Metagenomics vs Metabarcoding: Choosing the Right Approach for Your Study

Researchers studying microbial communities face a fundamental decision at the start of any project: whether to use shotgun metagenomics or amplicon-based metabarcoding. This decision shapes every downstream choice, from sample collection and DNA extraction to sequencing depth, bioinformatics pipelines, and the types of biological conclusions that can be drawn. Shotgun metagenomics sequences all DNA in a sample, providing both taxonomic and functional information, while metabarcoding targets a specific genetic marker, such as the 16S rRNA gene for bacteria or the ITS region for fungi, to profile community composition at a lower cost per sample. The right choice depends on your research question, sample type, budget, and bioinformatics capacity. This article provides a decision framework to help you select the appropriate method, understand the tradeoffs, and avoid common pitfalls.

Understanding the Core Differences

What Shotgun Metagenomics Measures

Shotgun metagenomics fragments all genomic DNA present in a sample and sequences random portions of it. This approach captures DNA from bacteria, archaea, fungi, viruses, and host organisms simultaneously. Because the method does not rely on a specific genetic marker, it can detect organisms that would be missed by primer-based approaches, including novel or divergent taxa. The resulting data can be used for taxonomic classification, functional gene annotation, pathway reconstruction, and even genome assembly when sequencing depth is sufficient.

A prospective comparison of shotgun metagenomics and Sanger sequencing of the 16S rRNA gene for infectious disease diagnosis found that shotgun metagenomics identified a bacterial etiology in 46.3% of cases compared to 38.8% for Sanger 16S, with the difference reaching significance at the species level where shotgun metagenomics identified species in 28 of 67 samples versus 13 of 67 for Sanger 16S (Frontiers in Microbiology, 2022). This study demonstrates the higher taxonomic resolution of shotgun metagenomics, particularly for clinical samples where culture-based methods have failed.

What Metabarcoding Measures

Metabarcoding, also called amplicon sequencing, uses PCR to amplify a specific genetic marker before sequencing. The 16S rRNA gene is the most common target for bacterial communities, while the ITS region is used for fungi and 18S for microbial eukaryotes. The method produces relative abundance data for the taxa present, based on the proportion of sequencing reads assigned to each operational taxonomic unit (OTU) or amplicon sequence variant (ASV).

A study comparing 16S rRNA amplicon sequencing and shotgun metagenomics in pediatric gut microbiomes found that 16S rRNA profiling identified a larger number of genera than shotgun metagenomics, and that both methods missed or underrepresented certain genera (Frontiers in Microbiology, 2021). This finding highlights that metabarcoding can provide broader taxonomic coverage at the genus level in some sample types, despite its lower resolution at the species level.

The Resolution Gap

The most significant difference between the two methods is taxonomic resolution. Metabarcoding typically provides reliable identification at the genus level, with variable success at the species level depending on the marker region and the taxonomic group. Shotgun metagenomics can achieve species-level and even strain-level resolution when sequencing depth is sufficient.

A study of the circulating microbiome in patients undergoing transjugular intrahepatic portosystemic shunt procedures compared 16S rRNA V3-V4 amplicon sequencing with shotgun metagenomics and found that 16S rRNA amplicon sequencing captured a broader range of microbial signals, although the taxonomic profiles from the two methods showed limited overlap (Frontiers in Medicine, 2025). The conserved core microbiota common to both methods exhibited stable diversity indices across vascular compartments, suggesting that while the methods differ in what they detect, both can identify stable community patterns.

At a Glance: Method Comparison Table

Cost and Resource Considerations

Sequencing Depth Requirements

Shotgun metagenomics requires substantially more sequencing depth than metabarcoding to achieve comparable taxonomic coverage. A study of the circulating microbiome reported mean sequencing depth of 234,152,679 reads per sample for shotgun metagenomics versus 220,422 reads per sample for 16S rRNA amplicon sequencing (Frontiers in Medicine, 2025). This thousand-fold difference in sequencing depth translates directly into cost differences.

Shallow shotgun metagenomics offers a middle ground. A study evaluating shallow shotgun sequencing of the faecal microbiome in feral horses found that it recapitulated biological patterns first described in a published amplicon dataset while providing useful insights regarding microbiome functional potential (Molecular Ecology Resources, 2023). This approach can be cost-effective for large sample sets where deep shotgun sequencing would be prohibitively expensive.

