Amplicons and Amplicon Sequencing

Amplicon sequencing—the targeted amplification and sequencing of specific genomic regions—is one of the most widely used and cost-effective NGS applications, enabling researchers to focus their sequencing budget on the regions that matter most for their specific research question. By focusing sequencing capacity on specific genomic loci rather than the entire genome or transcriptome, amplicon sequencing achieves the highest depth per base of any NGS method, making it the method of choice for detecting low-frequency variants in heterogeneous samples, profiling microbial communities, and identifying pathogens in clinical samples. The key enabler of amplicon sequencing’s widespread adoption is multiplex PCR, which allows dozens to hundreds of target regions to be amplified and sequenced simultaneously from a single DNA sample.

This guide is written for researchers who understand the basic concept of PCR amplification and need a practical framework for designing amplicon sequencing projects. It covers the key experimental design decisions—primer selection, multiplex PCR strategy, read length requirements, and platform choice—along with the bioinformatics pipeline and common failure modes. The focus is on decision-oriented content: which approach to choose for a given application, how to optimize multiplex PCR panels, and how to interpret amplicon sequencing data quality. Whether you are profiling microbial communities with 16S sequencing or designing a custom targeted panel for cancer mutation detection, the principles described here apply across all amplicon-based NGS applications.

Amplicon sequencing services provide standardized workflows for 16S/ITS profiling, targeted gene panels, and custom amplicon designs with optimized primer sets and validated analysis pipelines.

What Are Amplicons and Why Sequence Them?

Amplicons are DNA fragments generated by PCR amplification from a specific genomic template. In the context of NGS, amplicon sequencing refers to the high-throughput sequencing of PCR-amplified target regions to detect sequence variation at nucleotide resolution. The key advantages of amplicon sequencing over genome-wide or shotgun approaches are its sensitivity (detecting variants present at 0.1-1% frequency), low input DNA requirement (as little as 1-10 ng), and cost efficiency (focusing sequencing capacity on regions of interest).

The two major application areas of amplicon sequencing are marker gene amplicon sequencing for microbial community profiling and targeted amplicon panels for human genetics and cancer research. In marker gene amplicon sequencing, conserved regions such as the 16S rRNA gene (bacteria), 18S rRNA gene (eukaryotes), or ITS region (fungi) are amplified using universal primers that capture taxonomic information across diverse species. In targeted amplicon panels, custom primer sets are designed to amplify specific genes or genomic regions relevant to a research question—for example, cancer driver genes, pharmacogenomic markers, or pathogen resistance genes. The key technical distinction is that marker gene amplicon sequencing uses conserved primers to amplify the same genomic region across many species, while targeted amplicon panels use specific primers for each target region within a single species genome.

The choice between amplicon sequencing and shotgun sequencing (whole-genome or metagenomic) depends on whether the research question requires information from specific, predefined genomic regions or from the entire genome. 16S/ITS amplicon sequencing is the established standard for microbial community profiling when genus-level or species-level resolution is sufficient.

Figure 1. Amplicon sequencing workflow and QC checkpoints — from primer design through data analysis

Amplicon Sequencing vs. Shotgun Sequencing — A Decision Framework

The choice between amplicon sequencing and shotgun sequencing has significant implications for project cost, data resolution, and the types of biological questions that can be addressed.

Decision logic: Choose amplicon sequencing when the goal is to characterize predefined genomic regions across many samples at high depth. This applies to most microbiome profiling studies (16S/ITS), targeted cancer gene panels, and pathogen detection assays. Choose shotgun sequencing when the goal is to discover unknown variants, characterize the full functional potential of a microbial community, or analyze samples where the target regions are not known in advance.

