ITS and Fungal Amplicon Sequencing: Investigating Fungal Communities in Soil, Plant Roots, and Clinical Samples

A field ecologist sampling mycorrhizal roots in a temperate forest, a plant pathologist tracking Fusarium wilt across tomato fields, and a clinical microbiologist profiling the respiratory mycobiome of immunocompromised individuals in a research cohort share a common methodological question: which region of the fungal ribosomal DNA should I sequence, and which primers will capture the community I actually care about? The answer is rarely straightforward — it depends on whether your fungi are inside plant roots, free-living in soil, or colonizing human mucosa.

This article is a practical guide to internal transcribed spacer (ITS) amplicon sequencing for fungal community analysis. We walk through the decisions that determine whether an ITS project yields resolvable species-level taxonomy, which extraction and primer strategies work for each sample matrix, and how to avoid the most common pitfalls that turn fungal community data into uninterpretable lists of unclassified OTUs. For researchers outsourcing fungal community analysis, 16S/18S/ITS Amplicon Sequencing at CD Genomics covers the full ITS workflow from sample QC to taxonomic classification.

Why Study Fungi with ITS?

The fungal kingdom is estimated to contain 2.2 to 3.8 million species, of which fewer than 10% have been formally described. They are central to terrestrial carbon cycling, plant nutrient acquisition, and — increasingly recognized — human health and disease. But identifying fungi by morphology or culture is slow, biased toward sporulating taxa, and blind to unculturable species. DNA-based identification solves this, and the internal transcribed spacer (ITS) of the ribosomal RNA operon is the consensus barcode.

The ITS region sits between the 18S (small subunit) and 28S (large subunit) rRNA genes. It is transcribed but spliced out during rRNA maturation, which means it accumulates mutations at a faster rate than the flanking coding regions — fast enough to discriminate species, but conserved enough within species to serve as a reliable barcode. The ITS region is divided into ITS1 (between 18S and 5.8S) and ITS2 (between 5.8S and 28S), separated by the highly conserved 5.8S gene.

Which subregion performs better? The answer depends on the fungal group. ITS1 provides higher resolution for Basidiomycota — the phylum that includes rusts, smuts, and most mushroom-forming fungi. ITS2 outperforms ITS1 for Ascomycota, the largest fungal phylum encompassing most molds, plant pathogens, and clinically relevant yeasts. For studies targeting both phyla equally, the full ITS region (ITS1 + 5.8S + ITS2, approximately 450-700 bp depending on species) offers the highest taxonomic coverage. The widely used primer pair ITS1F/ITS4 amplifies the full ITS region and remains the most common choice for general fungal community profiling.

The UNITE database is the central reference for fungal ITS taxonomy. Unlike SILVA or Greengenes2, which cover all three domains of life, UNITE is curated specifically for fungi and uses a dynamic species hypothesis (SH) clustering system at 97-99% identity thresholds. For most fungal ITS projects, classification against UNITE v9.0+ with a 97% clustering threshold provides species-level assignments for well-sampled groups and genus-level assignments for under-characterized environmental lineages.

Critically, know what ITS cannot do. ITS does not reliably distinguish strains within a species, does not capture functional gene content, and does not work for non-fungal eukaryotes. If your question requires strain-level tracking — tracing a Fusarium oxysporum forma specialis across fields, or tracking a Candida auris outbreak in a hospital — ITS alone is insufficient. It tells you what species are present and their relative abundances, not what metabolites they are producing or whether they carry antifungal resistance genes.

Figure 1: ITS Region Structure in the Fungal rDNA Operon — ITS1 vs ITS2 Coverage and Taxonomic Resolution

ITS Sequencing Workflow

DNA Extraction: The Fungal Cell Wall Problem

Fungal cell walls are tougher than bacterial cell walls — chitin and glucan polymers resist the enzymatic lysis protocols that work well for Gram-negative bacteria. Spores are even more recalcitrant. Bead-beating with 0.5 mm zirconia/silica beads in a high-speed homogenizer (FastPrep or Precellys) is the minimum for recovering fungal DNA from spores and melanized hyphae. In a head-to-head comparison of extraction methods for soil fungal communities, protocols using mechanical lysis (bead-beating) recovered 40-60% more fungal OTUs than enzymatic-only protocols, and the difference was most pronounced for Ascomycota — the phylum that includes most plant pathogens.