Library Preparation and Laboratory Costs

Metabarcoding benefits from established, relatively simple library preparation protocols. The PCR amplification step is straightforward, and many commercial kits are available. Shotgun metagenomics requires more complex library preparation, including fragmentation, size selection, and adapter ligation. The study of shallow shotgun sequencing validated the use of more cost-effective high-throughput library preparation techniques compared to conventional methods (Molecular Ecology Resources, 2023).

Sample Size Implications

The choice of method affects how many samples you can process within a fixed budget. Metabarcoding allows researchers to include more biological replicates or time points, which can improve statistical power for detecting community differences. A study of oral microbiome collection methods estimated that to detect an odds ratio of 1.5 with one sample per individual, sample sizes ranged from 665 for common species to 219,547 for rare species (Microbiology Spectrum, 2026). These estimates highlight the importance of considering both method choice and sample size requirements when designing studies.

Taxonomic Resolution and Detection Capabilities

Species-Level Identification

The ability to identify organisms at the species level is a major advantage of shotgun metagenomics. A study comparing cigarette smoking effects on gut microbiota in Chinese men used both 16S rRNA and shotgun metagenomic sequencing across 3,308 participants and found that metagenomic data provided higher resolution at the species level, particularly for the Actinomyces genus (BMC Medicine, 2025). The study covered 206 genera and 237 species, demonstrating the broader taxonomic scope achievable with shotgun metagenomics.

Primer Bias and Detection Gaps

Metabarcoding is subject to primer bias, where certain taxa amplify more efficiently than others due to mismatches between the primer sequences and the target gene. A study evaluating a multi-amplicon 16S rRNA sequencing method found that single variable region analyses had positive predictive values of 44% to 61%, while the multi-amplicon approach achieved 65% to 91% (Journal of Microbiological Methods, 2018). This study also found that the multi-amplicon method had an abundance estimate correlation of 0.77 with shotgun sequencing, compared to 0.46 and 0.45 for single-region V3 and V4 analyses.

A study of planktonic microbial communities in Lake Lanier found that while 16S rRNA gene amplicon sequencing captured broad shifts in community diversity over time, the metagenomes identified approximately 1.5 times as many phyla and 10 times as many genera (PLoS One, 2014). The study also identified PCR and sequencing preparation steps as sources of bias in amplicon data.

Functional Information

Metabarcoding provides no direct information about the functional potential of a microbial community. Shotgun metagenomics can identify genes, metabolic pathways, and functional capabilities. A study of host immunogenetic variation and gut microbiome functionality in Seychelles warblers used shotgun metagenomics to show that taxonomic composition was associated with MHC-II diversity while functional composition was associated with MHC-I diversity (Microbiome, 2026). This type of functional analysis is only possible with shotgun metagenomics.

Shallow shotgun metagenomics can provide some functional insights. The feral horse study demonstrated that shallow shotgun data provided useful information about microbiome functional potential that supported previously hypothesized diet effects (Molecular Ecology Resources, 2023). However, functional resolution improves with sequencing depth.

Sample Collection and Preservation Effects

Collection Method Variability

The choice of sample collection method can affect results differently for the two sequencing approaches. A study comparing oral collection methods found that saliva preserved in glycerol was more similar to unpreserved saliva than mouthwash, with higher median intraclass correlation coefficients at genus (0.88 versus 0.60), species (0.92 versus 0.64), and gene levels (0.84 versus 0.36) (Microbiology Spectrum, 2026). Room temperature storage affected saliva in glycerol more than mouthwash, with a median genus-level ICC of 0.65.

Preservation and Storage

Sample preservation is particularly important for shotgun metagenomics because DNA degradation affects all genomic regions, while metabarcoding targets a short, relatively stable region. A study of heat-assisted sample desiccation found that while desiccation had a significant negative impact on DNA extraction success, its impacts on downstream metrics such as OTU richness, Shannon diversity, gene annotation, and metagenome-assembled genome recovery were more nuanced and often insignificant (Environmental Microbiome, 2026). The preservation method significantly influenced inferred microbial community composition, but samples from different source materials remained clearly distinguishable.

