UNITE group of ITS sequences treated as a putative species.
Phred Q30
A base-call quality score meaning about 1 error in 1,000 bases.
Chimera
An artefactual sequence stitched from two parents during PCR.
Alpha / beta
Diversity within one sample, and dissimilarity between samples.
CLR
Centred log-ratio: a transform for proportion data.
The Science

How a barcode region becomes a fungal profile

These schematics show the region we target, what happens to a sample on its way to reads, and why fungal ITS data need extra care compared with bacterial 16S.

Figure 1 The ribosomal DNA unit and the ITS target schematic
18S (SSU) ITS1 5.8S ITS2 28S (LSU) ITS1 amplicon ITS2 amplicon forward (18S end)reverse (5.8S)forward (5.8S)reverse (28S start) Primers sit in the conserved flanking genes; lengths of ITS1 and ITS2 differ between fungal species
Figure 1 | Conserved anchors, variable barcode. Primers bind the conserved 18S, 5.8S and 28S genes and copy the variable spacer between them. Because ITS length differs between species, products are of unequal size, which affects trimming, merging and sequencing bias. Drawing is not to scale; exact primers are agreed per project.
Figure 2 From sample to sequence infographic
STEP 01

DNA extraction

Tough fungal cell walls are broken open and DNA is purified from soil, plant tissue, swabs or clinical material.

gDNA
blank control
STEP 02

Amplify ITS1 or ITS2

PCR with fungal-targeting primers copies the chosen sub-region from every fungus at once.

ITS amplicon
band size · yield
STEP 03

Index and pool

A second short PCR adds a unique index per sample, so many samples can share one run.

indexed library
library QC
STEP 04

Paired-end sequencing

Both ends of each amplicon are read, giving overlapping reads that are later merged.

FASTQ
Q30 · cluster density
Figure 2 | Typical wet-lab path. Platform, read length and kits are agreed for each project. Projects that supply pre-generated FASTQ files enter the workflow at the data QC step.
Figure 3 One genome, several ITS copies schematic
One fungal genome What the data can show 4 copies of the ITS region ASV approach three ASVs, one organism OTU / cluster approach near-identical copies merged Both views are valid; the right one depends on the study question
Figure 3 | Why ASV counts need care in fungi. Some fungal genomes hold ITS copies that differ slightly. Exact-sequence methods can then report one organism as several ASVs, while clustering merges them but may also merge close species. See Figure 17 for survey data (Bradshaw et al., 2023).
Figure 4 Why one sample is never the whole story schematic
1 gram of soil or tissuekilometres of hyphae · many species
14–1,442rDNA copies per fungal genome
~10 MITS reference sequences in UNITE
~2.4 Mspecies hypotheses in UNITE
Figure 4 | Sampling a crowded community. Each green mark stands for one sequence drawn from a much larger pool. Copy-number range from Lofgren et al. (2019, 91 fungal taxa); UNITE figures from Abarenkov et al. (2024). Replicates, adequate depth and negative controls keep a small sample of reads from being over-interpreted.
Analysis Workflow

From raw reads to community profiles

Each sample passes through a quality-controlled analysis workflow designed to move from raw amplicon sequencing data to annotated fungal community profiles.

Figure 5 Quality-gated ITS workflow infographic
STAGE 01

Raw data QC

Per-base quality, adapter and primer content are reviewed for every sample.

FASTQQC report
Q30 · read counts
STAGE 02

Primer trimming & filtering

ITS1 or ITS2 primers are clipped, then reads are filtered on quality and expected errors without forcing one fixed length.

filtered FASTQ
reads passing filter
STAGE 03

Denoising

The run's error model is learned and exact sequence variants (ASVs) are inferred per sample.

denoised ASVs
error-model fit
STAGE 04

Chimera removal

PCR chimeras are detected and removed before classification.

