Choosing Between Amplicon and Shotgun Sequencing for Targeted Diagnostics

Amplicon sequencing and shotgun metagenomic sequencing answer different diagnostic questions. Amplicon sequencing targets specific genetic regions through PCR amplification, while shotgun sequencing fragments all nucleic acids in a sample and sequences them in parallel. For diagnostic laboratories, the choice between these methods depends on the target organism, sample type, clinical question, available infrastructure, and reporting timeline. This article provides a decision framework for selecting between these approaches, with attention to cost, turnaround time, sensitivity, bioinformatics complexity, and validation requirements.

Scope and Reader Context

This guidance is written for laboratory students, technicians, researchers, and diagnostic professionals who need to select a sequencing strategy for targeted diagnostic applications. The term targeted diagnostics here refers to workflows designed to detect, identify, or characterize specific microorganisms or genetic markers in clinical, environmental, or agricultural samples. The decision framework applies to bacterial, fungal, and viral targets, with attention to the strengths and limitations of each sequencing approach.

The practical outcome of this article is a structured method for choosing between amplicon and shotgun sequencing based on diagnostic goals. The decision table in the At a Glance section summarizes the key tradeoffs. Subsequent sections explain the scientific principles, workflow considerations, quality controls, and interpretation limits that should inform that decision.

Understanding the Two Sequencing Approaches

Amplicon Sequencing Principles

Amplicon sequencing begins with PCR amplification of a specific genetic region using conserved primers. For bacteria, the 16S rRNA gene is the most common target, often the V3-V4 variable region. For fungi, the internal transcribed spacer (ITS) region or the D1-D3 ribosomal DNA region serves a similar purpose. For viruses, amplicon schemes use tiled primer sets that cover the complete genome in overlapping fragments.

The PCR step is the defining feature of amplicon sequencing. It enriches the target region from a background of host and environmental nucleic acids, which makes the method useful for samples with low target abundance. However, PCR also introduces bias. Primer-template annealing preferences can skew the representation of different taxa, and this bias can be more substantial than differences between biological replicates from the same site. A study of hydrothermal vent microbial communities found that differences between amplicon and shotgun sequencing results could be explained largely by PCR bias caused by preferential primer-template annealing.

Amplicon sequencing produces reads that are all derived from the same genetic locus. This simplifies downstream analysis because the reads can be clustered into operational taxonomic units or denoised into amplicon sequence variants. The taxonomic resolution depends on the target region. The 16S rRNA gene provides reliable genus-level identification for bacteria in many contexts, but species-level resolution is often limited. Fungal ITS amplicon sequencing can provide species-level identification for many taxa, though the resolution varies by group.

Shotgun Metagenomic Sequencing Principles

Shotgun metagenomic sequencing fragments all nucleic acids present in a sample without a prior amplification step. The resulting reads represent a mixture of host, microbial, and other environmental DNA. Taxonomic classification of these reads requires comparison against reference databases, and functional analysis can be performed by mapping reads to gene catalogs or assembling reads into genomes.

The hypothesis-free nature of shotgun sequencing is a key advantage. It can detect bacteria, fungi, viruses, and parasites from a single dataset, and it can identify organisms that would be missed by targeted amplicon approaches. A study of cervicovaginal samples from 311 women used shotgun metagenomic sequencing to profile bacterial, viral, and fungal taxa from a single dataset, demonstrating the capacity of this approach to characterize multiple microbial kingdoms simultaneously.

Shotgun sequencing also provides functional information. Beyond taxonomic identification, the data can be used to assess the presence of genes involved in metabolic pathways, antimicrobial resistance, and virulence. A study of children with nonalcoholic fatty liver disease used shotgun metagenomic sequencing to identify genes contributing to bacterial pathways, including lipopolysaccharide biosynthesis and flagellar assembly, which were associated with disease severity.

The cost of shotgun sequencing is generally higher than amplicon sequencing because more sequencing depth is required to detect low-abundance organisms against a background of host and other nucleic acids. The predominance of host nucleic acid in clinical samples can overshadow low-abundance pathogen sequences and increase the cost of metagenomic sequencing due to the high sequencing depth required.