Host DNA Contamination

Shotgun metagenomics sequences all DNA in a sample, including host DNA. In samples with high host content, such as tissue biopsies or blood, host DNA can dominate the sequencing output and reduce the effective depth for microbial sequences. Metabarcoding avoids this problem because the PCR amplification step specifically targets microbial marker genes. This difference is particularly important for clinical samples and host-associated microbiomes.

Bioinformatics Pipelines and Data Analysis

Metabarcoding Analysis Workflow

Metabarcoding analysis follows a relatively standardized workflow. A proposed pipeline for 16S and 23S rRNA metabarcoding data integrates read merging, primer and adapter trimming, quality filtering, dereplication, chimaera removal, and clustering into operational taxonomic units (BioTech, 2026). Taxonomic assignment uses curated reference databases such as EZBioCloud for bacteria and µgreen for microalgae. The pipeline includes modules for alpha and beta diversity analysis and provides a graphical user interface for non-specialist users.

The study of circulating microbiome profiling used an ASV-based approach with USEARCH for denoising and VSEARCH for taxonomic annotation for 16S rRNA data, while shotgun metagenomic reads were classified using the Kraken2-Bracken pipeline (Frontiers in Medicine, 2025). This illustrates the different bioinformatics tools required for each method.

Shotgun Metagenomics Analysis Workflow

Shotgun metagenomics analysis is more computationally intensive and requires multiple steps: quality trimming, host DNA removal, taxonomic classification, functional annotation, and potentially genome assembly. The choice of tools and parameters can significantly affect results. The Kraken2-Bracken pipeline used in the circulating microbiome study is one common approach, but many alternatives exist.

Reproducibility and Standardization

Reproducibility is a concern for both methods. The multi-amplicon 16S study found that replicates obtained from the same biological sample clustered together, but several biases were observed linked to PCR or sequencing preparation steps (PLoS One, 2014). Standardized protocols and careful documentation of all analysis parameters are essential for reproducible results.

The FAIR Guiding Principles provide a framework for making research data findable, accessible, interoperable, and reusable (Scientific Data, 2016). Applying these principles to microbiome data, including raw sequencing reads, processed tables, and analysis code, supports reproducibility and enables meta-analyses across studies.

Decision Framework for Method Selection

When to Choose Metabarcoding

Metabarcoding is the appropriate choice when your primary research question concerns community composition at the genus level, when you have a large number of samples, when budget constraints limit sequencing depth, or when you are working with samples where host DNA contamination is a concern. It is also suitable for studies of fungi using ITS metabarcoding, as demonstrated in a systematic review of mangrove fungi that consolidated data from 23 original research articles and 1,154 samples (World Journal of Microbiology and Biotechnology, 2026).

Metabarcoding is well suited for surveillance applications. A review of metagenomics approaches for plant pathogen detection noted that metabarcoding could be useful for surveillance of soilborne, seedborne, and airborne pathogens, as well as for identifying new pathogens and determining the origin of outbreaks (Microorganisms, 2021).

When to Choose Shotgun Metagenomics

Shotgun metagenomics is the appropriate choice when you need species-level or strain-level resolution, when you require functional information about the microbial community, when you are studying novel or poorly characterized environments, or when you need to detect organisms that may be missed by primer-based approaches.

The study of smoking and gut microbiota demonstrated that metagenomic data provided higher resolution at the species level, particularly for the Actinomyces genus (BMC Medicine, 2025). This species-level resolution was important for understanding the specific taxa associated with smoking status.

When to Consider Shallow Shotgun Metagenomics

Shallow shotgun metagenomics offers a compromise between the low cost of metabarcoding and the functional information of deep shotgun sequencing. The feral horse study demonstrated that shallow shotgun sequencing recapitulated biological patterns from amplicon data while providing functional insights (Molecular Ecology Resources, 2023). This approach may be appropriate for large sample sets where deep sequencing is not feasible but functional information is desired.

Matching Method to Research Question

The following decision criteria can guide method selection:

1. If your question is about community composition and diversity patterns at the genus level, choose metabarcoding.
2. If your question requires species-level identification, choose shotgun metagenomics.
3. If your question involves functional potential, gene content, or metabolic pathways, choose shotgun metagenomics.
4. If you have more than a few hundred samples and limited budget, choose metabarcoding or shallow shotgun metagenomics.
5. If you are studying a poorly characterized environment where novel taxa may be present, choose shotgun metagenomics.
6. If you need to detect specific pathogens at low abundance, consider targeted approaches or deep shotgun metagenomics.

