• Home
• Services
• Microbial Diversity Analysis

Request a Quote

Why is Microbial Diversity Analysis Important

The analysis of microbial diversity is of paramount importance across several disciplines, including environmental science, agriculture, and human health. A rich diversity in microbial communities underpins ecosystem stability, facilitates nutrient cycling, and enhances disease resistance. For instance, within soil ecosystems, a varied microbiome can significantly promote plant growth and increase resilience against pathogens. In the context of human health, the diversity of gut microbiota is intricately linked to metabolic health and immune function. Through the assessment of microbial diversity, researchers can identify shifts that may indicate ecological disturbances or potential health issues, thereby enabling timely and effective interventions.

How Do You Analyze Microbial Diversity

The analysis of microbial diversity is crucial for elucidating ecosystem dynamics and the ecological roles of microorganisms. This requires high-resolution sequencing methods targeting specific genetic regions such as 16S rRNA, 18S rRNA, and Internal Transcribed Spacer (ITS). The primary approaches in this field include second-generation sequencing, third-generation sequencing, and metagenomic sequencing.

Second-Generation Sequencing

Second-generation sequencing, often utilizing Illumina technology, targets genetic regions within 16S rRNA, 18S rRNA, and ITS. This high-throughput approach permits the simultaneous detection of dominant, rare, and novel species within a sample. By amplifying and sequencing specific variable regions, researchers gain crucial insights into the composition and relative abundances of microbial communities, thereby elucidating ecological interactions with greater detail.

Third-Generation Sequencing

Conversely, third-generation sequencing platforms like PacBio leverage Single Molecule Real-Time (SMRT) technology to achieve full-length sequencing of target genes. This method emphasizes accuracy and fidelity, providing a more comprehensive overview of microbial community structures. It captures the complete variability of sequences, thus facilitating precise phylogenetic analyses.

Metagenomic Sequencing

Metagenomic sequencing extends beyond targeted sequencing by analyzing entire microbial genomes directly from environmental samples. This technique not only profiles microbial diversity but also uncovers functional traits, inter-species relationships, and environmental interactions. Through metagenomics, researchers can elucidate significant biological insights, thereby enhancing our understanding of complex microbial ecosystems.

Introduction to Our Microbial Diversity Analysis Platform

Microbial Diversity Analysis Workflow

Technical Parameters

• Illumina NovaSeq, Hiseq, MiSeq; PE250
• PacBio SMRT, Library Type: 2 K, Data Volume: Approximately 3,000 to 40,000 CCS reads per sample

Bioinformatics Analysis

Our bioinformatics analysis includes five parts: species annotation, diversity analysis, evolutionary analysis, functional profiling, and correlation analysis. For advanced bioinformatics analysis in detail, please refer to the following table.

Sample Requirement

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

Deliverables

• Raw sequencing data
• Trimmed and stitched sequences (FASTA)
• Data QC report
• Bioinformatics analysis report

Microbial Diversity Analysis FAQ

• 1. What does OTU refer to in microbial diversity?

  • Operational Taxonomic Unit (OTU) refers to a classification operational unit set artificially for a specific taxonomic unit (strain, species, genus, group, etc.) for the convenience of analysis in phylogenetic or population genetic studies. In microbial diversity analysis, OTUs are defined based on different similarity levels among all sequences. Typically, if the similarity between sequences exceeds 97% (species level), they can be classified as a single OTU, with each OTU representing a distinct species.

• 2. What are the differences between the four Beta diversity distance algorithms?

  • The Beta diversity analysis primarily employs four algorithms to calculate the distance between samples: binary Jaccard, Bray-Curtis, unweighted Unifrac (for bacteria), and weighted Unifrac (for bacteria). The key differences among these algorithms are as follows:

    • The unweighted methods consider only the presence or absence of species. If the species types of two populations are identical, the sample distance between these populations is minimal.
    • The weighted methods, in contrast, take into account both the presence of species and their abundance. For instance, if Sample A comprises 3 species of 'a' and 2 species of 'b', and Sample B comprises 2 species of 'a' and 3 species of 'b', the unweighted method would show a distance of 0 between the samples as they have the same species composition. However, the weighted method would reflect a difference since the quantities of species 'a' and 'b' differ between the samples.
    • Methods based on discrete OTUs assume no evolutionary relationship between OTUs, thus treating relationships among OTUs equally.
    • Phylogenetic tree-based methods classify OTUs based on 16S sequence information, acknowledging evolutionary relationships among OTUs and reflecting varying distances accordingly.
• 3. What is the difference between PCA and PCoA?

  • Principal Component Analysis (PCA) is a technique for analyzing and simplifying datasets by decomposing variance to reflect differences among multiple datasets on a two-dimensional coordinate graph. Principal Coordinates Analysis (PCoA) is a dimension reduction and ordination method similar to PCA. The distinction between PCoA and PCA lies in their basis of analysis; PCA performs analysis based on the original species composition matrix using Euclidean distances, focusing solely on species abundance differences. In contrast, PCoA first calculates distances between samples using various distance algorithms and then processes the distance matrix, ensuring that the distances between points in the graph accurately represent the original difference data, thereby achieving quantitative conversion of qualitative data.

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.

Reference

1. Villasante A, Catalán N, Rojas R, et al. Microbiota of the digestive gland of red abalone (Haliotis rufescens) is affected by withering syndrome. Microorganisms. 2020 Sep 13;8(9):1411.