Amplicon Sequencing Services for Microbiome and Biodiversity Research: 16S, 18S, ITS, and DNA Barcoding Solutions

In 2025 alone, CD Genomics received over 50 inquiries from researchers around the world asking about amplicon-based microbial sequencing services. The questions came from a Peruvian veterinarian studying feline subgingival plaque microbiota — the first such study in Peru — from a Spanish clinical trial investigating how daily peanut consumption reshapes the gut microbiome across 188 fecal samples, from an environmental microbiologist in the United States trying to characterize biofouling communities in two groundwater wells, and from a Canadian undergraduate student planning a summer soil microbiome project on a shoestring budget.

These inquiries span vastly different organisms, sample types, and budgets. Yet every single researcher chose amplicon sequencing as their primary or exclusive method — not shotgun metagenomics, not long-read sequencing, not metatranscriptomics. Targeted amplification of phylogenetic marker genes — 16S for bacteria and archaea, ITS for fungi, 18S for eukaryotic microbes, and COI/rbcL/matK for species identification — remains the approach that answers the majority of microbiome and biodiversity questions with the best balance of cost, throughput, interpretability, and database support.

Amplicon sequencing has been the foundation of microbial ecology for over two decades because it works in conditions that defeat other methods. It works on degraded DNA from formalin-fixed, paraffin-embedded (FFPE) clinical samples where DNA fragments are under 200 base pairs. It works on low-biomass environmental samples where the total microbial DNA is measured in picograms rather than nanograms. It works on hundreds of samples in a single sequencing run, enabling the biological replication that microbiome studies critically require. And it works within the budget constraints of academic labs, where a single shotgun metagenomics sample can cost as much as preparing and sequencing ten to twenty amplicon libraries.

This guide provides a practical decision framework for researchers who need to choose between the four main amplicon-based approaches — 16S rRNA sequencing for prokaryotic communities, ITS sequencing for fungal communities, 18S rRNA sequencing for eukaryotic microplankton, and DNA barcoding for macro-organism species identification. For each method, we cover when to use it, how to optimize experimental parameters, what depth and sequencing strategy to choose, what data to expect, and how to avoid common pitfalls that waste samples and budget. The recommendations that follow are grounded in what we have actually seen work — and fail — in real research projects submitted to our sequencing facility.

Before You Choose: The Four Amplicon Approaches at a Glance

All four amplicon methods covered in this guide — 16S rRNA sequencing, ITS sequencing, 18S rRNA sequencing, and DNA barcoding — share a common technical foundation: PCR amplification of a conserved marker gene from extracted DNA, followed by high-throughput sequencing of the amplicon pool, and bioinformatic assignment of the resulting sequences to taxonomic groups using curated reference databases. The differences between the methods lie in which organisms they target, what taxonomic resolution they provide, and what research questions they can answer. The sections that follow examine each method in detail, beginning with the most widely used: 16S rRNA sequencing for prokaryotic community profiling.

Figure 1: Decision-Tree Infographic — Choosing Your Amplicon Marker. Four branching paths from sample type and research question to recommended marker (16S, ITS, 18S, COI/rbcL), color-coded by method.

16S rRNA Sequencing — The Gold Standard for Prokaryotic Community Profiling

The Architecture of the 16S rRNA Gene and Why It Matters

The 16S ribosomal RNA gene is approximately 1,500 base pairs long and contains a well-characterized alternation of conserved and hypervariable regions. The nine hypervariable regions — V1 through V9 — are flanked by stretches of highly conserved sequence that serve as binding sites for universal PCR primers targeting both bacteria and archaea. This structural design allows amplification of essentially any prokaryote with a single primer pair, while the variable regions encode enough phylogenetic signal to assign taxonomy from phylum down to genus, and frequently to species level with full-length sequencing.

The key decision in 16S amplicon design is which hypervariable region or combination of regions to target. This choice has profound consequences for the taxonomic resolution you achieve, the amplification biases you introduce, and the comparability of your data to published studies.