For plant root samples, the co-extraction challenge works in both directions. Plant DNA can dominate the extract (root tissue is >95% plant by mass), diluting fungal signal in downstream sequencing. Surface sterilization with 2% sodium hypochlorite followed by sterile water rinses removes surface-attached fungal spores but preserves endophytic and mycorrhizal fungi inside the root. For rhizosphere soil, the standard approach is to shake roots vigorously in sterile PBS, collect the soil slurry, and extract from the pellet — this enriches for the root-adhering fungal community without overwhelming the sample with plant DNA.

For clinical samples — bronchoalveolar lavage fluid, skin swabs, vaginal swabs — human DNA is the dominant contaminant. Unlike 16S where host DNA is absent (mammals lack 16S genes), ITS primers do not amplify human DNA because humans lack the fungal rDNA operon. This means no host-depletion step is needed for fungal ITS PCR from human clinical samples — a distinct practical advantage over bacterial 16S where host DNA can compete with microbial template in low-biomass samples.

PCR Primers: The Bias You Cannot Avoid

Every ITS primer pair has taxonomic blind spots. The widely used ITS1F (CTTGGTCATTTAGAGGAAGTAA) paired with ITS2 (GCTGCGTTCTTCATCGATGC) amplifies ITS1 only. This pair captures most Ascomycota and Basidiomycota but under-amplifies early-diverging fungal lineages (Chytridiomycota, Mucoromycota), which includes arbuscular mycorrhizal fungi in the Glomeromycota — arguably the most ecologically significant fungal group for plant biology.

For studies centered on arbuscular mycorrhizal fungi (AMF), the dedicated primer pair AML1/AML2 targeting the 18S region or specific AMF ITS primers (e.g., NS31/AML2) are superior to general fungal ITS primers. AMF-specific primers capture Glomeromycota that general ITS primers miss entirely. The trade-off: AMF-specific primers are blind to non-AMF fungi, so you need a two-amplicon strategy (general ITS + AMF-specific) for comprehensive fungal community profiling of roots.

For the ITS2 region specifically, the primer pair ITS3/ITS4 (or the modified ITS3N/ITS4N) targets a region of ~250-350 bp and has the advantage of lower length variation across fungal lineages compared to ITS1, which simplifies bioinformatic processing and reduces PCR length bias. ITS2-based classification using the UNITE database achieves species-level assignments for 80-90% of fungal genera commonly found in soil and plant-associated environments.

PCR conditions matter. Fungal ITS amplification typically uses 25-35 cycles. Above 30 cycles, the risk of chimera formation rises substantially. Annealing temperature is critical: 52-55°C provides the best balance of yield and specificity for ITS1F/ITS4; higher annealing temperatures reduce yield of underrepresented taxa. Always include a no-template control and at least one positive control (a mock fungal community with known composition) in each PCR plate.

Bioinformatics: The UNITE-QIIME 2 Pipeline

Fungal ITS bioinformatics differs from 16S in two important respects. First, ITS amplicons are length-variable: ITS1 from one fungal species may be 200 bp while ITS1 from another is 400 bp. This means standard paired-end merging algorithms that assume uniform amplicon length need adjustment. DADA2's ITS-specific workflow handles this by trimming primer sequences, filtering based on quality scores, and then performing denoising and merging with relaxed length constraints.

Second, taxonomy assignment against UNITE uses a curated fungal-specific classifier. The QIIME 2-compatible UNITE classifier (available as pre-trained .qza files from the UNITE website) assigns taxonomy using the dynamic species hypothesis (SH) system. For most projects, the "UNITE dynamic" classifier trained on 97% OTUs provides the best balance of resolution and accuracy. The output includes SH identifiers (e.g., SH1234567.08FU) that link your sequences to the UNITE reference system, enabling direct comparison across studies.

ASV resolution — producing amplicon sequence variants rather than OTU clusters — works for fungal ITS data but requires caution. The ITS region contains intragenomic variants within a single fungal genome (multiple rDNA copies with slightly different ITS sequences; both haploid nuclei in dikaryotic fungi may carry different ITS alleles), and DADA2 can split these into separate ASVs. Post-clustering ASVs at 97% identity recovers the species-level resolution that fungal ecologists expect without the inflated diversity of raw ASV counts.

