Precise Microbial Diversity Analysis — Fast, Sensitive 16S/18S/ITS Sequencing Services

Why Microbial Diversity Analysis Matters

Microbial communities drive nutrient cycling, suppress diseases, and maintain ecosystem balance. Analyzing their diversity enables researchers to:

• Uncover microbial interactions and ecological roles
• Monitor shifts caused by environmental changes or interventions
• Support sustainable agriculture and improve soil health
• Deepen understanding of gut, plant, and marine microbiomes

How to Choose Your Microbial Diversity Sequencing Approach

Note: This page primarily covers 16S/18S/ITS amplicon sequencing for microbial community profiling. For combined species and functional gene insights, explore our metagenomic sequencing services.

Our 16S/18S/ITS Sequencing Services at a Glance

We provide targeted and full-length amplicon sequencing solutions designed to meet diverse microbiome research needs:

Research Applications & Recommended Sequencing Services

Tailored sequencing solutions for diverse microbiome research

Not sure which approach fits your study? [Contact our experts] for personalized guidance.

End-to-End Workflow: Tailored Solutions from Sample to Insight

Step 1: Project Consultation

Personalized 1-on-1 experimental design tailored to your research goals

Step 2: Sample Quality Control

DNA integrity assessment

Concentration normalization to ensure optimal sequencing input

Step 3: Sequencing & Data Analysis

Dual-platform sequencing using Illumina and PacBio

Quality benchmarks: Illumina Q30 ≥ 90%, PacBio CCS accuracy ≥ 99%

Step 4: Report Delivery

Comprehensive package including raw sequencing data and detailed analysis report

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Technology Platforms We Use

Comprehensive Bioinformatics Analysis

Our advanced bioinformatics pipeline delivers deep insights into microbial communities, supporting detailed taxonomic, functional, and ecological profiling. With stringent quality control and cutting-edge algorithms, we provide accurate, publication-ready results tailored to your research objectives.

Core Bioinformatics Analyses

Advanced Bioinformatics Options

• Antibiotic Resistance Gene (ARG) Analysis: Gene annotation and visualization using the CARD database
• Environmental Correlations: CCA/RDA analyses linking microbial profiles to physicochemical factors
• Time-Series Analysis: Tracking microbial community dynamics over multiple time points

Sample Requirement

Note: If you wish to obtain more accurate and detailed information regarding sample requirements, please feel free to contact us directly.

Demo

Partial results of our microbial diversity analysis – 16S/18S/ITS sequencing service are shown below:

Customer Case

Microbiota of the Digestive Gland of Red Abalone (Haliotis rufescens) Is Affected by Withering Syndrome
Journal: Microorganisms
Impact factor: 4.26
Published: 2020

Find out more

Backgrounds

The study centers on Withering Syndrome (WS), an infectious disease that heavily impacts abalone aquaculture. This disease is caused by the intracellular bacterium Candidatus Xenohaliotis californiensis, which infects the digestive glands, disrupting their function and causing progressive wilting in abalones. While the direct effects of WS are understood, its impact on the microbiota of the digestive glands in abalones is not well-studied. The main goal of this research is to determine if there are differences in the digestive gland-associated microbiota between healthy red abalones and those affected by WS.

Materials & Methods

Sample preparation:

• Red abalone
• Converted land samples
• DNA extraction

Method:

• 16S rRNA Sequencing
• Illumina platform Hiseq PE25

Data Analysis:

• Statistical analysis
• Beta diversity measures
• Community composition testing
• Differential abundance analysis

Results

The study found notable differences in microbiota composition between healthy and WS-affected abalones. Healthy abalones exhibited a diverse and balanced bacterial community, whereas WS-affected abalones had a microbiota dominated by specific bacteria associated with the disease. One of the most significant findings was that a certain bacterium was dominant in healthy abalones, while the primary pathogen of WS was more prevalent in the affected group. Interestingly, the pathogen was also present in some healthy specimens, indicating that the balance between different bacterial populations might be crucial in determining the disease status.