Practical Implementation Steps

Step 1: Define Your Research Question and Required Resolution

Write a clear statement of the biological question you need to answer. Specify the taxonomic level required for your conclusions. If genus-level information is sufficient, metabarcoding may be appropriate. If species-level or strain-level information is needed, shotgun metagenomics is required.

Step 2: Assess Your Sample Types and Collection Conditions

Consider the expected microbial load, host DNA content, and preservation conditions for your samples. For samples with high host DNA content, such as tissue or blood, metabarcoding may be more efficient. For environmental samples with low host DNA, shotgun metagenomics may be feasible.

Step 3: Evaluate Budget and Sequencing Resources

Calculate the total cost per sample for each method, including library preparation, sequencing, and bioinformatics analysis. Consider the number of biological replicates needed for statistical power. The oral microbiome study estimated that sample sizes for detecting an odds ratio of 1.5 ranged from 665 for common species to 219,547 for rare species (Microbiology Spectrum, 2026).

Step 4: Consider Bioinformatics Capacity

Assess your access to computational resources and bioinformatics expertise. Metabarcoding analysis can be performed with established pipelines and modest computational requirements. Shotgun metagenomics requires substantial computational resources for quality filtering, host removal, taxonomic classification, and functional annotation.

Step 5: Plan for Validation and Controls

Include appropriate positive and negative controls in your experimental design. For metabarcoding, include a mock community with known composition to assess primer bias and pipeline accuracy. For shotgun metagenomics, include spike-in controls to assess quantification accuracy.

Step 6: Document All Parameters and Decisions

Record all laboratory protocols, sequencing parameters, and bioinformatics settings. This documentation is essential for reproducibility and for interpreting results in the context of methodological choices.

Records and Measurements

Key Metrics to Track

For both methods, record the following metrics:

1. DNA yield and quality for each sample
2. Sequencing depth and read quality metrics
3. Number of reads passing quality filters
4. Number of taxa detected at each taxonomic level
5. Alpha diversity metrics such as Shannon diversity and observed richness
6. Beta diversity metrics and ordination results
7. Relative abundance of key taxa

For shotgun metagenomics, additionally record:

1. Proportion of reads mapping to host genome
2. Number of genes or functional pathways annotated
3. Assembly statistics if genome assembly is performed
4. Number of metagenome-assembled genomes recovered

Quality Control Thresholds

Establish quality control thresholds before analysis and apply them consistently. For metabarcoding, typical thresholds include minimum read quality scores, maximum expected error rates, and minimum read lengths after trimming. For shotgun metagenomics, thresholds include minimum mapping quality and minimum coverage for taxonomic assignment.

The study of heat-assisted desiccation assessed multiple metrics related to DNA extraction success, sequencing, contig assembly, OTU clustering, gene annotation, and MAG recovery (Environmental Microbiome, 2026). This comprehensive approach to quality assessment is recommended for all microbiome studies.

Common Failure Patterns and How to Avoid Them

Primer Bias Leading to Incorrect Community Profiles

Primer bias is a well-documented limitation of metabarcoding. The multi-amplicon study found that single variable region analyses had positive predictive values of 44% to 61%, while the multi-amplicon approach achieved 65% to 91% (Journal of Microbiological Methods, 2018). To mitigate primer bias, consider using multiple primer sets, using a multi-amplicon approach, or validating results with shotgun metagenomics on a subset of samples.

Insufficient Sequencing Depth for Shotgun Metagenomics

Shotgun metagenomics requires substantial sequencing depth to detect rare taxa and achieve species-level resolution. The circulating microbiome study used a mean depth of 234 million reads per sample for shotgun metagenomics (Frontiers in Medicine, 2025). If your budget does not support this depth, consider shallow shotgun metagenomics or metabarcoding instead.

Host DNA Dominating Shotgun Metagenomic Data

In host-associated samples, host DNA can constitute the majority of sequencing reads, reducing the effective depth for microbial sequences. This is particularly problematic for blood, tissue, and mucosal samples. Consider host DNA depletion methods during sample processing or choose metabarcoding for these sample types.

Inconsistent Sample Collection Methods

The oral microbiome study found that collection method significantly affected results, with saliva in glycerol more similar to unpreserved saliva than mouthwash (Microbiology Spectrum, 2026). For epidemiological studies, consistent use of a single collection method and inclusion of longitudinal sampling can improve reproducibility and power.