Figure 2: End-to-End ITS Amplicon Sequencing Workflow — From Sample Collection to Taxonomic Profile

Soil and Root-Associated Fungal Communities

Mycorrhizal Networks: The Underground Economy

Arbuscular mycorrhizal fungi (AMF, phylum Glomeromycota) form symbioses with approximately 80% of terrestrial plant species. They extend the plant root system by orders of magnitude, trading soil phosphorus and nitrogen for plant photosynthate. Ectomycorrhizal fungi (ECM, primarily Basidiomycota and Ascomycota) associate with most temperate and boreal tree species. Both groups are ecologically central and methodologically challenging to study — AMF because they are not captured by general ITS primers, and ECM because the fungal tissue is intermixed with root cells.

For AMF-focused studies, the strategy is straightforward: use AMF-specific 18S primers (AML1/AML2 or NS31/AML2) for the mycorrhizal component and general ITS for the broader fungal community. For ECM studies, general ITS primers work well on ectomycorrhizal root tips that have been individually dissected and pooled, but bulk root samples will be dominated by saprotrophic and endophytic fungi.

Soil fungal communities exhibit extreme spatial heterogeneity at the centimeter scale. A single gram of forest soil may contain DNA from over 1,000 fungal species spanning saprotrophs, pathogens, and mutualists. For soil fungal community studies, composite sampling is essential: collect 5-10 cores from each plot, homogenize, and extract from a representative subsample. For rhizosphere soil specifically, collect the soil that remains attached to roots after gentle shaking; this is the rhizosphere compartment, distinct from bulk soil meters away.

Agricultural Soil Health and Biocontrol

Soil fungal communities are increasingly used as indicators of agricultural soil health. Saprotrophic fungi — which decompose crop residue and cycle nutrients — tend to increase under no-till management and cover cropping. Pathogenic fungi tend to increase under continuous monoculture. The ratio of saprotroph to pathotroph functional guilds, inferred from ITS data using FUNGuild or FungalTraits, provides a quantitative index of soil health that complements chemical measurements.

Trichoderma species are among the most widely studied biocontrol fungi. They parasitize fungal pathogens, produce antimicrobial secondary metabolites, and induce plant systemic resistance. ITS sequencing of bulk soil or rhizosphere samples detects Trichoderma at the genus level, but species-level resolution requires Full-Length 16S/18S/ITS Amplicon Sequencing or additional markers (tef1, rpb2) because the ITS1 region alone does not resolve the Trichoderma harzianum species complex. For isolate-level identification, Microbial Identification services integrate ITS with complementary genetic markers.

Endophytes: The Fungal Community Inside Leaves and Stems

Foliar endophytes — fungi that live asymptomatically inside plant leaves and stems — are among the most diverse and least understood components of the plant microbiome. They influence drought tolerance, herbivore resistance, and pathogen susceptibility. ITS amplicon sequencing has been the primary tool for cataloging endophyte diversity because most endophytic fungi cannot be cultured on standard media.

The key challenge with endophyte ITS profiling is surface decontamination. Leaf surfaces harbor abundant epiphytic fungi (yeasts, mold spores) that are distinct from the interior endophyte community. Surface sterilization with 2% sodium hypochlorite (30-60 seconds), 70% ethanol (2 minutes), and three sterile water rinses is standard, but the treatment time must be calibrated per plant species — too short and epiphytes persist, too long and the sterilant penetrates the leaf, killing endophytes. The simplest validation: plate the final rinse water on PDA agar and check for fungal growth after 5 days.

Plant Pathogen Surveillance with ITS

Fungal pathogens cause an estimated 10-23% crop loss globally, and climate change is expanding the geographic range of many species. ITS amplicon sequencing offers two advantages for pathogen surveillance over traditional methods: it detects pathogens before symptoms appear, and it captures the full fungal community context in which a pathogen operates.

Key Pathogens Detectable by ITS

Fusarium species (Ascomycota: Hypocreales) cause wilts, root rots, and head blights across cereal, vegetable, and ornamental crops. ITS resolves the Fusarium genus cleanly but species-level discrimination within the F. oxysporum and F. solani species complexes requires the translation elongation factor 1-alpha (tef1) gene. For surveys where knowing "Fusarium is present at X% relative abundance" is sufficient, ITS works well. For studies requiring which Fusarium species is driving disease, pair ITS with a Fusarium-specific protein-coding marker.