At a Glance: Decision Table for Sequencing Approach Selection

Cost and Turnaround Time Considerations

Cost Structure of Amplicon Sequencing

Amplicon sequencing has a lower per-sample cost than shotgun sequencing because the PCR step enriches the target region, reducing the sequencing depth needed to obtain useful data. The cost components include primer synthesis or purchase, PCR reagents, library preparation, and sequencing. For high-throughput applications such as epidemiological studies, the lower cost of amplicon sequencing allows larger sample numbers within a fixed budget.

A large cohort study of 1,772 participants with overlapping 16S V4 rRNA gene amplicon, ITS1 fungal amplicon, and shotgun sequencing data demonstrated that 16S V4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. This finding supports the use of amplicon sequencing when genus-level bacterial identification is sufficient and cost is a primary constraint.

Cost Structure of Shotgun Sequencing

Shotgun sequencing requires substantially more sequencing depth to achieve useful coverage of microbial genomes in samples with high host background. The cost scales with the amount of sequencing required, and for clinical samples with abundant host nucleic acids, the cost can be considerable. Enrichment techniques can reduce this cost by selectively amplifying pathogen-specific sequences, but these techniques compromise the hypothesis-free nature of shotgun sequencing.

A review of enrichment techniques for clinical metagenomics noted that the predominance of host nucleic acid in most samples poses a significant challenge, often overshadowing low-abundance pathogen sequences and increasing the cost of metagenomic sequencing due to the high sequencing depth required. Enrichment methods including PCR-based enrichment, CRISPR-Cas9 enrichment, molecular inversion probes, nanopore adaptive sequencing, and hybridisation capture can improve sensitivity and efficiency, but they narrow the detection scope.

Turnaround Time Differences

Turnaround time is a critical factor in diagnostic applications where clinical decisions depend on rapid results. Targeted next-generation sequencing can achieve the rapid detection of known resistance genes within 8 to 24 hours, while whole-genome sequencing provides comprehensive resistance profiling over 24 to 48 hours. Metagenomic sequencing offers broader detection but at longer processing times.

Amplicon sequencing with nanopore platforms can provide particularly rapid results. A study of fungal infection diagnostics using ITS and D1-D3 nanopore amplicon metagenomic sequencing found that sequencing was faster than culturing, with a mean difference of 4.92 days for the fungal infection group and 4.67 days for the fungal colonization group. This speed advantage is relevant for diagnostic laboratories where culture-based methods are the current standard.

Sensitivity and Detection Limits

Amplicon Sequencing Sensitivity

The PCR amplification step in amplicon sequencing provides inherent sensitivity for the target region. Even samples with very low target abundance can yield sufficient amplicon for sequencing. A study of tick-borne encephalitis virus developed a novel primer scheme for amplification of the complete viral genome and successfully obtained nearly complete genomes from all clinical samples, including those with extremely low viral loads. The amplicon-based strategy was compared to direct shotgun sequencing, and comparison of consensus sequences showed no difference between the two approaches.

This sensitivity makes amplicon sequencing valuable for samples where the target organism is present at low concentration. However, the sensitivity is limited to the specific target region. Organisms outside the primer scope will not be detected, regardless of their abundance.

Shotgun Sequencing Sensitivity

Shotgun sequencing sensitivity depends on sequencing depth and the proportion of target nucleic acids in the sample. In samples with high host background, low-abundance pathogens may be missed unless sequencing depth is increased or enrichment techniques are applied. The sensitivity for a given organism also depends on the availability of reference sequences for taxonomic classification.

A study comparing 16S rRNA amplicon and shotgun metagenomic sequencing for circulating microbiome detection in blood samples found that 16S rRNA amplicon sequencing captured a broader range of microbial signals than shotgun metagenomics. The shotgun approach generated a mean depth of 234,152,679.2 reads per sample, while the amplicon approach generated a mean depth of 220,422.6 reads per sample. Despite the much higher sequencing depth in the shotgun approach, the amplicon method captured more diverse microbial signals.