Inadequate Bioinformatics Documentation

Failure to document bioinformatics parameters can make results irreproducible and difficult to interpret. The proposed metabarcoding pipeline emphasizes standardized processing with consistent parameterization across samples (BioTech, 2026). Apply the same standardization to shotgun metagenomics analysis.

Limitations and Interpretation Caveats

Relative Abundance Versus Absolute Abundance

Both metabarcoding and shotgun metagenomics provide relative abundance data, not absolute counts. Changes in the relative abundance of one taxon can be driven by changes in other taxa. For absolute quantification, additional methods such as quantitative PCR or spike-in controls are needed.

Method-Specific Biases

Each method has distinct biases. Metabarcoding is affected by primer bias, 16S copy number variation, and PCR errors. Shotgun metagenomics is affected by DNA extraction efficiency, sequencing depth, and reference database completeness. The study of pediatric gut microbiomes found that both methods missed or underrepresented certain genera (Frontiers in Microbiology, 2021).

Database Limitations

Taxonomic assignment depends on reference databases. The metabarcoding pipeline described uses EZBioCloud for bacteria and µgreen for microalgae (BioTech, 2026). Shotgun metagenomics classification similarly depends on the completeness and accuracy of reference genomes. Novel or poorly characterized taxa may be misclassified or missed entirely.

Cross-Study Comparability

Comparing results across studies that used different methods, primers, or bioinformatics pipelines is challenging. The review of athlete gut microbiome studies critically examined how methodological approaches influence the interpretation of results in sports contexts (Biology, 2026). Standardized protocols and careful documentation are essential for cross-study comparability.

Safety and Regulatory Context

Data Sharing and Privacy

Microbiome data from human samples may contain identifiable information and are subject to data sharing policies. The NIH Genomic Data Sharing Policy provides requirements for data sharing, including informed consent and privacy protections (National Institutes of Health). Researchers working with human microbiome samples should review applicable policies before data collection.

Data Repositories

Raw sequencing data should be deposited in public repositories to support reproducibility and secondary analysis. The NCBI provides resources for sequence data deposition and retrieval (National Center for Biotechnology Information). The EMBL-EBI also offers training and resources for bioinformatics analysis (European Bioinformatics Institute).

Biosafety Considerations

When working with clinical or environmental samples, follow appropriate biosafety protocols for sample collection, processing, and disposal. Shotgun metagenomics sequences all DNA in a sample, including potential pathogens, so consider the implications for data interpretation and reporting.

Professional Escalation Criteria

When to Seek Additional Expertise

Consider consulting with a bioinformatics specialist or microbiome methodologist when:

1. Your research question requires species-level resolution but your budget only supports metabarcoding
2. You are working with a sample type that has not been well characterized by either method
3. Your shotgun metagenomics data show unexpected patterns that may indicate contamination or technical artifacts
4. You need to compare results across studies that used different methods
5. You are planning a large epidemiological study where method choice will significantly affect statistical power

When to Reconsider Method Choice

Reconsider your method choice if:

1. Preliminary results show that your chosen method cannot detect the taxa of interest
2. Sequencing depth is insufficient to answer your research question
3. Host DNA contamination is more severe than expected
4. Bioinformatics resources are inadequate for the chosen method
5. Budget constraints prevent the sequencing depth required for reliable results

Frequently Asked Questions

What is the main difference between metagenomics and metabarcoding?

Metagenomics, specifically shotgun metagenomics, sequences all DNA in a sample without targeting specific genes. Metabarcoding uses PCR to amplify a specific genetic marker, such as the 16S rRNA gene for bacteria, before sequencing. Metagenomics provides both taxonomic and functional information with species-level resolution, while metabarcoding provides genus-level taxonomic profiles at lower cost per sample.

Can metabarcoding identify bacteria at the species level?

Metabarcoding can sometimes identify bacteria at the species level, but reliability varies by taxonomic group and marker region. A study comparing shotgun metagenomics and Sanger 16S sequencing found that shotgun metagenomics identified species in 28 of 67 samples versus 13 of 67 for Sanger 16S (Frontiers in Microbiology, 2022). For reliable species-level identification, shotgun metagenomics is generally preferred.