Figure 1. (A) Relative abundance of microbiota at the phylum level in the digestive glands of healthy (H1-H5) and withering syndrome-affected red abalone (WS1-WS5). (B) Comparison of relative abundance at the phylum level between healthy (red boxes) and affected (blue boxes) abalone. (C) Comparison at the genus level.

Figure 2. Differences in digestive gland microbiota of healthy red abalones (H) compared with red abalones with withering syndrome disease (WS).

Conclusions

The findings suggest that WS significantly disrupts the microbiota composition in the digestive glands of red abalones. Specific bacterial communities are associated with either health or disease, highlighting the potential ecological role of microbiota in WS pathogenesis. Further research is needed to deepen our understanding of the dynamics of digestive gland microbiota and its influence on the progression of Withering Syndrome in abalones.

Published Research Supported by Our 16S/18S/ITS Sequencing Services

Explore representative publications by our clients, showcasing the real-world applications of 16S/18S/ITS sequencing in fields like agriculture, environment, aquaculture, and food safety.

FAQ

• Q: What types of microbes do 16S, 18S, and ITS sequencing target?
  • • 16S rRNA: Bacteria and archaea (ideal for prokaryotic profiling)
    • 18S rRNA: Eukaryotic microorganisms like algae and protozoa
    • ITS region: Fungi, especially for species-level differentiation
• Q: Should I choose V3–V4 or full-length 16S?
  • • V3–V4 region (Illumina): Cost-effective; suitable for genus/species-level studies like human gut microbiota profiling
    • Full-length 16S (PacBio): Higher resolution; excellent for resolving closely related strains such as pathogenic variants
• Q: Can 18S replace ITS for fungal sequencing?

  • Not fully.

    • 18S offers broad classification (phylum/class level)
    • ITS remains the gold standard for species-level fungal identification, especially in complex environmental samples
• Q: How many samples do I need for reliable results?
  • • Environmental samples (soil, water): ≥3 biological replicates
    • Host-associated samples (gut, skin, etc.): ≥5 individuals per group

    Tip: Adjust based on your study’s statistical power and expected variation.

• Q: What's the difference between OTUs and ASVs?
  • • OTU (Operational Taxonomic Unit): Clusters sequences by 97% similarity; may merge closely related species
    • ASV (Amplicon Sequence Variant): Offers exact sequence resolution; better for detecting low-abundance taxa

    By default, we use ASV pipelines unless OTUs are specifically requested.

• Q: What insights do α and β diversity analyses provide?
  • • α diversity: Measures species richness and evenness within a sample (e.g., Chao1, Shannon indices)
    • β diversity: Compares microbial communities between samples or groups (e.g., PCoA, NMDS clustering)
• Q: What does the LDA score in LEfSe results indicate?
  • • LDA (Linear Discriminant Analysis) score quantifies the impact of differentially abundant taxa
    • Scores >2.0 are generally considered statistically meaningful
• Q: How do I identify microbial biomarkers from the data?

  • Recommended workflow:

    1. Identify differentially abundant taxa (e.g., LEfSe or ANCOM)
    2. Evaluate diagnostic potential via ROC curves (AUC >0.7 is promising)
    3. Validate importance using Random Forest or similar machine learning models
• Q: How should I process samples from extreme environments (e.g., high salt, high temperature)?
  • • Increase lysis strength (e.g., extend lysozyme digestion)
    • Add PCR facilitators like BSA or formamide to counter inhibitors
    • Ask us about customised DNA extraction protocols
• Q: How can I reduce host DNA interference in plant endophyte studies?
  • • Use selective lysis buffers (e.g., CTAB method)
    • Choose primers that avoid chloroplast DNA (e.g., skip 1492R in the V4 region)
• Q: How should I design a time-series microbiome study?
  • • Sample at consistent time intervals (e.g., weekly/monthly)
    • Include ≥3 biological replicates per time point
    • Use time-aware statistical tools like PERMANOVA for trend analysis