This finding illustrates an important principle: amplicon sequencing can detect low-abundance targets that shotgun sequencing misses because the PCR step enriches the target region. Shotgun sequencing distributes its sequencing effort across all nucleic acids in the sample, so low-abundance organisms may fall below the detection threshold.

Fungal Detection Considerations

Fungal detection presents specific challenges for both sequencing approaches. Fungi are often present at lower abundance than bacteria in clinical and environmental samples, and their cell walls require specific lysis methods for efficient DNA extraction. A study of gut mycobiome maturation in infants compared ITS2 amplicon and shotgun metagenomic sequencing and found that less resolution of taxa to species and genera levels was observed for the metagenomic dataset. The predominant taxa identified by both approaches, including Candida albicans, Saccharomyces cerevisiae, and Malassezia restricta, exhibited similar dynamics in abundances and prevalences over the first two years of life.

A large epidemiological study found that for fungal taxa, there was no meaningful agreement between shotgun and ITS1 amplicon results, in contrast to the good agreement observed for bacterial taxa at the genus level. This discrepancy suggests that fungal detection with shotgun sequencing is less reliable than bacterial detection, and that amplicon sequencing may be the preferred approach when fungal targets are the primary diagnostic question.

Taxonomic Resolution and Functional Information

Bacterial Genus-Level Identification

For bacterial identification at the genus level, amplicon and shotgun sequencing can provide equivalent accuracy. The large cohort study of 1,772 participants demonstrated that 16S V4 amplicon sequencing and shotgun metagenomics offer the same level of taxonomic accuracy for bacteria at the genus level even at shallow sequencing depths. This equivalence supports the use of amplicon sequencing when genus-level identification is sufficient for the diagnostic question.

Species-Level Resolution

Species-level resolution is more challenging. Amplicon sequencing of the 16S rRNA gene often cannot distinguish between closely related species because the variable regions do not contain sufficient discriminatory information. Shotgun sequencing can provide species-level identification when sequencing depth is adequate and reference genomes are available.

A study of the microbiome of traditional Greek cheese products used both 16S rDNA amplicon sequencing and shotgun metagenomics. Shotgun analysis identified species including Lactococcus lactis, Lactococcus raffinolactis, Streptococcus thermophilus, Streptococcus gallolyticus, Escherichia coli, Hafnia alvei, Streptococcus parauberis, and Enterococcus durans. The amplicon approach provided genus-level information but could not achieve the same species-level resolution.

For diagnostic applications where species-level identification is clinically important, such as distinguishing between pathogenic and commensal species within the same genus, shotgun sequencing may be necessary. However, the cost and turnaround time implications must be considered.

Functional Gene Content

Shotgun sequencing provides functional information that amplicon sequencing cannot. The data can be analyzed for the presence of genes involved in metabolic pathways, antimicrobial resistance, virulence factors, and other functional categories. A study of children with nonalcoholic fatty liver disease used shotgun metagenomic sequencing to identify genes contributing to bacterial pathways. Genes for lipopolysaccharide biosynthesis were enriched in microbiomes from children with nonalcoholic steatohepatitis, and genes involved in flagellar assembly were enriched in patients with moderate to severe fibrosis.

For diagnostic applications involving antimicrobial resistance, shotgun sequencing can identify resistance genes directly. A review of sequencing technologies for antimicrobial resistance detection in bloodstream infections noted that whole-genome sequencing provides comprehensive genome-wide resistance profiling over 24 to 48 hours, while targeted next-generation sequencing can detect known resistance genes within 8 to 24 hours. Metagenomic sequencing offers broader detection, including rare or unexpected pathogens, although at higher cost and longer processing times.

Bioinformatics Complexity and Infrastructure Requirements

Amplicon Sequencing Analysis

Amplicon sequencing analysis is well established and supported by mature software pipelines. The reads are all derived from the same genetic locus, which simplifies quality filtering, denoising or clustering, and taxonomic assignment. Standard pipelines include DADA2 and QIIME2 for amplicon sequence variant generation and downstream diversity analysis.