ASV table
chimera rate
STAGE 05

Taxonomic assignment

ASVs are classified against curated fungal ITS reference databases.

taxonomy table
classified to genus
STAGE 06

Diversity analysis

Alpha metrics, beta-diversity distances and ordination to describe richness and composition.

metricsordinations
depth · rarefaction
STAGE 07

Comparative analysis

Community composition and abundance compared across samples or groups, with FDR control.

group tables
FDR-adjusted
STAGE 08

Reporting & delivery

Taxonomic profiles, diversity statistics and QC summaries shared through secure storage.

reportshared drive
access control
Figure 5 | Decision points. Amber gates mark where metrics are reviewed before samples move on. Samples with unusually low read counts or filter pass rates are flagged to you rather than silently carried forward.
01

Data QC & Pre-processing

Sequencing quality assessed; primer trimming and quality filtering of ITS reads.

02

Denoising / ASV Generation

Quality-filtered reads denoised into amplicon sequence variants.

03

Chimera Removal

Chimeric sequences identified and removed for accurate classification.

04

Taxonomic Assignment

ASVs classified against curated fungal ITS reference databases.

05

Diversity Analysis — Alpha- and beta-diversity metrics to characterise community richness and composition.

06

Comparative Analysis — Community composition and abundance compared across samples or groups.

07

Reporting & Delivery — Taxonomic profiles, diversity statistics and QC summaries delivered securely.

Quality Control

Trust the data before you trust the biology

Every downstream result depends on clean reads. These interactive plots use simulated data to show what good and poor quality look like, and why denoising matters.

Figure 6 Quality profile of paired reads simulated
Figure 6 | Read 2 degrades first. Mean Phred quality along 2 × 300 bp reads. Truncation points are chosen where quality drops (dashed lines), keeping enough overlap to merge the pair into the full amplicon.
Figure 7 Learned sequencing error model simulated
Figure 7 | DADA2 learns errors from your own run. Points show observed error frequency at each quality score and the line shows the fitted model. A good fit supports confident separation of real variants from noise (Callahan et al., 2016).
Figure 8 ITS products differ in length simulated
Figure 8 | Why reads are not cut to one fixed length. Simulated amplicon-length distribution for a mixed fungal community. Different groups give products of different sizes, and the longest may exceed what paired reads can merge (orange zone, illustrative for 2 × 300 bp reads). Primer-based trimming keeps this natural variation instead of discarding it, and long products are also prone to under-representation during sequencing (Frau et al., 2019).
Figure 9 Read tracking through the pipeline simulated
Input readsafter demultiplexing
100%
Filtered & trimmedexpected-error filter
92%
DenoisedDADA2 error correction
90.5%
Merged pairsfull ITS amplicon
85.3%
Non-chimericfinal ASV table
80.1%
Figure 9 | Where reads are lost. Illustrative retention for one typical sample. Each step has a purpose: removing low-quality reads, correcting errors, joining pairs and discarding PCR artefacts. Sudden drops at the merge step usually point to over-trimming.
Figure 10 Rarefaction curves simulated
Figure 10 | Is sequencing deep enough? Observed ASVs as reads are subsampled. Curves that plateau indicate most of the community has been captured; curves still rising suggest a rich sample or insufficient depth.
Diversity Analysis

Within samples and between them

Alpha diversity summarises one community. Beta diversity compares communities and is the basis of most group comparisons. All plots below are simulated to illustrate the outputs you receive.

Figure 11 Alpha diversity by group simulated
Figure 11 | Richness and evenness. Shannon index for three simulated groups. Boxes show the median and interquartile range, dots are samples. Groups are compared with non-parametric tests, with multiple-testing correction when several metrics are reported.
Figure 12 Beta diversity ordination (PCoA) simulated
Figure 12 | Community structure. Each point is a sample; closer points have more similar communities. Ellipses show 95% confidence regions per group. PERMANOVA tests whether the group centroids differ and is reported with a dispersion check (Anderson, 2001).
Figure 13 Taxonomic composition simulated
Figure 13 | Relative abundance of top genera. Stacked bars for simulated samples in three groups, with rarer taxa pooled as "Other". Bars show proportions, so they describe the make-up of each community, not absolute cell numbers.
Figure 14 Why relative abundance can mislead interactive · simulated
Figure 14 | The compositional trap. Only taxon A changes in the real community (left). Because sequencing reports proportions, every other taxon seems to fall (right). Compositionally aware methods and, where possible, absolute-abundance anchors such as qPCR or spike-ins help avoid false conclusions (Gloor et al., 2017).
Differential Abundance

Finding the taxa that really differ

Tests ask which fungal taxa are more or less abundant between groups. Effect size and statistical confidence should be read together, with false discovery rate control across all taxa tested.