For microbiome studies, a common approach is to start with 16S amplicon sequencing for broad community profiling and then follow up with shotgun metagenomics on selected samples for functional analysis or higher taxonomic resolution. This two-stage strategy balances cost and information content, using amplicon sequencing for the initial screening phase where sample numbers are large and the required resolution is moderate, and reserving shotgun sequencing for the subset of samples requiring deeper functional characterization. When combined with appropriate statistical power calculations, this approach can yield robust biological conclusions at a fraction of the cost of shotgun sequencing alone.

Sample size considerations for amplicon studies: For microbiome profiling, the number of samples required depends on the expected effect size and the biological variability within each group. Studies comparing two groups typically need 10-30 samples per group to detect moderate shifts in community composition. For studies aiming to detect small effect sizes or rare taxa, 50-100 samples per group may be necessary. Power analysis tools designed specifically for microbiome data (e.g., MicrobiomeAnalyst, R package micropower) incorporate the compositional structure and high dimensionality of amplicon data and should be used during the planning phase rather than after data collection.

Experimental Design — Primer Selection and Multiplex PCR

The success of an amplicon sequencing project depends primarily on the quality of the primer design. Poor primer design produces uneven coverage, amplification bias, and failed or partially amplified target regions.

Primer design considerations: Primers should have melting temperatures (Tm) within 2-4°C of each other, GC content between 40-60%, minimal secondary structure (hairpins, self-dimers, cross-dimers), and high specificity for the target region. For 16S amplicon sequencing, the choice of variable region (V3-V4, V4, V1-V2) determines taxonomic resolution — V3-V4 provides the best balance of coverage and resolution for most bacterial communities. The widely used 341F/785R primer pair amplifies the V3-V4 region with high coverage across diverse bacterial phyla but has known biases against certain taxa including the SAR11 clade and some Archaea. For targeted panels, primer pairs should produce amplicons of similar length (typically 150-400 bp for Illumina sequencing) to ensure even amplification across targets.

Multiplex PCR challenges: When multiple primer pairs are combined in a single PCR reaction, three issues arise: primer-dimer formation between different primer pairs, preferential amplification of some targets over others, and uneven coverage due to different amplification efficiencies. Pooling strategies that split primer pairs across multiple PCR reactions and then pool the products before library preparation reduce these issues. For panels exceeding 50 amplicons, a two-pool or three-pool design is standard practice. Each pool contains a subset of primer pairs designed to minimize interactions, and the PCR products are combined after amplification. For panels exceeding 200 amplicons, hybridization capture is often more reliable than multiplex PCR because it avoids the combinatorial complexity of large primer pools and produces more uniform coverage across targets.

Polymerase selection: High-fidelity polymerases (error rate <10⁻⁶ per base) are essential for amplicon sequencing to minimize amplification-introduced errors. Standard Taq polymerase introduces errors at 10⁻⁴ to 10⁻⁵ per base, which can produce false-positive variant calls. KAPA HiFi, Q5, and Phusion are commonly used for amplicon-based NGS library preparation. Cycle number should be limited to 25-35 cycles to minimize PCR bias and chimera formation.

PCR condition optimization: Beyond primer and polymerase selection, the PCR cycling conditions directly affect amplification success. Annealing temperature should be determined empirically — a gradient PCR across a 5-10°C range around the calculated Tm identifies the optimal temperature for each primer pair. Extension time should be set to approximately 30 seconds per kb of amplicon length. For multiplex PCR, a two-step cycling protocol (annealing and extension combined at 60-65°C) can reduce primer-dimer formation compared to three-step protocols. Touchdown PCR, where the annealing temperature is decreased by 0.5-1°C per cycle from an initial high temperature to a final lower temperature, improves specificity in complex primer pools by favoring on-target amplification in early cycles.

For researchers designing custom targeted panels, amplicon sequencing services provide primer design support and multiplex PCR optimization as part of the project workflow.

16S/ITS Amplicon Sequencing for Microbiome Research

Marker gene amplicon sequencing targeting the 16S rRNA gene (bacteria and archaea) or ITS region (fungi) is the most widely used method for microbial community profiling. The choice of variable region and sequencing read length directly affects the taxonomic resolution achievable.