The computational requirements for amplicon analysis are modest compared to shotgun analysis. A laboratory with basic bioinformatics capacity can process amplicon data with standard desktop computers or small servers. The analysis time is short, which supports rapid turnaround for diagnostic applications.

Shotgun Sequencing Analysis

Shotgun sequencing analysis is more complex and computationally demanding. The reads must be quality filtered, host sequences must be removed, and the remaining reads must be classified taxonomically or assembled into genomes. Common tools include Kraken2 for taxonomic classification, MetaPhlAn for species-level profiling, and various assemblers for genome reconstruction.

The choice of bioinformatics tools can significantly impact results. A benchmarking study of SARS-CoV-2 subgenomic RNA detection found substantial performance variability among common tools. Tools developed to identify subgenomic RNAs struggled with shotgun data and were sensitive to mutations depending on the chosen aligner, while amplicon sequencing improved detection sensitivity, with aligners and primer design choices still significantly impacting outcomes. The study highlighted the need for benchmarking steps and analyses to inform workflow selection, noting that without such evaluations, researchers risk drawing inaccurate conclusions from suboptimal workflows.

Harmonization of Amplicon and Shotgun Data

For epidemiological studies or longitudinal diagnostic programs, the ability to combine amplicon and shotgun data from different samples or time points is valuable. The large cohort study demonstrated that amplicon and shotgun data can be harmonized and pooled to yield larger microbiome datasets with excellent agreement, with less than 1% effect size variance across three independent outcomes when using pooled amplicon and shotgun data compared to pure shotgun metagenomic analysis.

This harmonization capability allows researchers to leverage the massive amount of amplicon sequencing data generated over the last two decades while incorporating shotgun data from new samples. For diagnostic programs that span multiple years or multiple laboratories, this pooling approach can maintain continuity while allowing methodological evolution.

Practical Workflow for Method Selection

Step 1: Define the Diagnostic Question

The first step in selecting a sequencing approach is to define the diagnostic question precisely. Consider the following questions:

• What organism or group of organisms is the target?
• Is genus-level identification sufficient, or is species-level resolution required?
• Is functional information needed, such as antimicrobial resistance genes or virulence factors?
• Is the target expected to be present at high or low abundance?
• Are unexpected pathogens a concern, or is the target known in advance?

Document the answers to these questions in the laboratory records. This documentation supports the method selection decision and provides a reference for future validation.

Step 2: Assess Sample Characteristics

The sample type and quality influence the choice of sequencing approach. Consider the following factors:

• What is the expected host nucleic acid background?
• What is the expected microbial load?
• Are multiple microbial kingdoms relevant, such as bacteria and fungi?
• Is the sample fresh or stored, and what preservation method was used?
• What nucleic acid extraction method will be used, and does it lyse the target organisms efficiently?

Samples with high host background, such as tissue biopsies or blood, may benefit from amplicon sequencing or from shotgun sequencing with enrichment techniques. Samples with low host background, such as stool or environmental samples, may be suitable for shotgun sequencing without enrichment.

Step 3: Evaluate Infrastructure and Resources

The laboratory infrastructure and resources determine which approaches are feasible. Consider the following factors:

• What sequencing platforms are available?
• What is the bioinformatics capacity, including hardware, software, and personnel?
• What is the budget per sample and per run?
• What is the required turnaround time for clinical decisions?
• What validation and quality control procedures are in place?

A laboratory with limited bioinformatics capacity may choose amplicon sequencing because the analysis pipelines are simpler and the computational requirements are lower. A laboratory with robust bioinformatics support may choose shotgun sequencing to gain functional information and broader detection.

Step 4: Select the Approach and Document the Rationale

Based on the diagnostic question, sample characteristics, and infrastructure, select the sequencing approach and document the rationale in the laboratory records. The documentation should include:

• The diagnostic question and clinical context
• The sample types and expected characteristics
• The selected sequencing approach and the reasons for the choice
• The expected sensitivity, resolution, and turnaround time
• The validation status of the method for the intended use

This documentation supports quality management and provides a basis for method comparison and improvement over time.

