1. Introduction

16S/18S/ITS amplicon metagenomic sequencing is designated to sequence the target genes of 16S ribosomal RNA (rRNA), or 18S rRNA and Internal Transcribed Spacer (ITS) by universal primers. The services are applied to study the phylogeny and taxonomy of bacteria (and archaea) and fungi, gene variations in specific genomic regions, and the diversity of microorganisms.

Figure 1: Target genes for 16S/18S/ITS rRNA Amplicon Metagenomic Sequencing

Figure 2: Workflow of Amplicon Metagenomic Sequencing

DNA Extraction: Novogene recommends the conventional CTAB (Cetyl trimethylammonium bromide) technique or other commercial kits in the DNA extraction stage, and offers tips and method recommendations for researchers who do their own extractions. The following format shows the sample requirements.

PCR Amplification & Purification: PCR amplification of targeted regions was performed by using specific primers connecting with barcodes. In addition to the in-house primers shown in 16S/18S/ITS Amplicon Metagenomic Sequencing, Novogene also accepts customized primer designs and offer primer synthesis services for clients. PCR products are purified with magnetic beads to remove residual enzyme and primer, then undergo QC before moving to the library preparation step.

Quantification and Pooling: For sample quality control of the 16SV4, 16SV34, ITS1-1F, and ITS1-5F regions, Novogene uses Agilent 5400 (AATI) instruments with higher sensitivity and resolution. If clients prefer not to use AATI, we could redo the quantification using conventional agarose gel electrophoresis.

Library Preparation and Sequencing: The same amount of PCR products from each sample was pooled, end-repaired, A-tailed, and further ligated with Illumina adapters. The library was checked with Qubit and real-time PCR for quantification, while a bioanalyzer was used for size distribution detection. Quantified libraries were pooled and sequenced on Illumina platforms according to the effective library concentration and data amount required.

2. Standard Bioinformatics Analysis of Amplicon Metagenomic Sequencing

Two bioinformatics packages are available from Novogene, Qiime1 and Qiime2 for different research goals.

The noticed difference is Qiime1 analysis only contains OTU analysis results, while Qiime2 analysis only contains ASV analysis results. The detailed differences between Qiime1 and Qiime2 are listed below: 

Amplicon Sequence Variants – ASV (Qiime2)	Operational Taxonomic Units – OTUs (Qiime1)
●   Taxonomy annotation: cluster exact sequences into taxonomic groups (~99% similarity) ●   Up to species level ●   Readily compared between studies	●   Taxonomy annotation: cluster closely related sequences into taxonomic groups (~97% similarity) ●    Up to genus level ●    Reanalysis is required if new sequences are added to ensure OTUs accurately representing present data

The workflow of Qiime2 (Novogene’s default) is shown below.

Figure 3: Qiime2 Analysis Workflow using ASV approach

After the data were merged and filtered, the effective tags were used for further OTU/ASV cluster analysis to perform taxa annotation. To help you imagine what data visualization you can expect from Novogene services, we’ll try to interpret different plots shown

Once quality control and ASV clustering are complete, the following steps typically focus on three main modules: ASV analysis, Alpha-Diversity analysis, and Beta-Diversity analysis. These modules provide comprehensive insights into taxonomic composition, internal complexity, and differences between microbial communities.

2.1. ASV Analysis

ASV analysis is primarily used for identifying and describing the taxonomic composition and phylogenetic structure of microbial communities.

• Taxonomy Annotation: Each ASV is assigned a taxonomic label using reference databases, offering high-resolution identification from phylum to genus level.
• Species Distribution: Taxonomic profiles are visualized across samples or groups, helping to identify dominant or condition-specific taxa.
• Ternary Plot: This plot is used to visualize the distribution of dominant taxa among three groups or samples at the selected taxonomic rank (e.g. order in figure 4). Each point represents a taxon, and its position reflects its relative abundance in the three groups. For example, the plot below shows that:
  • Bacteroidales (red) clusters near D25 with the largest diameter, showing its high abundance and frequent presence in the D25 sample.
  • Enterobacterales (yellow) clusters near D26 with the smallest diameter, showing its low abundance and frequent presence in the D26 sample.

Figure 4: Ternary plot

• Phylogenetic Tree: To further study evolutionary relationships, the top 100 genera based on the species distribution plot are selected to construct a circular phylogenetic tree with these features:
  • These 100 genera are grouped by phylum (e.g., Bacteroidota, Cyanobacteria), with each phylum represented by a different branch color
  • Relative abundance of each genus (e.g. Lactobacillus) in each group (e.g. A100) was displayed as a colored box outside the circle.
  • The tree highlights whether dominant taxa are closely related or widely spread evolutionarily.

Note: A simple example of classification levels:

1. Taxa are microbes at any level of classification.
2. Kingdom → Phylum → Class → Order → Family → Genus → Species → Strain
3. Bacteria → Proteobacteria → Gamma Proteobacteria → Enterobacterales → Enterobacteriaceae → Escherichia → Escherichia coli → Escherichia coli O157:H7

Figure 5: Phylogenetic tree

• Relative Abundance Analysis: The top 10 taxa at each taxonomic level are selected to create distribution histograms (stacked bar plots) to illustrate the dominance of specific taxa across samples or groups.