V3–V4: The Default Standard for Most Applications

The V3–V4 region is the most widely used target in human and animal microbiome research. Both the Human Microbiome Project (HMP) and the Earth Microbiome Project (EMP) standardized on V3–V4, generating the largest body of publicly available 16S data for cross-study comparison. The standard primers (341F and 805R) produce ~460 bp amplicons, well within Illumina paired-end 300 bp chemistry for full overlap and high-confidence merging. V3–V4 provides excellent coverage of bacteria and archaea — capturing Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, and Verrucomicrobia with minimal bias — and performs consistently across fecal, soil, water, sediment, and clinical sample types. Taxonomic resolution is typically at the genus level, with species-level assignment possible for well-characterized taxa within robust reference databases.

V4–V5: Better Resolution for Certain Environmental Taxa

The V4–V5 region (515F/926R primers, ~410 bp) provides better coverage of Thaumarchaeota, making it a preferred choice in soil and marine studies where ammonia-oxidizing archaea play critical ecological roles. However, V4–V5 can underrepresent Firmicutes compared to V3–V4, so it is not recommended for gut microbiome studies unless specific archaeal groups are the primary research target.

Full-Length 16S Sequencing: The Species-Level Revolution

The adoption of PacBio circular consensus sequencing (CCS) and Oxford Nanopore technologies for full-length 16S analysis represents the most significant methodological advance in amplicon sequencing in recent years. By sequencing the entire ~1,500 bp 16S gene in a single read, full-length 16S sequencing achieves species-level and sometimes strain-level taxonomic resolution, resolving ambiguities inherent in single-region sequencing. Two bacterial species indistinguishable in V3–V4 often differ in V1–V2 or V6–V8 — full-length sequencing captures all of this variation. The Genome Taxonomy Database (GTDB), now the standard reference for prokaryotic taxonomy, is based on full-length 16S and genome-level data, meaning full-length amplicon sequencing aligns directly with the most current taxonomic framework.

A marine research team studying scallop (Argopecten purpuratus) larval survival in Chilean aquaculture needed to determine whether hatchery-reared spat that undergo mass mortality during transfer to the Humboldt Current upwelling zone lack specific microbial taxa that confer environmental stress tolerance. Standard V3–V4 sequencing distinguished only the dominant genera. Full-length PacBio CCS sequencing revealed the species-level differences that distinguished surviving from non-surviving cohorts — information that could not have been obtained with short-read amplicon sequencing.

The tradeoff with full-length 16S is throughput and per-sample cost: PacBio HiFi sequencing costs two to three times more per sample than Illumina for equivalent sequencing depth. However, each full-length read provides the phylogenetic information of all nine hypervariable regions simultaneously with complete linkage between them, often eliminating the need for multiple region-specific assays. For projects where species-level taxonomic assignment is the primary goal, the per-sample cost of full-length 16S is justified by the avoidance of taxonomic ambiguity.

Sequencing Depth Requirements: How Many Reads Do You Actually Need?

"How many reads per sample do I need?" is the single most common question we receive from researchers planning a 16S amplicon project. The answer depends on sample type, biomass level, research question, and tolerance for undetected rare taxa. There is no one-size-fits-all answer, but we have developed evidence-based guidelines from our project experience and the published literature.

High-biomass samples (feces, soil, sediment, biofilm): For standard community composition profiling and between-group comparison of diversity metrics, 50,000 paired-end reads per sample is generally sufficient. Rarefaction curves for most human and environmental microbiomes approach their asymptote between 10,000 and 30,000 reads. Increasing depth beyond 100,000 reads per sample yields diminishing returns — you detect increasingly rare OTUs, but the biological significance of these ultra-rare taxa in community-level analyses is often questionable. A fecal microbiome clinical trial with 188 samples, like the peanut consumption RCT in Spain, generates robust publication-ready data at 50,000 reads per sample. Our Microbial Whole Genome Sequencing service can complement 16S data for projects that require functional metagenomic insights.

