Who Is There, What Can They Do, or What Are They Doing? Choosing a Human Microbiome Analysis Method

Every human microbiome study begins with a deceptively simple question: which sequencing method should you use? The answer depends entirely on the biological insight you need. Three sequencing-based approaches dominate the field—16S rRNA sequencing, shotgun metagenomics, and metatranscriptomics—and each answers a fundamentally different question about the microbial communities living on and within us.

Understanding the Human Microbiome: Why Method Matters

Although both 16S and shotgun approaches fall under the umbrella of metagenomics sequencing, they are designed to answer different research questions, as the Centre for Proteomic and Genomic Research explains. Choosing the right approach depends on your project objectives, the level of detail required, and your available budget. A method that excels at profiling bacterial diversity will tell you little about what those bacteria are actively doing; a method built to capture functional potential will not show which genes are actually expressed. Mismatching the method to the question is one of the most common—and most consequential—design errors in microbiome research.

16S rRNA Sequencing: Identifying "Who Is There"

16S rRNA gene sequencing is used to identify and compare the bacteria present within a given sample, according to Illumina's description of the technique. It targets a single marker gene rather than whole genomes, making it a cost-effective option for initial taxonomic profiling. The trade-off is resolution and scope: as CPGR's comparison notes, 16S detects primarily bacteria and archaea, typically resolves taxa only to genus level, and offers no functional analysis, no antimicrobial resistance detection, and limited capacity for discovering novel organisms.

There is a current push toward full-length 16S and ITS sequencing using PacBio or Oxford Nanopore technologies, but a review of microbiome analysis tools in the journal Pathogens observes that the reference databases for each marker gene lag behind the technological advances, limiting the utility of these methods.

Shotgun Metagenomics: Uncovering "What They Can Do"

Shotgun metagenomic sequencing is a DNA sequencing method that enables comprehensive sampling of all genes in all organisms in a complex microbial sample. Where marker-gene methods capture only a small portion of microbial genomes, shotgun approaches use untargeted sequencing to capture all microbial genomes present within a sample.

The payoff is breadth: CPGR's comparison shows shotgun metagenomics detects bacteria, archaea, fungi, viruses, protozoa and other microorganisms, resolves taxa to species and strain level, supports functional analysis and antimicrobial resistance detection, and is described as excellent for novel organism discovery. The cost is complexity—CPGR characterizes its data complexity as high and its bioinformatics requirements as advanced. On the analysis side, tools such as MetaPhlAn2 use unique clade-specific marker genes to differentiate microbial taxa and estimate their relative abundance.

Metatranscriptomics: Revealing "What They Are Doing"

Metatranscriptomics leverages similar analytical ideas to shotgun metagenomics but specifically captures the RNA transcribed from microbial cells, allowing assessments of the expression activities of these organisms. In Illumina's framing, microbial metatranscriptomics analyzes all RNAs encoded by a group of microorganisms in a complex sample, enabling researchers to quantify gene expression changes, study host-pathogen immune interactions, and track disease progression.

Both shotgun metagenomics and metatranscriptomic methods rely primarily on Illumina sequencing, most often the HiSeq or NovaSeq instrument families, owing to their high throughput and low cost per base. Metatranscriptomics is the only one of the three methods that measures current activity rather than composition or capability.

Presence, Potential, and Activity: A Critical Distinction

A large proportion of microbiome studies focus on the taxonomic composition of microbial communities, most commonly inferred from 16S rRNA sequencing data. But taxonomic information alone provides limited insight into the actual biological activity of microbial communities and their interactions with the host, as a review in Microorganisms emphasizes. Comparative analyses of metagenomic and metatranscriptomic data have demonstrated that gene presence does not necessarily correspond to gene expression or metabolic activity.

The practical implication is direct: a gene catalog from shotgun metagenomics tells you what a community could do, not what it is doing. Only metatranscriptomic data closes that gap. Researchers interpreting 16S profiles as evidence of functional states are working beyond what the data support.

Decision Framework: Choosing Your Microbiome Analysis Method

The table below maps each method to its primary research question, biological output, and trade-offs, so you can match your study design to the insight you need.

On cost: shotgun metagenome sequencing is usually at least double to triple the cost of 16S rRNA sequencing, although costs are falling continuously, according to Microbiome Insights. Some researchers work around this data-versus-cost dilemma by running 16S on all samples and shotgun metagenomics on a small subset. Cost per sample always depends on sequencing depth—deeper sequencing means more data and more analysis options—and a newer variation, shallow shotgun sequencing, has begun to bridge the gap between data and cost.

Analysis Pitfalls: Method Choice Is Only Half the Battle

Even a well-matched method can yield misleading results depending on how the data are analyzed. A study in Nature Communications led by Jacob Nearing and colleagues compared differential abundance methods across 38 datasets and found that different analysis pipelines produce different results. Their findings, the authors write, "should serve as a cautionary tale for researchers conducting their own microbiome data analysis and reinforce the need to accurately report the findings of a representative set of different analysis options to ensure robust results are reported."

Importantly, the same team cautions against concluding that 16S data is inherently less reliable than other microbiome data types such as shotgun metagenomics—they expect similar issues affect those analyses too, given similar or higher levels of sparsity and inter-sample variation. The lesson is not to avoid a method, but to report analysis choices transparently and, where feasible, test alternatives.

Making an Informed Choice for Your Microbiome Research

Researchers should select a microbiome analysis method based on whether their primary goal is to identify community members (16S rRNA), understand their functional potential (shotgun metagenomics), or assess their active gene expression (metatranscriptomics). The distinction matters because gene presence does not equal gene expression, and taxonomic profiles cannot substitute for functional evidence.

The measurable indicator of a sound decision is a research proposal or study design document that clearly justifies the chosen sequencing method by aligning it with specific biological questions and acknowledging its inherent trade-offs—resolution, scope of organisms detected, cost, and bioinformatics burden. If your design document can articulate why 16S suffices, or why shotgun's added cost is warranted, or why only metatranscriptomics answers your question, your method choice will withstand the scrutiny that Nearing's team reminds us every microbiome dataset deserves.

Sources

• Microbiomics: 16S Vs Shotgun Explained — CPGR
• Human Microbiome Analysis — Illumina
• Tools for Analysis of the Microbiome - PMC
• List of Available Microbiome Grants - Updated for 2025 — BLOG