Variable region selection: The 16S gene contains nine hypervariable regions (V1-V9) flanked by conserved sequences suitable for universal primer design. V3-V4 (2 × 300 bp on MiSeq) is the most commonly targeted region, providing genus-level resolution for most bacterial taxa. V4 alone (2 × 250 bp) is sufficient for phylum to family-level classification but may not distinguish closely related genera. V1-V2 provides better resolution for certain genera (Staphylococcus, Streptococcus) but poorer coverage of others, particularly in the Bacteroidetes phylum. Studies comparing multiple variable regions from the same samples have shown that V3-V4 and V4 produce the most consistent results across diverse bacterial communities, while V1-V2 and V6-V8 are more taxon-specific. For fungal community profiling, ITS1 is preferred over ITS2 for its higher taxonomic resolution and shorter amplicon length, though the choice also depends on the expected fungal community composition.

Read length requirements: For 2 × 300 bp sequencing on MiSeq, the full V3-V4 region (~460 bp) is covered with sufficient overlap for accurate merging. For 2 × 150 bp sequencing (NextSeq, NovaSeq), only V4 or ITS2 regions that fit within the read length should be targeted. Paired-end reads must overlap for accurate merging and ASV inference.

Bioinformatics pipeline: The standard analysis pipeline for 16S amplicon data uses DADA2 for denoising (inferring exact amplicon sequence variants, or ASVs, rather than clustering into OTUs at 97% similarity), followed by QIIME 2 for taxonomic assignment, diversity analysis, and statistical testing. The nf-core/ampliseq pipeline provides a community-validated workflow that integrates DADA2, taxonomic classification against SILVA, GTDB, or Greengenes2 databases, and downstream analysis. ASV-based analysis provides higher resolution than OTU clustering and is now the standard approach.

Database selection: SILVA is the most comprehensive 16S database and the default for most projects. GTDB provides genome-based taxonomy that is more phylogenetically accurate for certain groups. Greengenes2 is a newer database that integrates with QIIME 2. The choice between them should be based on the expected microbial community and the level of taxonomic resolution required.

Figure 2. 16S variable region selection guide — amplicon length, read length requirements, and taxonomic resolution

Targeted Amplicon Panels — Cancer and Disease Applications

Targeted amplicon panels focus sequencing depth on genes or regions of known clinical or biological relevance. The most common applications are cancer genomics, inherited disease testing, and pharmacogenomics.

Cancer panel design: Amplicon-based cancer panels target hotspots in frequently mutated oncogenes and tumor suppressor genes (e.g., EGFR, KRAS, BRAF, TP53). The ultra-high depth achievable with amplicon sequencing (1,000-10,000×) enables detection of low-frequency somatic mutations present at 0.1-5% frequency, which is essential for liquid biopsy applications where circulating tumor DNA (ctDNA) is present at low concentrations. For ctDNA detection, unique molecular identifiers (UMIs) are incorporated into the library preparation to distinguish true variants from PCR and sequencing errors, enabling accurate detection of mutations at 0.1% allele frequency. The sequencing depth required for ctDNA detection depends on the expected ctDNA fraction — for early-stage cancers where ctDNA may be as low as 0.01% of total cfDNA, depths of 50,000-100,000× per target may be necessary.

Multiplex PCR vs. hybrid capture: For small panels (<50 amplicons), multiplex PCR is more cost-effective and faster than hybrid capture. For larger panels (50-500 targets), hybrid capture provides more uniform coverage and is less affected by primer-dimer interference. The crossover point where capture becomes more cost-effective than PCR depends on the number of samples and the required depth.