Low-biomass samples (water, swabs, biopsy tissue, air filters): These samples require 100,000 to 300,000 reads per sample because total microbial DNA is low and sequencing libraries may be dominated by host DNA or reagent contaminants. For low-biomass water microbiome studies, we consistently recommend a 10-sample pilot experiment at 100,000 reads to assess community richness, depth adequacy, and contamination levels before committing to full-scale sequencing. One researcher studying groundwater well biofouling asked whether their water samples, which yielded undetectable nucleic acid levels after standard extraction, could be sequenced at all. The answer was yes, but only after optimizing the DNA extraction protocol to concentrate the minimal microbial DNA present and sequencing at higher depth to maximize recovery of the sparse community. For such challenging samples, shotgun metagenomic sequencing would have been prohibitively expensive.

Clinical samples with high host DNA background: Cervicovaginal swabs, skin swabs, and biopsy tissues often contain well over 90% host DNA. Even with host DNA depletion, the effective microbial fraction may be as low as 1–10% of total reads. For such samples, we recommend a minimum of 100,000 reads per sample. If an initial sequencing run yields fewer than 5,000 microbial reads per sample, deeper sequencing or re-extraction with enhanced host DNA removal is indicated.

The Critical Importance of Negative Controls

One of the most common and damaging mistakes in 16S study design is the omission of proper negative controls. DNA extraction kits, PCR reagents, and laboratory air all contain bacterial DNA at low levels. In low-biomass samples, these "kitome" and laboratory contaminants can dominate the sequencing results, leading to completely erroneous conclusions about the composition of the microbial community being studied. A highly publicized study claiming to have discovered a distinct placental microbiome was later shown through careful negative control analysis to have detected primarily reagent contaminants. We require extraction-negative and PCR-negative controls in every low-biomass project we sequence, and we recommend them strongly for all projects regardless of biomass level. These negative controls are sequenced alongside experimental samples and analyzed using tools like the decontam R package to identify and remove contaminant OTUs. This step is not optional for rigorous low-biomass microbiome studies, yet it is rarely mentioned in standard protocol descriptions.

For the researcher whose groundwater samples had undetectable nucleic acid levels: the negative controls were not a precautionary measure. They were the only way to distinguish the actual groundwater microbial signal from the background contamination introduced during DNA extraction and sequencing library construction. The project generated interpretable data precisely because we designed the study with negative controls from the first sample — not as an afterthought.

The OTU-to-ASV Transition in 16S Bioinformatics

The bioinformatic processing of 16S data has shifted from OTU clustering at 97% similarity to higher-resolution ASV (Amplicon Sequence Variant) analysis using DADA2 or Deblur. ASVs distinguish sequences differing by a single nucleotide, providing finer resolution, higher reproducibility across independent studies, and the ability to link individual ASVs to reference genome sequences through exact sequence matching rather than cluster-based approximation. The standard pipeline at CD Genomics follows these steps: demultiplexing and primer removal (cutadapt), quality filtering and trimming (DADA2 expected error filtering), ASV inference (DADA2 core sample inference), taxonomic assignment (SILVA 138 or GTDB), phylogenetic tree construction, alpha diversity calculation and rarefaction, beta diversity ordination (PCoA), and differential abundance testing (DESeq2, ANCOM-BC, or MaAsLin2). QIIME 2 is the most widely adopted platform for end-to-end 16S analysis. We provide both QIIME 2 output artifacts and R-compatible data formats in our standard bioinformatic deliverables package.

ITS Sequencing — Unlocking the Fungal Kingdom

Why a Separate Marker for Fungi?

Bacteria dominate most discussions of microbiome research, but fungi are ubiquitous — in soil, plant roots, animal digestive tracts, the human skin, and the built environment. A 16S rRNA survey captures prokaryotes only, missing the entire fungal component. Given that fungi are major drivers of organic matter decomposition, direct mediators of plant-microbe interactions in agricultural systems, and important opportunistic pathogens in immunocompromised patients, excluding fungi from microbiome surveys means seeing at most half of the microbial picture.