Verticillium dahliae and V. albo-atrum cause wilt diseases in over 200 plant species. ITS distinguishes between the two species reliably, making ITS profiling of soil a practical pre-planting risk assessment tool. A soil sample with >1% Verticillium relative abundance by ITS strongly predicts wilt development in susceptible crops planted the following season.

Puccinia (rust fungi, Basidiomycota) and other obligate biotrophs cannot be cultured on artificial media — ITS sequencing directly from infected leaf tissue is the most practical identification method. The rust fungi have unusually large ITS regions (ITS1 can exceed 500 bp), so primer pairs targeting ITS2 only (ITS3/ITS4) produce more consistent amplification across rust species than full-ITS primer pairs.

Disease-Suppressive Soils

Some agricultural soils naturally suppress fungal pathogens — a phenomenon driven by antagonistic members of the soil microbiome. ITS sequencing of suppressive vs. conducive soils has identified consistent enrichment of specific fungal genera (Chaetomium, Clonostachys, and specific Trichoderma clades) in suppressive soils. For growers and agronomists, ITS profiling of field soil can identify fields with naturally suppressive fungal communities that may require less fungicide input — a precision agriculture application that is moving from research into commercial practice.

Figure 3: Soil and Root Fungal Communities — Mycorrhizal Networks and Pathogen Interactions

The Human Mycobiome in Health and Disease

The human body harbors fungal communities (the mycobiome) at every mucosal surface — oral cavity, respiratory tract, gastrointestinal tract, vaginal tract, and skin. The mycobiome is far lower in biomass than the bacteriome (fungi account for <0.1% of total microbial genes in stool), and this low abundance creates unique technical challenges for ITS-based profiling.

Gut Mycobiome

In the healthy human gut, Candida, Saccharomyces, and Malassezia are the dominant fungal genera detected by ITS sequencing, but the inter-individual variation is far higher than for gut bacteria — a person's gut fungal profile is closer to a fingerprint than a conserved community type. Longitudinal studies show that gut fungal communities fluctuate more dynamically than bacterial communities over days to weeks, likely reflecting dietary fungal intake (cheese rinds, bread, fermented foods) and transient passage of environmental fungi.

For gut mycobiome studies, stool DNA extraction protocols optimized for bacteria (e.g., DNeasy PowerSoil Pro) recover fungal DNA adequately, but the low fungal-to-bacterial DNA ratio means that fungal ITS amplicon sequencing requires higher PCR cycle numbers (35-40 cycles) to generate sufficient library yield. The consequence is increased chimera formation and PCR drift — biological replicates and negative controls are more important for fungal ITS than for bacterial 16S from the same stool sample.

Respiratory Mycobiome

The lower respiratory tract was long considered sterile, but ITS sequencing of bronchoalveolar lavage (BAL) fluid has revealed a low-biomass fungal community dominated by environmental fungi (Cladosporium, Penicillium, Aspergillus) in healthy individuals. In patients with cystic fibrosis, asthma, or COPD, the respiratory mycobiome shifts — Aspergillus fumigatus and Candida albicans are the most common fungal colonizers, and their presence is associated with accelerated lung function decline in CF.

The dominant technical challenge for respiratory mycobiome studies is controlling for airway and reagent contamination. The fungal biomass in BAL fluid is on the order of picograms, making extraction blanks and PCR no-template controls indispensable. In a well-controlled study, fungal taxa that appear in negative controls (typically Cladosporium, Alternaria, and Penicillium — common airborne fungi) must be subtracted or excluded from analysis. Without this step, the reported respiratory mycobiome is predominantly a catalog of lab air and kit contaminants.

Oral and Skin Mycobiome

Oral ITS profiling consistently identifies Candida as the dominant fungal genus in the oral cavity, but Malassezia, Aspergillus, and Fusarium also appear at lower abundance. Oral yeast carriage increases with denture use, xerostomia (dry mouth), and immunosuppression. Oral rinse or tongue swab samples work well for ITS profiling — no specialized collection devices are needed, and the high fungal load in oral samples means 25-30 PCR cycles are sufficient.