Step 5: Validate the Method for the Intended Use

Before implementing a sequencing approach for diagnostic use, validate the method for the intended application. The validation should include:

• Analytical sensitivity, determined by testing samples with known target concentrations
• Analytical specificity, determined by testing samples with related but non-target organisms
• Precision, determined by replicate testing of the same samples
• Reproducibility, determined by testing across runs and operators
• Comparison with a reference method, where available

The World Health Organization Laboratory Quality Management System Handbook provides guidance on validation and quality management for laboratory methods. The U.S. Food and Drug Administration Bioanalytical Method Validation Guidance provides additional context for validation of analytical methods used in regulated settings.

Records and Measurements

Required Records for Method Selection

Maintain the following records for each sequencing method implementation:

• Method selection documentation, including the diagnostic question and rationale for the chosen approach
• Validation data, including sensitivity, specificity, precision, and reproducibility results
• Sample tracking records, including collection date, preservation method, extraction method, and storage conditions
• Sequencing run records, including platform, reagent lot numbers, quality metrics, and any run failures
• Analysis records, including software versions, parameters, and database versions
• Interpretation records, including the basis for clinical or diagnostic conclusions

These records support quality management, troubleshooting, and method improvement. They also provide evidence of compliance with quality standards for diagnostic laboratories.

Key Measurements for Quality Monitoring

Monitor the following measurements to assess sequencing method performance:

• Sequencing depth per sample, which affects detection sensitivity
• Read quality scores, which affect the reliability of taxonomic assignments
• Positive and negative control results, which detect contamination and reagent issues
• Turnaround time from sample receipt to result reporting
• Detection rates for expected targets in clinical samples
• Concordance with reference methods, where available

For amplicon sequencing, also monitor PCR amplification success and the distribution of reads across samples in a batch. For shotgun sequencing, monitor the proportion of host reads, which affects the effective depth for microbial detection.

Common Failure Patterns and Troubleshooting

Amplicon Sequencing Failures

Amplicon sequencing can fail for several reasons. PCR amplification failure occurs when the target region is not amplified, often due to primer mismatches with the target organism or inhibitors in the sample. The result is no sequence data or very low read counts. Troubleshooting includes testing alternative primer sets, optimizing PCR conditions, and assessing sample inhibition with spike-in controls.

Primer bias is a more subtle failure mode. Preferential primer-template annealing can skew the representation of different taxa, leading to inaccurate abundance estimates. A study of hydrothermal vent communities found that amplicon versus shotgun sequencing differences could be explained to a large extent by bias introduced during PCR. This bias can be reduced by using primers with broad specificity and by validating the method with mock communities of known composition.

Contamination is a particular risk for amplicon sequencing because the PCR step amplifies any template present, including contaminants from reagents, laboratory surfaces, or previous samples. Negative controls are essential to detect contamination, and the World Health Organization Laboratory Biosafety Manual provides guidance on preventing contamination in laboratory workflows.

Shotgun Sequencing Failures

Shotgun sequencing failures often relate to insufficient sequencing depth or poor data quality. In samples with high host background, the proportion of microbial reads may be too low to support detection of low-abundance targets. Troubleshooting includes increasing sequencing depth, using enrichment techniques to deplete host nucleic acids or enrich pathogen sequences, and optimizing the extraction method to maximize microbial DNA yield.

Bioinformatics failures can also compromise shotgun sequencing results. The choice of taxonomic classifier, reference database, and analysis parameters can significantly impact results. A benchmarking study of SARS-CoV-2 subgenomic RNA detection found that common tools struggled with shotgun data and were sensitive to mutations depending on the chosen aligner. The study emphasized the need for systematic benchmarking to inform workflow selection.

Cross-Method Discordance

Amplicon and shotgun sequencing can produce discordant results for the same samples. A study of circulating microbiome detection in blood found that although the taxonomic profiles from both sequencing methods showed limited overlap, the core microbiota common to both were still identified. A study of the gut microbiome in infants with suspected food protein induced proctocolitis found that amplicon sequencing identified genus-level differences between patients and controls, while shotgun analysis provided species-level information that complemented the amplicon results.