Index hopping risk: For multiplexed amplicon sequencing projects with many samples, index hopping can cause misassignment of reads between samples, generating false-positive variant calls. Using unique dual indexes (UDI) instead of single indexes is strongly recommended for amplicon-based cancer panel sequencing, particularly for ctDNA applications where low-frequency variant detection is essential. The risk is highest on patterned flow cell platforms (NovaSeq 6000, NovaSeq X) where free index primers in the library can re-anneal to neighboring clusters during cluster generation, causing between-sample contamination. UDI pairs are designed so that each sample receives two independent index sequences, and only reads with both indexes matching the expected sample pair are assigned to that sample, effectively eliminating index hopping.

Targeted amplicon sequencing services support both small custom panels and larger capture-based designs with optimized protocols for ctDNA and FFPE samples.

Pathogen Detection and Outbreak Surveillance

Amplicon sequencing is increasingly used for pathogen detection, antimicrobial resistance profiling, and outbreak surveillance. Multiplex amplicon panels targeting conserved and variable regions of pathogen genomes enable simultaneous identification of the pathogen species, its genetic lineage, and its resistance gene profile.

Applications: During infectious disease outbreaks, amplicon sequencing of pathogen genomes from clinical samples provides rapid identification of the causative agent and tracking of transmission chains. The ARTIC network’s amplicon-based SARS-CoV-2 sequencing protocol, which used multiplex PCR to tile the viral genome in ~400 bp amplicons, demonstrated the power of amplicon sequencing for real-time genomic surveillance during the COVID-19 pandemic. Similar approaches are now used for influenza, mpox, and antimicrobial resistance surveillance. The key advantage of amplicon-based pathogen sequencing over metagenomic approaches is its sensitivity — amplicon sequencing can detect and characterize pathogens present at very low levels in clinical samples, where the majority of sequencing reads would be from the host genome in a metagenomic approach.

Nanopore real-time amplicon sequencing: Oxford Nanopore’s real-time sequencing capability is particularly valuable for pathogen detection, as data can be analyzed while the sequencing run is in progress, providing results within hours of sample collection rather than days. The portable MinION platform enables on-site sequencing in field settings. The trade-off is lower raw read accuracy compared to Illumina, but consensus accuracy from amplicon sequencing with sufficient coverage (>100×) is adequate for species identification and variant calling in most pathogen surveillance applications. Real-time analysis also enables adaptive sampling, where the instrument can selectively reject reads from host genomes to enrich for pathogen reads during the sequencing run. This combination of portability, real-time analysis, and targeted amplification makes Nanopore amplicon sequencing particularly well suited for outbreak response scenarios in resource-limited settings where rapid pathogen identification is critical for public health decision-making.

Figure 3. Amplicon sequencing applications — microbiome profiling, cancer panels, and pathogen detection compared

Long-Read Amplicon Sequencing — Full-Length Resolution

Short-read amplicon sequencing targets subregions of marker genes (e.g., V3-V4 of 16S). Long-read amplicon sequences can capture full-length genes (e.g., the complete 16S rRNA gene at ~1,500 bp), providing species-level or even strain-level taxonomic resolution that short-read amplicons cannot achieve.

PacBio CCS for full-length amplicons: PacBio circular consensus sequencing (CCS) generates highly accurate (>99.9%) long reads from full-length 16S amplicons. Studies comparing short-read V3-V4 with full-length 16S CCS have shown that full-length sequencing improves species-level classification by 20-40% and enables strain-level discrimination in some bacterial groups. The main limitation is lower throughput — a PacBio Sequel II/IIe run generates approximately 50-130 Gb, which for full-length 16S amplicons (~1,500 bp) translates to fewer reads per sample than short-read amplicon sequencing.

Nanopore for long amplicons: Nanopore sequencing can generate reads spanning the full 16S gene and beyond, including ITS regions and multi-gene amplicons. Tools like EasyAmplicon 2 provide integrated pipelines for long amplicon data processing. The lower raw accuracy of Nanopore is mitigated by consensus calling from high-depth amplicon sequencing and by UMI-based error correction methods that have been developed specifically for long-read amplicon data.