Malassezia dominates the skin mycobiome, particularly on the face, scalp, and upper torso — regions rich in sebaceous glands. Malassezia species are lipid-dependent, and their abundance correlates with sebum production. ITS1 is sufficient for Malassezia species-level identification for the common species (M. restricta, M. globosa, M. sympodialis), but rarer species in the genus benefit from ITS2 sequencing or full-length ITS.

Figure 4: Human Mycobiome — Key Body Sites and Dominant Fungal Taxa by ITS Profiling

Choosing Your ITS Strategy

ITS1 vs. ITS2: Final Recommendation

Choose ITS1 (primer pair ITS1F/ITS2) if your study targets Basidiomycota (rusts, smuts, ECM fungi, wood-decay fungi), if you need maximum compatibility with the largest number of published ITS datasets, or if your samples are soil or plant-associated where Basidiomycota diversity is high.

Choose ITS2 (primer pair ITS3/ITS4 or ITS3N/ITS4N) if your study targets Ascomycota (molds, plant pathogens, yeasts), if you are working with the human mycobiome where most clinically relevant fungi are Ascomycota, or if you value the lower amplicon length variation that simplifies bioinformatic processing.

Choose full ITS (ITS1F/ITS4) if you need maximum taxonomic breadth across all fungal phyla and your sequencing platform supports amplicons up to 700 bp (MiSeq v3 2×300 bp with overlap; PacBio; Nanopore).

Figure 5: ITS1 vs ITS2 Decision Framework — Choosing the Right Amplicon Strategy

Sequencing Depth and Biological Replicates

Fungal ITS libraries from most sample types reach saturation at 50,000-100,000 reads per sample — similar to bacterial 16S. Soil samples, which contain the most diverse fungal communities, may require 100,000-150,000 reads for rarefaction curve saturation.

The biological replicate requirements mirror those for 16S: 5-8 per group for controlled-environment plant studies (with pot or plot as the experimental unit), 20-30 per group for human cross-sectional mycobiome studies, and 8-12 per treatment for field-scale agricultural studies. Technical replicates generally add negligible information for ITS, as they do for 16S.

Cost Factors and Budget Planning

The per-sample cost of ITS amplicon sequencing has dropped substantially, but the all-in cost extends beyond the sequencing itself. When budgeting for an ITS project, the major cost components are DNA extraction (higher for soil and plant samples requiring additional cleanup), PCR amplification and library preparation (one amplicon for ITS-only, two amplicons for dual ITS + 16S), sequencing depth (50K-150K reads per sample depending on sample type), and bioinformatics analysis (standard UNITE taxonomic classification vs. custom functional predictions). As a rough guide for grant budgeting: a 50-sample ITS-only project with standard soil DNA extraction through basic bioinformatics typically falls in the range of $50-100 per sample at most CROs, while a dual ITS + 16S project adds 30-50% to the per-sample cost.

Note: Exact pricing depends on sample type, DNA quality, and analysis tier. Always request an all-inclusive quote covering extraction through bioinformatics, and confirm that samples are randomized across sequencing plates to avoid batch effects.

Integrating ITS with 16S and Other Approaches

A growing number of studies sequence both ITS and 16S from the same samples — fungal and bacterial community profiles from the same DNA extract, amplified with separate primer sets. This dual-marker approach captures the full microbial community and reveals fungal-bacterial interactions that neither marker alone can detect. When planning a dual ITS + 16S study, extract enough DNA to split into two aliquots for separate PCR amplification, rather than trying to multiplex both primer sets in a single reaction.

A dual ITS + 16S project with 50 samples typically costs 30-50% more than a single-marker project of the same size — the additional cost comes from the second PCR amplification, second library preparation, and the additional sequencing depth needed to achieve adequate coverage for both amplicons. However, the combined per-sample cost for both markers remains well below a shallow shotgun metagenome (5M reads per sample), making dual-marker amplicon sequencing the most economical approach for comprehensive bacterial-fungal community profiling. When requesting quotes from a CRO, confirm that both amplicons can be sequenced on the same flow cell to maximize cost efficiency, and clarify whether bioinformatics analysis for both markers (UNITE for ITS, SILVA for 16S) is included in the standard analysis package or billed separately.