When discordance occurs, consider the following explanations:

• PCR bias in the amplicon method may overrepresent or underrepresent certain taxa
• Sequencing depth in the shotgun method may be insufficient for low-abundance taxa
• Reference database limitations may affect taxonomic classification in either method
• Extraction method differences may affect the recovery of DNA from different organism groups

Document discordant results and investigate the causes before drawing diagnostic conclusions.

Limitations and Interpretation Boundaries

Amplicon Sequencing Limitations

Amplicon sequencing has several inherent limitations that affect interpretation:

• Detection is limited to the target region. Organisms outside the primer scope will not be detected, regardless of their clinical significance.
• Taxonomic resolution is limited by the information content of the target region. The 16S rRNA gene often cannot distinguish closely related species.
• Functional information is not directly available. The presence of a taxon does not indicate the presence of specific genes or metabolic capabilities.
• PCR bias can distort abundance estimates. Relative abundances from amplicon sequencing may not reflect true proportions in the sample.
• Primer design affects detection. Organisms with sequence mismatches in the primer binding regions may be underdetected or missed entirely.

Shotgun Sequencing Limitations

Shotgun sequencing also has limitations that affect interpretation:

• Detection depends on sequencing depth and the proportion of target nucleic acids in the sample. Low-abundance organisms may be missed.
• Host nucleic acid background can overwhelm microbial sequences, particularly in clinical samples.
• Reference database completeness affects taxonomic classification. Organisms without close relatives in the database may be classified incorrectly or not at all.
• Computational requirements are substantial, and the choice of analysis tools can significantly impact results.
• Cost per sample is higher than amplicon sequencing, which may limit sample numbers within a fixed budget.

Method-Specific Considerations for Fungi

Fungal detection presents particular challenges for both methods. A study of gut mycobiome maturation found that less resolution of taxa to species and genera levels was observed for the metagenomic dataset compared to the ITS2 amplicon dataset. A large epidemiological study found no meaningful agreement between shotgun and ITS1 amplicon results for fungal taxa, in contrast to the good agreement observed for bacterial taxa.

For diagnostic applications where fungal targets are the primary question, amplicon sequencing of the ITS region or D1-D3 ribosomal DNA may be more reliable than shotgun sequencing. A study of fungal infection diagnostics using nanopore amplicon sequencing found that sequencing was faster than culturing and had a higher detection rate for uncommon fungal pathogens, including Trichosporon asahii and Phycomyces blakesleeanus.

Safety and Regulatory Context

Biosafety Considerations

Laboratory safety is a primary consideration for any sequencing workflow. The World Health Organization Laboratory Biosafety Manual provides guidance on safe handling of clinical samples, nucleic acid extraction, and PCR amplification. Key considerations include:

• Sample collection and transport must follow established biosafety protocols
• Nucleic acid extraction should be performed in appropriate containment facilities
• PCR amplification creates amplicons that can contaminate the laboratory, so separate areas for pre-amplification and post-amplification work are recommended
• Waste disposal must follow institutional and regulatory requirements

The Laboratory Quality Management System Handbook from the World Health Organization provides additional guidance on quality management for laboratory testing, including documentation, validation, and quality control requirements.

Quality Management Requirements

Diagnostic laboratories must operate within a quality management framework. The World Health Organization Laboratory Quality Management System Handbook describes the components of a quality management system, including organization, personnel, equipment, purchasing and inventory, process control, information management, documents and records, occurrence management, assessment, process improvement, customer service, and facilities and safety.

For sequencing methods, quality management includes:

• Validation of the method for the intended use before implementation
• Ongoing quality control with positive and negative controls
• Participation in proficiency testing programs, where available
• Documentation of all procedures, results, and interpretations
• Investigation of failures and implementation of corrective actions

The U.S. Food and Drug Administration Bioanalytical Method Validation Guidance provides additional context for validation of analytical methods, including considerations for selectivity, sensitivity, accuracy, precision, and stability.