Figure 15 Volcano plot of differential taxa simulated
Figure 15 | Effect size versus confidence. Each point is a genus. Green and orange points pass both the FDR (q < 0.05) and log2 fold-change thresholds; grey points do not. Labels mark the strongest hits.
Table 1 Choosing a differential abundance method
MethodIdeaGood for
DESeq2Negative-binomial model on countsSensitive; widely used; watch composition effects
ANCOM-BCBias-corrected log-ratio modelCompositional data; good false-positive control
ALDEx2CLR on Monte Carlo drawsSmall samples; conservative
LEfSeRank tests plus LDA effect sizeExploratory biomarker discovery
No method wins everywhere. A 2022 benchmark in Microbiome found that compositional methods improved false-positive control but often lost power, and none was robust, powerful and flexible at once. Agreement across methods is the strongest signal.
Deliverables

Structured outputs for community biology

At project completion, clients receive a structured set of sequencing, analysis and reporting outputs via secure shared storage.

Raw & Processed Data

FASTQ files — raw and processed amplicon sequence data.

QC Reports

Sample-level sequencing and preprocessing QC summaries.

ASV Tables

Amplicon sequence variant count tables per sample.

Taxonomic Tables

Taxonomy assigned to each ASV at available ranks.

Diversity Summaries

Alpha- and beta-diversity metrics, ordination plots and group comparisons.

Summary Report

Sample-wise QC, ASV and taxonomic-assignment statistics.

Secure Delivery

Project files via secure shared storage or cloud access.

Comparative Outputs

Group-wise abundance and composition comparisons when designed.

Sample Requirements

What we accept

Flexible input options for sequencing-plus-analysis or analysis-only projects.

DNA from Diverse Matrices

We accept genomic DNA extracted from soil, plant tissue, environmental swabs, clinical samples or other relevant matrices, as well as pre-generated amplicon sequencing data where analysis-only services are required.

Project-specific requirements for DNA quantity, concentration, purity, primer selection and sequencing depth are finalised according to the study design.

Contact us for a project-specific sample submission checklist and recommended sequencing depth.

Soil · Plant · Environmental · Clinical
or pre-generated FASTQ

Case Studies & Performance

Proven on real fungal communities

Production metrics from soil fungal profiling and comparative community studies.

Soil Fungal Community Profiling

Multi-sample · ASV generation · Taxonomic classification

Mean raw-data Q30 93.4%
Mean processed-data Q30 97.2%
Mean reads passing filter ~88%
Mean ASVs per sample ~410
Mean genera identified ~95

The study achieved high sequencing quality and consistent ASV recovery, with taxonomic profiles and diversity statistics delivered alongside a project summary report.

Comparative Fungal Community Analysis

Treatment groups · Replicate cohorts

ASV recovery Consistent
Diversity metrics Reproducible
Technical replicates Supported
Group-wise comparisons Enabled
Differential abundance Available

In production studies, we have observed consistent ASV recovery and reproducible diversity metrics across technical replicates, supporting robust group-wise comparisons.

The Frontier

What ITS sequencing can tell you, and where the field is heading

ITS amplicon sequencing is the workhorse of fungal ecology and mycobiome research. Recent studies show both its power and the biological quirks that need careful handling.