Figure 6: Relative Abundance Analysis

• Taxonomic Abundance Cluster Heatmap:
  • Displays the abundance of key taxa across samples.
  • Darker colors represent higher abundance of a specific taxon in that sample.
  • Clustering helps identify samples with similar microbial profiles and distinguish between groups.

Figure 7: Taxonomic Abundance Cluster Heatmap

2.2. Alpha-Diversity Analysis

Alpha-diversity measures the richness and evenness of microbial communities within each sample, offering insights into their internal complexity and ecological stability.

• Venn Diagram (≤ 5 samples): Each circle represents a sample or group (based on normalized OTU data). Overlapping areas reflect shared OTUs, and non-overlapping regions represent unique taxa.
• Flower Diagram (> 5 samples): Similar in concept to the Venn diagram but more scalable. Each petal indicates the number of unique OTUs in a group, while the center displays the number of shared OTUs across all groups.

Figure 8: Venn and Flower Diagrams

• Rarefaction Curve: Shows the relationship between sequencing depth and species richness (OTUs/ ASVs) to determine if sequencing depth was sufficient to capture microbial diversity.
  • The X-axis represents the number of reads, and Y-axis represents Chao1 estimating species richness: each curve represents a sample; a curve plateau means that the sequencing depth is sufficient to reflect microbial diversity within a sample.
  • Group A has the highest Chao1 index (~400–450), while Group D has the lowest (~150–200), indicating a significant difference in microbial diversity between groups.
  • Most curves plateau after approximately 20,000 reads, suggesting this is an appropriate threshold for sequencing or normalizing data before comparing samples.

Figure 9: Rarefaction curve

2.3. Beta-Diversity Analysis

Beta-diversity examines differences in microbial community structure between samples or groups, highlighting patterns of similarity or dissimilarity.

• Beta diversity heatmap: visualize dissimilarities between pairwise samples based on Weighted Unifrac distance and Unweighted Unifrac distance. The heatmap based on these values is plotted in figure 10:
  • The upper value represents Weighted Unifrac distance, while the lower one (in parentheses) represents Unweighted Unifrac distance.
  • The gradient below (red → yellow) represents dissimilarity (beta diversity) across the pairwise samples.
  • Based on species presence or absence, A and B exhibit the greatest difference in microbial composition (Unweighted UniFrac = 0.671).

Note: compares between Weighted and Unweighted UniFrac

Feature	Unweighted UniFrac	Weighted UniFrac
Type of data used	Phylogenetic distance weighted by presence/ absence of taxa	Phylogenetic distance weighted by relative abundance of taxa
Sensitivity	Sensitive to rare taxa	Sensitive to dominant taxa
Interpretation	Reflects differences in microbial community composition, regardless of abundance.	Reflects differences in both microbial community and abundance.

Figure 10: Beta diversity heatmap

• Unweighted Pair-group Method with Arithmetic Mean (UPGMA): takes a distance or similarity matrix derived from 16s rRNS gene sequences or from microbial composition to construct phylogenetic trees that cluster microbes or samples based on their similarity.

Additionally, when comparing multiple samples, a distance matrix can be generated to quantify the similarities and differences among them. Subsequently, statistical methods such as:

• PCA (Principal Component Analysis)
• PCoA (Principal Coordinates Analysis)
• NMDS (Non-metric Multidimensional Scaling)

can be applied to better visualize the differences between sample groups—for example, between healthy and diseased groups, or soils planted with crop A versus crop B.

In addition, Novogene also provides advanced analysis such as environment analysis, function prediction, etc.

3. Application of Amplicon Metagenomic Sequencing

• Through 16S amplification metagenomics sequencing, Alpha Diversity and Beta Diversity of the Gut Microbiota was illustrated to determine microbial community composition between samples.
• Alterations in the microbiota were found through Taxonomic proportions at the phylum, family, and genus levels. Obtaining deep insight of specific taxa using statistical results, the relative abundances of significantly different biomarkers could be found.

In summary, GeneSmart is committed to provide our customers solutions with both optimized hands-on workflow and bioinformatics pipelines via Novogene sequencing services.

Dive deeper into 16S/18S/ITS Amplicon Metagenomic Sequencing service at GeneSmart here

4. References

• Yang, Li et al. “Comprehensive Analysis of the Relationships Between the Gut Microbiota and Fecal Metabolome in Individuals with Primary Sjogren’s Syndrome by 16S rRNA Sequencing and LC-MS-Based Metabolomics.” Frontiers in immunology vol. 13 874021. 11 May. 2022, doi:10.3389/fimmu.2022.874021
• Bulgarelli, Davide et al. “Structure and function of the bacterial root microbiota in wild and domesticated barley.” Cell host & microbe vol. 17,3 (2015): 392-403. doi:10.1016/j.chom.2015.01.011
• Langille, Morgan G I et al. “Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences.” Nature biotechnology vol. 31,9 (2013): 814-21. doi:10.1038/nbt.2676