Professional Escalation Criteria

Laboratory professionals should escalate issues to supervisors or clinical colleagues when:

• Sequencing results are discordant with clinical presentation or reference methods
• Quality control failures indicate potential contamination or reagent issues
• Results have implications for patient management that exceed the laboratory's scope of practice
• Method limitations affect the interpretation of results for specific clinical questions
• Unexpected organisms are detected that may have public health implications

Document all escalations and the resulting actions in the laboratory records.

Frequently Asked Questions

What is the main difference between amplicon and shotgun sequencing?

Amplicon sequencing uses PCR to amplify a specific genetic region before sequencing, which enriches the target and reduces the sequencing depth needed. Shotgun sequencing fragments and sequences all nucleic acids in a sample without prior amplification, providing a broader view but requiring more sequencing depth to detect low-abundance organisms. The choice between them depends on whether the diagnostic question targets a known organism or requires hypothesis-free detection.

When should I choose amplicon sequencing over shotgun sequencing?

Choose amplicon sequencing when the target organism is known in advance, genus-level identification is sufficient for bacteria, cost is a primary constraint, rapid turnaround is required, or the sample has a high host nucleic acid background. Amplicon sequencing is also preferred for fungal targets because shotgun sequencing has shown limited agreement with ITS amplicon results for fungal taxa.

When should I choose shotgun sequencing over amplicon sequencing?

Choose shotgun sequencing when species-level resolution is required, functional information such as antimicrobial resistance genes is needed, unexpected pathogens are a concern, or multiple microbial kingdoms must be characterized from a single dataset. Shotgun sequencing also provides data that can be reanalyzed for different questions as reference databases improve.

Can amplicon and shotgun sequencing data be combined in a single study?

Yes. A large cohort study demonstrated that amplicon and shotgun data can be harmonized and pooled to yield larger datasets with excellent agreement, with less than 1% effect size variance across three independent outcomes when using pooled data compared to pure shotgun analysis. This approach allows researchers to combine historical amplicon data with new shotgun data.

How does sequencing depth affect the choice between methods?

Amplicon sequencing requires less sequencing depth because the PCR step enriches the target region. Shotgun sequencing requires substantially more depth to detect low-abundance organisms against a background of host and other nucleic acids. A study of circulating microbiome detection found that shotgun sequencing generated a mean depth of 234 million reads per sample while amplicon sequencing generated 220 thousand reads per sample, yet the amplicon method captured a broader range of microbial signals.

What are the bioinformatics requirements for each method?

Amplicon sequencing analysis is simpler and computationally less demanding, with established pipelines such as DADA2 and QIIME2. Shotgun sequencing analysis is more complex, requiring tools for quality filtering, host sequence removal, taxonomic classification, and potentially genome assembly. The choice of tools can significantly impact results, so benchmarking is essential for reliable shotgun analysis.

How do the methods compare for antimicrobial resistance detection?

Targeted sequencing approaches can detect known resistance genes within 8 to 24 hours, while whole-genome sequencing provides comprehensive resistance profiling over 24 to 48 hours. Metagenomic sequencing offers broader detection, including rare or unexpected pathogens, but at higher cost and longer processing times. The choice depends on whether the resistance genes of interest are known in advance and how quickly results are needed.

What quality controls are essential for sequencing-based diagnostics?

Essential quality controls include positive controls with known target sequences, negative controls to detect contamination, and spike-in controls to assess inhibition and extraction efficiency. For amplicon sequencing, monitor PCR amplification success and read distribution across samples. For shotgun sequencing, monitor sequencing depth, read quality, and the proportion of host reads. Document all quality control results and investigate any failures before reporting results.