CD Genomics offers comprehensive fungal community profiling through ITS amplicon sequencing, with options for both short-read (Illumina) and long-read (PacBio, Nanopore) sequencing, depending on your taxonomic resolution requirements.

ITS1 vs. ITS2: Choosing the Right Region

ITS1 (primers ITS1f/ITS2): This primer set amplifies the ITS1 region between the 18S SSU and 5.8S rRNA. The ITS1f primer is specifically optimized to avoid co-amplification of plant DNA, making ITS1 the preferred choice for plant root tissues, rhizosphere soil, or any sample with high plant biomass. ITS1 provides better coverage of Ascomycota and Basidiomycota and is the default for most environmental fungal surveys. Amplicon length ranges from approximately 250 to over 600 base pairs depending on the fungal species, with the wide length variation presenting both analytical challenges and additional taxonomic information.

ITS2 (primers ITS3/ITS4): This primer set amplifies the ITS2 region between 5.8S and 28S LSU. ITS2 has significantly less length variation across fungal taxa compared to ITS1 — a meaningful advantage for bioinformatic clustering because it reduces the need for length-based filtering and normalization steps that can introduce bias. Some studies have shown that ITS2 provides better species-level resolution within taxonomically difficult groups, notably the genus Fusarium and other agriculturally important plant pathogenic fungi. ITS2 is recommended when the primary research focus is clinical mycology or specific pathogen detection rather than broad environmental fungal diversity.

Full-length ITS using long-read sequencing: PacBio CCS can capture both ITS1 and ITS2 plus the intervening 5.8S gene in a single amplicon of approximately 600–800 base pairs for most fungi, although some groups exceed 1,000 base pairs. CD Genomics provides Full-Length ITS Sequencing on Pacific Biosciences and Oxford Nanopore platforms for projects requiring maximum phylogenetic resolution.

ITS Data Analysis and Database Considerations

Fungal ITS bioinformatic analysis differs from 16S analysis in several important respects. The UNITE database (unite.ut.ee) is the standard reference for fungal ITS taxonomy, providing a "species hypothesis" (SH) release that clusters sequences at approximately 97% similarity for standardized OTU clustering across independent studies. A critical practical consideration is that fungal ITS reference databases remain far less complete than 16S databases. A typical soil fungal community study using ITS assigns only 60–80% of reads to named fungal genera, compared to well over 95% for bacterial 16S studies. This "dark matter" fraction of uncharacterized fungal sequences represents both a limitation and an opportunity: many ITS-based environmental studies report previously unknown fungal lineages, which can be significant contributions to discovery-driven mycology but presents challenges for ecological interpretation when the functional roles of detected organisms are unknown.

Case Studies from Inquiry Data

A researcher studying fungal community composition in plant roots submitted 18 samples for ITS amplicon sequencing. The experimental goal was to characterize the mycorrhizal fungal community colonizing the root system — arbuscular mycorrhizae, ectomycorrhizae, and dark septate endophytes — and to compare colonization patterns across soil treatments. ITS1 sequencing using the ITS1f primer set (to minimize co-amplification of plant chloroplast DNA from root tissue) was the correct methodological choice. We performed library preparation and sequencing targeting 50,000 reads per sample — sufficient to characterize the dominant mycorrhizal taxa and detect rare root-associated species that might serve as indicator taxa for soil treatment effects.

18S rRNA Sequencing — Profiling the Microbial Eukaryotes

The Overlooked Majority of Microbial Diversity

When researchers design a microbiome study using 16S and ITS as their sequencing targets, they capture the prokaryotic and fungal components. But eukaryotic microbes — protists, single-celled algae, nematodes, and other microscopic eukaryotes — can constitute a majority of both the biomass and the metabolic activity in many natural and engineered ecosystems. Photoautotrophic protists (diatoms, dinoflagellates) are the primary producers at the base of marine and freshwater food webs. Phagotrophic protists (ciliates, flagellates) graze on bacterial populations and regulate microbial community structure. Parasitic protists (Plasmodium, Giardia, Cryptosporidium, Toxoplasma, Leishmania) cause some of the most important infectious diseases worldwide. All of these organisms are invisible to both 16S and ITS sequencing. The 18S ribosomal RNA gene serves as the phylogenetic marker of choice for surveying the eukaryotic microbial community.