Figure 16 Ribosomal DNA copy number across fungi published data
Minimum
14
Median
82
Mean
113
Maximum
1,442
More than 100-fold spread. A fungus with many rDNA copies yields more ITS reads than one with few, even at equal cell numbers, so read counts are not biomass.
Figure 16 | Copies per genome. In silico estimates for 91 fungal taxa with sequenced genomes (Lofgren et al., Molecular Ecology, 2019). Copy number tracked phylogeny but not trophic mode, guild or genome size.
Figure 17 How often does ITS vary within one genome? published data
1,773 single copy 419
Single ITS copy Multi-copy, identical (222) Multi-copy, differing (419)
641 of 2,414 species carried more than one ITS copy, and for 419 of these the copies differed in sequence. The authors note this could inflate diversity estimates and even cause mis-described species.
Figure 17 | Intragenomic ITS variation. Survey of 2,414 fungal genomes (Bradshaw et al., iScience, 2023), who also noted that many genomes, especially from unculturable fungi, were contaminated. Counts of single-copy and identical multi-copy species are derived from the reported totals.
Table 2 Choosing a fungal marker and read strategy
ApproachStrengthsWatch-outs
ITS1 (short reads)Established primer sets; good species-level signal for many groupsPrimer bias against some lineages; variable product length
ITS2 (short reads)Widely used; broad reference coverageCan amplify plant DNA in host samples; less accurate placement than full-length ITS in one review
Full-length ITS (long reads)More accurate taxonomic placement in a 2022 reanalysisHigher cost per read; fewer reads per run
18S / 28S markersFewer length and primer-mismatch problems in a gut mock communityLess species-level resolution; smaller reference sets for fungi
Table 2 | Marker trade-offs. Summarised from Tedersoo et al. (2022) and Frau et al. (2019). The right choice depends on the taxa, the sample matrix and the resolution the question needs.
REFERENCE

UNITE species hypotheses

The 2024 UNITE description reports nearly 10 million reference ITS sequences grouped into about 2.4 million species hypotheses, each with a DOI so studies can cite them unambiguously (Abarenkov et al., 2024).

BEST PRACTICE

ASV or OTU

A 2022 review of fungal metabarcoding reported that OTUs recovered fungal diversity better than ASVs for long markers, while ASVs give exact, reusable sequences. Reporting both views is a practical safeguard.

STATISTICS

Compositional methods

The same review found that methods designed for compositional data gave more reliable estimates of shifts in community structure than methods that ignore composition.

BIAS

Extraction and primers

A gut mycobiome study found that DNA extraction kits favoured some species, and that ITS primers showed amplification bias and under-represented long products (Frau et al., 2019).

CONTAMINATION

Controls matter

Reagent and environmental contaminants matter most when little DNA is present. Extraction blanks and statistical decontamination help flag them (Davis et al., 2018).

STANDARDS

Mock communities

A defined mixture of fungal species run alongside samples shows how accurately a workflow recovers composition and makes batches comparable.

Scope note. Nucleome's ITS service covers the workflow and deliverables described on this page, from data QC through ASV generation, taxonomy, diversity and comparative analysis. The frontier methods shown here are provided to place the service in its scientific context; contact our team to discuss extended or project-specific analyses.
Why Nucleome

Service highlights

End-to-end support for research, environmental, agricultural and clinical ITS projects.

End-to-End ITS Analysis

From raw amplicon sequencing data through QC, ASV generation, taxonomic classification and reporting.

ASV-Based Resolution

Denoising-based approach for improved taxonomic accuracy over traditional clustering methods.

Curated Fungal Databases

Reliable taxonomic assignment using established, curated ITS reference databases.

Transparent QC

Sample-wise and cohort-level quality metrics incorporated into project reporting.

Flexible Study Design

Support for individual samples, cohorts and comparative community studies.

Secure Data Delivery

Raw, intermediate and final datasets delivered through secure project storage.