If your question demands functional annotation — what are these fungi doing? — ITS taxonomy alone will not answer it. Metagenomic Shotgun Sequencing captures fungal genomes alongside bacterial genomes, providing direct functional gene annotation including carbohydrate-active enzyme (CAZy) profiles and secondary metabolite biosynthetic gene clusters. For plant-fungal interaction studies where the host transcriptome is also of interest, RNA-Seq captures both plant gene expression and fungal gene expression from infected tissue simultaneously. If you need species-level resolution beyond what standard ITS provides — for resolving species complexes in Fusarium, Trichoderma, or Candida — Full-Length 16S/18S/ITS Amplicon Sequencing using PacBio or Nanopore platforms closes the resolution gap.

For a broader perspective on how ITS fits into the multi-marker amplicon sequencing landscape — including 16S for bacteria, 18S for protists, and DNA barcoding for species identification — see the article Amplicon Sequencing Services for Microbiome and Biodiversity Research: 16S, 18S, ITS, and DNA Barcoding Solutions. For species-level identification of individual fungal isolates (rather than community profiling), DNA Barcoding Services for Species Identification: COI, rbcL, matK, and Beyond provides complementary approaches.

CD Genomics' Amplicon Sequencing Services support both single-marker (ITS or 16S) and dual-marker projects, with standardized UNITE + SILVA bioinformatics pipelines and customized analysis options for fungal community studies. For researchers who need absolute fungal abundance rather than relative proportions, Absolute Quantitative 16S/18S/ITS Amplicon Sequencing adds spike-in standards to convert ITS read counts to absolute fungal cell numbers per sample.

FAQ

Q: Which is better for fungal community profiling — ITS1 or ITS2?

ITS1 provides better resolution for Basidiomycota; ITS2 for Ascomycota. For balanced coverage across the fungal kingdom, full ITS (ITS1F/ITS4, ~450-700 bp) is the best choice. If technical constraints limit amplicon length, ITS2 (~250-350 bp) offers lower length variation and more uniform amplification across fungal lineages.

Q: Can general ITS primers detect arbuscular mycorrhizal fungi (AMF)?

No, not reliably. The widely used ITS1F primer has mismatches against Glomeromycota. For AMF-focused studies, use AMF-specific 18S primers (AML1/AML2 or NS31/AML2) in parallel with general ITS primers for the broader fungal community.

Q: How do I prevent plant DNA from dominating my root fungal ITS libraries?

Surface-sterilize roots with 2% sodium hypochlorite (30-60 seconds), rinse with sterile water, and extract DNA directly from the root tissue. ITS primers do not amplify plant DNA (plants lack the fungal rDNA operon), but co-extracted plant compounds (polyphenols, polysaccharides) can inhibit PCR. Post-extraction cleanup with PVP or SPRI beads improves amplification from plant-derived extracts.

Q: What negative controls are essential for fungal ITS sequencing?

The same three controls as for 16S: extraction blank (no-sample control processed through the entire extraction workflow), PCR blank (molecular-grade water substituted for DNA template), and field blank (a sterile swab or collection device exposed to the sampling environment). Fungal ITS projects are especially sensitive to airborne spore contamination — Cladosporium, Penicillium, and Alternaria commonly appear in negative controls.

Q: How many reads per sample do I need for ITS?

50,000-100,000 for most sample types. Soil samples at the high end (100,000-150,000). Human mycobiome samples (stool, skin, oral) at the low end (30,000-50,000) due to lower fungal diversity.

Q: Can I use the same DNA extract for ITS and 16S sequencing?

Yes. DNA extracted with bead-beating protocols (e.g., DNeasy PowerSoil Pro) recovers both bacterial and fungal DNA. Split the extract into two aliquots for separate ITS and 16S PCR amplification. Do not multiplex both primer sets in a single PCR reaction — the primers will interfere with each other.

Q: How reliable is ITS for species-level identification of clinical fungi?

Reliable for common clinical fungi (Candida albicans, C. glabrata, Aspergillus fumigatus, A. niger, Cryptococcus neoformans) when using UNITE as the reference database. For less common clinical fungi or within-species complexes (e.g., Candida parapsilosis complex), ITS may only resolve to genus or species-complex level.

Q: What bioinformatics pipeline should I use for fungal ITS data?

DADA2 with the ITS-specific workflow (trim primers, filter, denoise, merge with relaxed length constraints), followed by taxonomy assignment against the UNITE dynamic classifier (QIIME 2-compatible .qza format). Post-cluster ASVs at 97% identity to collapse intragenomic variants into species-level units.

References:

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For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.