The 18S Gene and Regional Targeting

The 18S rRNA gene is approximately 1,800 base pairs with nine variable regions (V1–V9). Two variable regions dominate published eukaryotic metabarcoding studies:

V4 region: The most commonly targeted 18S region for eukaryotic community profiling. Amplified using primers such as TAReuk454FWD1 and TAReukREV3, it produces a ~380–430 bp amplicon with good taxonomic resolution across the eukaryotic tree of life. The V4 region was chosen by the Tara Oceans project and the Ocean Sampling Day initiative as their standard marker for eukaryotic microbial diversity, generating a massive global reference dataset. For most eukaryotic microbial ecology applications — marine protistan community surveys, freshwater plankton monitoring, and soil protist diversity assessments — 18S V4 sequencing is the recommended starting point.

V9 region: A shorter (~130 bp) hypervariable region at the 3' end of the 18S gene. V9 can be amplified and sequenced with shorter reads and is less susceptible to length variation biases. It is preferred for degraded DNA such as ancient environmental DNA from sediment cores, but its taxonomic resolution is generally lower than V4 for most eukaryotic groups.

Limitations of 18S Metabarcoding

Several challenges unique to 18S amplicon sequencing require careful attention. The PR2 (Protist Ribosomal Reference) database is the best-curated resource for eukaryotic 18S sequences, but taxonomic coverage is uneven: alveolates, stramenopiles, and rhizarians are well-represented, while less-studied protist phyla have sparse reference sequences. 18S rRNA gene copy number varies by over three orders of magnitude across eukaryotic lineages — meaning read counts do not reflect cell counts, making quantitative comparisons across disparate taxonomic groups unreliable. Contamination by non-microbial eukaryotic DNA is common: the researcher's own skin cells, the host animal or plant tissue being studied, and other macro-organisms all contain 18S sequences that amplify with universal eukaryotic primers, reducing effective sequencing depth for the protist community of interest.

18S Applications from the Inquiry Data

A marine ecology research group submitted sediment samples for 18S metabarcoding to assess changes in protistan community composition following a seasonal phytoplankton bloom. The researcher selected 18S V4 sequencing because analyzing post-bloom protistan succession required a marker capturing eukaryotic diversity broadly, including both the primary producers and their heterotrophic consumers. This question could not be answered with 16S or ITS sequencing, neither of which would recover the protistan community.

DNA Barcoding — Species Identification for the Macroscopic World

From Community Profiling to Precise Specimen Identification

Amplicon metabarcoding answers the question "What microbial community members are present in this mixed sample?" DNA barcoding answers a different but equally fundamental question: "What species is this individual organism?" A researcher studying butterfly diversity submitted 300 specimens — 75 presumed species with 4 individuals each — for COI barcoding. They needed confirmed species identity for every specimen and haplotype sequences for population genetic analysis, not community profiles from mixed DNA extracts.

The Consortium for the Barcode of Life (CBOL, barcodeoflife.org) has established standardized markers:

• COI (cytochrome c oxidase subunit I): A standardized 658 bp fragment of the mitochondrial COI gene is the universal animal barcode, amplifiable across most animal phyla using universal primers (LCO1490/HCO2198). The Barcode of Life Data System (BOLD) archives over 11 million barcode sequences from approximately 500,000 described species. CD Genomics provides DNA Barcoding Services covering COI, rbcL, matK, and ITS markers.
• rbcL and matK: The standard barcode combination for land plants. rbcL is easy to amplify with universal primers but provides limited species-level resolution. matK is more variable but harder to amplify universally. Together, the two markers achieve approximately 70–75% species-level identification success across land plants.
• ITS in the barcoding context: The same ITS region that serves as the fungal metabarcoding marker serves as the fungal barcode, but each sequence originates from a single cultured isolate or fruiting body, producing an unambiguous species-level identification.