Is shotgun metagenomics always better than metabarcoding?

No. Shotgun metagenomics provides higher resolution and functional information, but it is more expensive, requires more bioinformatics expertise, and can be affected by host DNA contamination. A study of pediatric gut microbiomes found that 16S rRNA profiling identified a larger number of genera than shotgun metagenomics (Frontiers in Microbiology, 2021). The best method depends on your research question, sample type, and resources.

How much sequencing depth is needed for shotgun metagenomics?

Sequencing depth requirements vary by sample type and research question. A study of the circulating microbiome used a mean depth of 234,152,679 reads per sample for shotgun metagenomics (Frontiers in Medicine, 2025). Shallow shotgun metagenomics can provide useful results at lower depth, as demonstrated in a study of feral horse faecal microbiomes (Molecular Ecology Resources, 2023).

What is shallow shotgun metagenomics?

Shallow shotgun metagenomics uses lower sequencing depth than conventional shotgun metagenomics, reducing cost while still providing some functional information. A study of feral horse faecal microbiomes found that shallow shotgun sequencing recapitulated biological patterns from amplicon data and provided useful functional insights (Molecular Ecology Resources, 2023).

How does sample collection method affect microbiome results?

Sample collection method can significantly affect results. A study of oral microbiome collection methods found that saliva in glycerol was more similar to unpreserved saliva than mouthwash, with higher intraclass correlation coefficients at genus, species, and gene levels (Microbiology Spectrum, 2026). Consistent use of a single collection method is recommended for epidemiological studies.

Can metabarcoding be used for fungal communities?

Yes. The ITS region is commonly used for fungal metabarcoding. A systematic review of mangrove fungi consolidated data from 23 original research articles and 1,154 samples using ITS metabarcoding (World Journal of Microbiology and Biotechnology, 2026). Metabarcoding provides a broader view of fungal diversity than traditional collection and culturing methods.

What bioinformatics skills are needed for each method?

Metabarcoding analysis can be performed with established pipelines and modest computational resources. A proposed pipeline for 16S and 23S rRNA metabarcoding integrates all critical processing steps in a graphical user interface (BioTech, 2026). Shotgun metagenomics requires more advanced bioinformatics skills, including quality trimming, host DNA removal, taxonomic classification, and functional annotation.

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References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• Prospective Comparison Between Shotgun Metagenomics and Sanger Sequencing of the 16S rRNA Gene for the Etiological Diagnosis of Infections.. Frontiers in microbiology, 2022.
• Circulating microbiome profiling in transjugular intrahepatic portosystemic shunt patients: 16S rRNA vs. shotgun sequencing.. Frontiers in medicine, 2025.
• Shallow shotgun sequencing of the microbiome recapitulates 16S amplicon results and provides functional insights.. Molecular ecology resources, 2023.
• Comparative Analysis of 16S rRNA Gene and Metagenome Sequencing in Pediatric Gut Microbiomes.. Frontiers in microbiology, 2021.
• A multi-amplicon 16S rRNA sequencing and analysis method for improved taxonomic profiling of bacterial communities.. Journal of microbiological methods, 2018.
• Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics.. PloS one, 2014.
• Potential roles of cigarette smoking on gut microbiota profile among Chinese men.. BMC medicine, 2025.
• Comparison of oral collection methods for 16S rRNA gene and shotgun metagenomic sequencing.. Microbiology spectrum, 2026.
• Profiling the Athletes' Gut Microbiome: A Critical Methodological Perspective on 16S Metabarcoding and Shotgun Metagenomics.. 2026.
• Effects of heat-assisted sample desiccation on microbiome surveys.. 2026.
• Host immunogenetic variation and gut microbiome functionality in a wild vertebrate population.. 2026.
• Systematic Review: Long-Read Sequencing in Algal Studies. 2026.
• Bioinformatics Strategy for 16s and 23s rRNA Metabarcoding Data.. 2026.
• Worldwide diversity and ecology of mangrove fungi: a systematic review of ITS metabarcoding studies and a quantitative, integrative analysis of raw sequence data.. 2026.
• Metagenomics Approaches for the Detection and Surveillance of Emerging and Recurrent Plant Pathogens. Microorganisms, 2021.
• Soil-plant-microbiota interactions to enhance plant growth. Revista Brasileira de Ciência do Solo, 2022.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.