Related Diagnostic Guides

• Digital Droplet PCR for Absolute Quantification of Canine Parvovirus in Fecal Samples: A High-Sensitivity Molecular Diagnostic Approach
• Multiplex Real-Time RT-PCR for Simultaneous Detection and Subtyping of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and Swine Influenza A Virus in Oral Fluids: Analytical Sensitivity and Diagnostic Performance
• Multiplex Real-Time RT-PCR Detection of Canine Respiratory Pathogens Including Canine Distemper Virus, Bordetella bronchiseptica, and H3N8 Influenza: Analytical Sensitivity and Clinical Validation in Nasal Swabs
• CRISPR-Based Diagnostics for Avian Influenza
• Cloud-Based Diagnostic Data Integration for Herd Health Management

References and Further Reading

• Laboratory Quality Management System Handbook. World Health Organization.
• Laboratory Biosafety Manual. World Health Organization.
• Assay Guidance Manual. National Center for Advancing Translational Sciences.
• Bioanalytical Method Validation Guidance. U.S. Food and Drug Administration.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Circulating microbiome profiling in transjugular intrahepatic portosystemic shunt patients: 16S rRNA vs. shotgun sequencing.. Frontiers in medicine, 2025.
• Gut Microbiota Dysbiosis in Suspected Food Protein Induced Proctocolitis-A Prospective Comparative Cohort Trial.. Journal of pediatric gastroenterology and nutrition, 2023.
• Microbiome Signatures Associated With Steatohepatitis and Moderate to Severe Fibrosis in Children With Nonalcoholic Fatty Liver Disease.. Gastroenterology, 2019.
• Exploring the composition and diversity of microbial communities at the Jan Mayen hydrothermal vent field using RNA and DNA.. FEMS microbiology ecology, 2011.
• Specific Bacteria and Metabolites Associated With Response to Fecal Microbiota Transplantation in Patients With Ulcerative Colitis.. Gastroenterology, 2019.
• How benchmarking of bioinformatics tools is essential for informed workflow selection: a case study on SARS-CoV-2 subgenomic RNA detection.. 2026.
• Enrichment techniques for clinical metagenomics.. 2026.
• Cervicovaginal Mycobiome Restructuring by HPV and Bacterial Community State Types in a Kazakhstani Shotgun Metagenomic Cohort: &lt,i&gt,Lactobacillus iners&lt,/i&gt, as a &lt,i&gt,Candida&lt,/i&gt,-Permissive Niche Associated with α-9 HPV in Cytologically Normal Women.. 2026.
• Molecular Mechanisms Underlying the Higher Prevalence of Anemia in Crohn’s Disease Compared with Ulcerative Colitis: A Systematic Review. 2026.
• Profiling the Athletes' Gut Microbiome: A Critical Methodological Perspective on 16S Metabarcoding and Shotgun Metagenomics.. 2026.
• Comparative Evaluation of Sequencing Technologies for Detecting Antimicrobial Resistance in Bloodstream Infections.. 2025.
• Gut mycobiome maturation and its determinants during early childhood: a comparison of ITS2 amplicon and shotgun metagenomic sequencing approaches. Frontiers in Microbiology, 2025.
• Exploring Microbial Rhizosphere Communities in Asymptomatic and Symptomatic Apple Trees Using Amplicon Sequencing and Shotgun Metagenomics. Agronomy, 2024.
• Investigation of the Microbiome of Industrial PDO Sfela Cheese and Its Artisanal Variants Using 16S rDNA Amplicon Sequencing and Shotgun Metagenomics. Foods, 2024.
• Study of the Microbiome of the Cretan Sour Cream Staka Using Amplicon Sequencing and Shotgun Metagenomics and Isolation of Novel Strains with an Important Antimicrobial Potential. Foods, 2024.
• Comprehensive evaluation of shotgun metagenomics, amplicon sequencing, and harmonization of these platforms for epidemiological studies. Cell Reports Methods, 2023.
• Complete Genome Sequencing of Tick-Borne Encephalitis Virus Directly from Clinical Samples: Comparison of Shotgun Metagenomic and Targeted Amplicon-Based Sequencing. Viruses, 2022.
• Real-time application of ITS and D1-D3 nanopore amplicon metagenomic sequencing in fungal infections: Enhancing fungal infection diagnostics.. International Journal of Medical Microbiology, 2024.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.