Barcoding at Scale: From Sanger to NGS

Traditional Sanger-based barcoding is practical for tens to hundreds of specimens but becomes prohibitively expensive at the scale of thousands of specimens. For large biodiversity inventory projects, we use an NGS-based barcoding workflow. Individual specimens are processed through DNA extraction and PCR amplification in 96-well plate format, each receiving a unique barcode index. Indexed amplicons are pooled and sequenced in a single Illumina run, generating barcode sequences for thousands of specimens at a per-specimen cost below one US dollar.

Choosing Your Amplicon Approach: Decision Framework

Figure 2: Marker Gene Comparison Schematic — Side-by-side genomic maps of 16S rRNA (V1–V9), 18S rRNA, ITS (ITS1/5.8S/ITS2), and COI genes with conserved and variable regions highlighted.

The table below summarizes when to choose each method based on your research question:

Dual and Multi-Marker Strategies

A 16S + ITS dual approach captures bacteria, archaea, and fungi from the same DNA extraction, revealing cross-kingdom interactions that neither marker can detect alone. Bacterial-fungal co-occurrence networks are increasingly recognized as drivers of microbiome function in soil health, plant disease suppression, and human gut ecology. By sequencing both markers from the same set of samples, you can construct interkingdom association networks and identify potential synergistic or antagonistic relationships between bacterial and fungal community members.

For comprehensive three-domain profiling (prokaryotes + fungi + protists), a 16S + ITS + 18S triple approach is feasible in a single coordinated project but expensive. We recommend it only when eukaryotic microbial communities are a core research question, not an exploratory aim. The cost grows roughly additively with each additional marker, but the per-sample DNA requirement does not — one extraction from each sample provides sufficient template DNA for all three PCR reactions, and the library preparations run in parallel.

16S Sequencing vs. Shotgun Metagenomics: A Decision Point

A question that arises in almost every project consultation is when to upgrade from 16S amplicon sequencing to shotgun metagenomic sequencing. The short answer is that 16S sequencing answers who is there (taxonomic composition) while shotgun metagenomics answers what are they capable of (functional potential). If your research question requires knowledge of metabolic pathways, antibiotic resistance gene profiles, or strain-level genomic variation across the community, shotgun metagenomics is the appropriate method — at typically 5–10 times the per-sample cost. If your question is about community composition, diversity comparisons between groups, or changes in abundance of specific taxa following an intervention, 16S amplicon sequencing provides the right information at a fraction of the cost. For many studies, a tiered approach works best: a 16S survey across all samples to characterize community-wide patterns, followed by shotgun metagenomics on a strategically selected subset of samples for functional insights.

Figure 4: Comparison Infographic — Amplicon Sequencing vs. Shotgun Metagenomics. Side-by-side comparison across cost per sample, data type, sequencing depth required, sample type flexibility, and bioinformatics complexity.

How CD Genomics Delivers Your Amplicon Project

Figure 3: Process Flow Diagram — End-to-End Amplicon Sequencing Service. Six sequential steps from Sample through Extract, Amplify, Sequence, Analyze, to Data Delivery.

Our Amplicon Sequencing Service is designed around a complete project lifecycle from design consultation to publication-ready data. During the initial scoping discussion, we cover sample matrix and expected challenges, sample number and experimental groups, research question, budget, timeline, and any special requirements including custom primer sets.

Upon sample receipt, we measure concentration (Qubit), purity (NanoDrop A260/A280, A260/A230), and integrity (TapeStation). For raw samples, we adapt extraction protocols to the specific matrix — humic acid-rich soil requires a different approach than routine fecal samples. Water samples with undetectable nucleic acids require concentration steps and enhanced detection, often without additional charge.