Full-length 16S/ITS sequencing services using PacBio CCS provide species-level resolution for microbiome projects requiring higher taxonomic precision than short-read amplicon sequencing.

Figure 4. Short-read vs. long-read amplicon sequencing — read length, resolution, and application fit

Bioinformatics Analysis of Amplicon Data

The bioinformatics pipeline for amplicon sequencing data follows a standardized workflow that processes raw reads into taxonomic abundance tables and diversity metrics.

Step 1 — Quality control and merging: Paired-end reads are quality-filtered, trimmed of primer sequences, and merged into full amplicon sequences. FastQC and MultiQC provide quality reports. Merging requires sufficient overlap between the forward and reverse reads — for 2×300 bp V3-V4 amplicons (~460 bp), the overlap is approximately 140 bp, providing high confidence for read merging. If the overlap is too short (<20 bp), the merged reads will have high error rates in the merged region and should be filtered out before downstream analysis.

Step 2 — Denoising and ASV inference: DADA2 is the standard tool for denoising amplicon data. It models the error profile of the sequencing run and infers exact amplicon sequence variants (ASVs) that differ by as little as one nucleotide. ASVs provide higher resolution than OTU clustering (97% similarity threshold) and are directly comparable across studies because they are based on sequence identity rather than clustering parameters. The DADA2 algorithm uses a parametric error model that learns the specific error rates from the data, enabling accurate differentiation between true biological variants and sequencing errors even when they differ by a single nucleotide.

Step 3 — Taxonomic assignment: ASVs are classified against reference databases (SILVA, GTDB, Greengenes2) using naive Bayesian classifiers (QIIME 2’s feature-classifier) or alignment-based methods. The classification confidence threshold should be set to ≥0.8 for genus-level assignment. For novel or poorly characterized taxa, a substantial fraction of ASVs may remain unclassified at the genus level — this is expected for environmental or clinical samples containing understudied microorganisms.

Step 4 — Diversity and differential abundance: Alpha diversity (within-sample richness and evenness), beta diversity (between-sample community dissimilarity), and differential abundance testing are performed using QIIME 2 or R packages (phyloseq, vegan, DESeq2). For differential abundance, ANCOM-BC and MaAsLin2 provide more robust results than standard DESeq2 for microbiome data because they account for the compositional nature of amplicon sequencing data, where the relative abundance of each taxon depends on the abundance of all other taxa in the sample.

Quality assessment of the analysis results: Before interpreting the biological findings, it is essential to evaluate whether the sequencing depth was sufficient for each sample. Rarefaction curves plotting the number of observed ASVs against sequencing depth should plateau for all samples — if they do not, the sequencing depth was insufficient and additional sequencing may be needed. The number of ASVs retained after filtering should be consistent with the expected microbial diversity of the sample type. A sample with substantially fewer ASVs than others in the same group may indicate a technical problem such as PCR inhibition or low biomass rather than true biological depletion.

Figure 5. Amplicon sequencing bioinformatics pipeline — from raw reads to taxonomic profiles

Common Amplicon Sequencing Pitfalls and How to Avoid Them

Figure 6. Common amplicon sequencing pitfalls — problems, causes, and solutions

Computational Requirements for Amplicon Sequencing

Amplicon sequencing generates substantially less data per sample than WGS or RNA-seq, making computational requirements modest.