Questions

Frequently asked, in plain language

From the basics to the finer points of study design and statistics.

basicWhat is ITS amplicon sequencing?
It reads the internal transcribed spacer, a short stretch of DNA that acts as a barcode for fungi, to find out which fungi are in a sample and in what proportions. It does not read whole genomes.
basicWhich samples can I send?
DNA from soil, plant tissue, environmental swabs, clinical samples and other matrices. You can also send existing amplicon FASTQ files for analysis only.
basicWhat will I receive?
FASTQ files, QC reports, ASV tables, taxonomic tables, diversity summaries, comparative outputs and a summary report, delivered through secure shared storage.
intermediateShould I choose ITS1 or ITS2?
Both are standard. Primer sets for each favour different fungal groups, so the choice depends on which taxa and sample types matter most. Our team will discuss the best fit for your study objectives.
intermediateWhy are ITS reads not trimmed to one length?
Fungal ITS products vary in length between species. A fixed cut-off would discard long products or distort the community, so primers are removed and reads are filtered on quality instead.
intermediateWhich database is used for naming fungi?
Assignment uses established, curated fungal ITS databases. A widely used resource is UNITE, which groups ITS sequences into species hypotheses. Names change as taxonomy is revised, so keep the database version consistent within a study.
advancedDo ASV counts equal the number of fungal species?
Not exactly. Some fungal genomes carry ITS copies that differ, so one organism can give several ASVs, and different species can share an ASV. A survey of 2,414 genomes found differing copies in 419 species. Report ASV-level and taxon-level results together.
advancedDoes read abundance reflect how much of each fungus is present?
Only roughly. rDNA copy number varied from about 14 to 1,442 across 91 fungal taxa, and primers and extraction add further bias, so read counts are best used for comparing the same taxon between samples.
advancedWhy might different statistical tests disagree?
They make different assumptions about zeros and compositional data. A benchmark of microbiome tools found no single method best in every setting, so confirming key taxa with more than one method is a practical safeguard.
References

Key literature & resources

Selected sources behind the methods and reference values on this page. Figures labelled "simulated", "schematic" or "illustrative" are generated for this page, not client data.

  1. Schoch CL, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. PNAS 2012;109:6241–6246.
  2. Tedersoo L, Bahram M, Zinger L, Nilsson RH, Kennedy PG, Yang T, Anslan S, Mikryukov V. Best practices in metabarcoding of fungi: from experimental design to results. Molecular Ecology 2022;31:2769–2795. Record
  3. Abarenkov K, et al. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Research 2024;52:D791–D797. Record
  4. Nilsson RH, et al. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nature Reviews Microbiology 2019;17:95–109.
  5. Bradshaw MJ, et al. Extensive intragenomic variation in the internal transcribed spacer region of fungi. iScience 2023;26. Record
  6. Lofgren LA, et al. Genome-based estimates of fungal rDNA copy number variation across phylogenetic scales and ecological lifestyles. Molecular Ecology 2019;28:721–730. Record
  7. Frau A, et al. DNA extraction and amplicon production strategies deeply influence the outcome of gut mycobiome studies. Scientific Reports 2019;9:9328. Link
  8. Callahan BJ, et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods 2016;13:581–583.
  9. Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME Journal 2017;11:2639–2643.
  10. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 2011;17:10–12.
  11. Gardes M, Bruns TD. ITS primers with enhanced specificity for basidiomycetes: application to the identification of mycorrhizae and rusts. Molecular Ecology 1993;2:113–118.
  12. Ihrmark K, et al. New primers to amplify the fungal ITS2 region: evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiology Ecology 2012;82:666–677.
  13. Bengtsson-Palme J, et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods in Ecology and Evolution 2013;4:914–919.
  14. Kõljalg U, et al. Towards a unified paradigm for sequence-based identification of fungi. Molecular Ecology 2013;22:5271–5277.
  15. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013;8:e61217.
  16. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecology 2001;26:32–46.
  17. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014;15:550.
  18. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nature Communications 2020;11:3514.
  19. Gloor GB, et al. Microbiome datasets are compositional: and this is not optional. Frontiers in Microbiology 2017;8:2224.
  20. Yang L, Chen J. A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions. Microbiome 2022;10. Record
  21. Davis NM, et al. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018;6:226.