Library preparation uses dual-index barcoding for multiplexing up to 384 samples per NovaSeq S4 flow cell lane. Standard primer sets include 16S V3–V4 (341F/805R), ITS1 (ITS1f/ITS2), ITS2 (ITS3/ITS4), and 18S V4 (TAReuk454FWD1/TAReukREV3). Full-Length 16S/18S/ITS Amplicon Sequencing on PacBio or Nanopore platforms is available for species-level resolution. Custom primers are supported for specialized applications including cyanobacteria-specific and archaea-specific 16S primers.

Standard bioinformatic deliverables include demultiplexed FASTQ files, ASV/OTU tables with taxonomy (SILVA or UNITE), ASV representative sequences, alpha diversity metrics (Shannon, Simpson, Chao1, Faith's PD), beta diversity ordinations (PCoA with UniFrac, Bray-Curtis, Jaccard), taxonomic bar plots, community heatmaps, and phylogenetic trees. Custom analysis — differential abundance testing (DESeq2, ANCOM-BC, MaAsLin2), functional prediction (PICRUSt2), co-occurrence network analysis, publication-ready figures — is available on request.

Standard turnaround is 3–6 weeks from sample receipt to data delivery. Small pilot projects (10–30 samples) can be completed in 2–3 weeks. Rush service is available for urgent projects. We provide detailed QC documentation for every project, including per-sample read counts, Q-score distributions, and contamination assessment. If a sample fails to amplify, we inform you before proceeding to sequencing — we do not sequence non-amplifying libraries or charge for failed library preparations.

FAQ

Q: My water samples yield undetectable nucleic acid levels after DNA extraction. Can you still sequence them?

Yes, in most cases. We can optimize the extraction protocol to concentrate minimal microbial DNA and sequence at higher depth. This requires negative controls sequenced alongside experimental samples to distinguish microbial signal from reagent contamination — a step that is not optional for low-biomass studies. Contact our scientific team to discuss your specific samples before submitting.

Q: Is duplicate sufficient or do we need triplicate biological replicates?

For any community-level comparison, a minimum of three biological replicates per experimental group is required. We recommend five replicates for high-variance communities such as soil or skin microbiomes. The most common underpowered study design is two replicates at high sequencing depth — for the same total cost, we recommend sequencing five replicates at lower depth.

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

Yes. Both markers are amplified from the same DNA extraction using separate primer sets in independent PCR reactions. We can coordinate dual-marker projects to minimize your sample handling and shipping.

Q: What is the minimum DNA amount required for amplicon sequencing?

We recommend at least 10 ng of DNA per sample for standard amplicon library preparation. For samples with lower yields, we can attempt amplification with increased cycle numbers, but the risk of PCR bias and chimera formation increases with additional cycles.

Q: How long does a typical amplicon project take from submission to data delivery?

Standard projects require 3–6 weeks: sample QC and extraction (3–5 days), library preparation (3–5 days), sequencing (1–7 days), and bioinformatic analysis (5–10 days). Small pilot projects (10–30 samples) can be completed in 2–3 weeks.

Q: Do you provide DNA extraction from raw samples, or do I need to extract first?

We offer DNA extraction from a wide range of sample types including feces, soil, water filters, swabs, tissue, and FFPE. Extraction is performed using protocols optimized for each matrix with appropriate inhibitor removal steps. Please specify whether you need extraction when requesting your quote.

Q: What is the difference between OTU and ASV analysis?

OTU (Operational Taxonomic Unit) analysis clusters sequences at 97% similarity, grouping closely related but distinct sequences. ASV (Amplicon Sequence Variant) analysis distinguishes sequences differing by a single nucleotide, providing higher resolution and reproducibility. We provide both options in our standard deliverables.

Q: Do you support custom primer sets?

Yes. We can synthesize and validate custom primers for specialized applications including group-specific 16S primers for specific bacterial phyla, archaea-specific primers, and custom markers for non-standard barcoding targets.

All references below are published under CC BY 4.0 or equivalent open-access licenses.

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