• Data per sample: A standard 16S run at 50,000 reads per sample produces approximately 25 MB of FASTQ data. For a 384-sample project, plan for approximately 10 GB of raw data. Targeted amplicon panels generate slightly more data per sample (1-5 MB per sample for a 50-amplicon panel at 1,000× coverage), but the total project data volume remains modest compared to WGS or RNA-seq projects.
• Storage requirements: Total storage including merged reads, ASV tables, and analysis outputs is approximately 20-50 GB for a large project.
• Compute time: DADA2 denoising: 5-15 minutes per sample. QIIME 2 analysis: 1-4 hours total for 100+ samples.
• Memory requirements: Most amplicon analysis tools run on 4-16 GB RAM, making them accessible on standard laptops. DADA2 requires approximately 8-16 GB for large datasets with hundreds of samples. QIIME 2 and phyloseq run on 4-8 GB for most projects. The nf-core/ampliseq pipeline with containerization can run on a laptop for up to 500 samples, making amplicon bioinformatics highly accessible and eliminating the need for high-performance computing infrastructure for most microbiome projects.

Figure 7. Amplicon sequencing project decision tree — from research question to platform and read length

FAQ

What read length do I need for 16S amplicon sequencing?
For the V3-V4 region (~460 bp), 2 × 300 bp sequencing on MiSeq provides full coverage with sufficient overlap for merging. For the V4 region alone (~250 bp), 2 × 150 bp is sufficient. For full-length 16S (~1,500 bp), long-read platforms (PacBio CCS or Nanopore) are required.

How many reads per sample do I need for 16S amplicon sequencing?
For standard microbial community profiling, 10,000-50,000 reads per sample is sufficient. For detecting rare taxa (<0.1% abundance), 50,000-100,000 reads per sample may be needed. For low-biomass samples or samples with high host DNA contamination, higher read depths are required.

What is the difference between ASVs and OTUs in amplicon analysis?
ASVs (amplicon sequence variants) are exact DNA sequences inferred by denoising algorithms (DADA2) that resolve biological differences of a single nucleotide. OTUs (operational taxonomic units) are clusters of sequences at 97% similarity. ASVs provide higher resolution, are directly comparable across studies, and are now the standard for amplicon analysis.

Can I use amplicon sequencing for cancer liquid biopsy?
Yes. Amplicon-based targeted panels with ultra-high depth (1,000-10,000×) and UMI error correction can detect circulating tumor DNA mutations at 0.1% allele frequency, making them suitable for liquid biopsy applications including early detection and minimal residual disease monitoring.

How do I choose between multiplex PCR and hybrid capture for targeted panels?
For panels with fewer than 50 amplicons, multiplex PCR is faster and more cost-effective. For panels with 50-500 targets, hybrid capture provides more uniform coverage. For panels exceeding 500 targets, hybridization capture is the standard approach.

What is the role of UMIs in amplicon sequencing?
Unique molecular identifiers (UMIs) are random nucleotide sequences attached to each DNA molecule before amplification. They enable the identification and removal of PCR duplicates and the correction of PCR-introduced errors, enabling accurate detection of low-frequency variants (<1% allele frequency) that would otherwise be indistinguishable from sequencing errors.

Which 16S variable region should I choose for my study?
V3-V4 is the most commonly recommended region, providing the best balance of taxonomic coverage and resolution for most bacterial communities. V4 alone is suitable for high-throughput studies where genus-level resolution is sufficient. ITS1 or ITS2 should be used for fungal community profiling.

How do I avoid PCR bias in amplicon sequencing?
PCR bias can be minimized by using high-fidelity polymerases, limiting cycle numbers to 25-30, optimizing annealing temperatures for each primer pair, and using degenerate primers that cover sequence diversity across target taxa. Validating primer sets with mock community standards of known composition is the most reliable way to assess and correct for PCR bias.

References

1. Olivar: automated variant-aware primer design for multiplex amplicon sequencing. Nature Communications. 2024;15:49957.
2. Multi-factorial examination of amplicon sequencing workflows. BMC Microbiology. 2023;23:158.
3. EasyAmplicon 2: expanding long amplicon sequencing. Advanced Science. 2025;12:2512447.
4. Comparative evaluation of sequencing platforms for 16S analysis. Frontiers in Microbiology. 2025;16:1633360.
5. DADA2: fast and accurate sample inference from amplicon data.

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