1 Project Overview

1.1 Project Information

1.2 Project Background

Sequencing of the full-length 16S rDNA gene library was completed for 12 samples, with each sample generating 50,000 raw sequences, and the sequencing amount for each sample was no less than 90% of the target data volume; standard amplicon bioinformatics analysis was completed.

1.3 Basic Sample Information

Table 1 Basic Sample Information

Note: sampleId: Sample name submitted; analysisId: Analysis sample name, which will be displayed in subsequent reports; group1 : Group names.

2 Analysis Overview

Amplicon sequencing technology is a widely used method for studying microbial community structure and diversity, which includes steps such as sample preparation, nucleic acid extraction quality control, target fragment amplification, library construction, sequencing, data quality control, and data analysis. These steps help researchers understand the structure, diversity, and function of microbial communities at the molecular level, thereby gaining deeper insights into the role of microorganisms in ecosystems.

3 Technical Route

Oxford Nanopore Technologies has become one of the important methods for studying microbial community structure. The process includes sample preparation, nucleic acid extraction quality control, amplification of target fragments, library construction, sequencing, data quality control, and data analysis. First, sample testing is conducted to ensure sample quality, followed by DNA extraction and nucleic acid testing to obtain high-quality DNA samples. Then, the target fragments are amplified using the corresponding primers; Subsequently, library construction is performed to convert DNA samples into libraries suitable for sequencing. Next, sequencing is carried out to obtain a large amount of sequence data; afterward, data quality control is performed to remove low-quality data and ensure accuracy. Finally, data analysis is conducted, including feature analysis (OTU/ASV analysis), species annotation, diversity analysis, inter-group difference analysis, correlation analysis, and functional prediction analysis.

Figure 1 Technical roadmap

4 Analysis process

Amplicon data analysis includes the following steps: data splitting and quality control, feature analysis (OTU/ASV), species annotation, alpha diversity analysis, beta diversity analysis, inter-group difference analysis, correlation analysis, and functional prediction analysis. Through these steps, researchers can gain insights into the diversity, composition, and functions of microorganisms in the samples, thereby providing important information for research in fields such as ecology, medicine, and agriculture.

1、The original sequences that passed the initial quality screening are divided into libraries and samples based on index and barcode information, with barcode sequences and primers removed;

2、A feature table is obtained through sequence clustering (OTU, ASV) or alignment;

3、The specific composition of each sample (group) at different taxonomic levels is presented to understand the overall profile;

4、The Alpha diversity level of each sample is assessed, and the sequencing depth is reflected through rarefaction curves to determine its adequacy;

5、A distance matrix for each sample is calculated, and various unsupervised ordination and clustering methods are employed to measure the beta diversity differences between different samples (groups);

6、Identify statistically significant biomarkers between different groups through inter-group differential significance analysis;

7、Study the relationship between microbial communities and environmental factors through correlation and association analysis;

8、Perform gene function or phenotype prediction on samples through functional prediction analysis.

Figure 2 Analysis Flowchart

Note: If a project is not suitable for a certain analysis content, that analysis content will not appear in the report.

5 Results Presentation

5.1 Results Preview

12 samples were sequenced, resulting in a total of 359,243 full-length sequences identified through Barcode recognition, with each sample generating at least 29,538 full-length sequences, averaging 29,936 full-length sequences.

5.2 Data quality control

The data from sequencing, after barcode splitting, low-quality filtering, and primer identification and trimming, results in valid sequencing data.

Data statistics are shown in the table below:

Table 2 Sample sequencing data statistics

Note: Sample: Sample name; Raw Reads: Number of raw sequences; Clean Reads: Number of filtered sequences; Mean_len(bp): Average sequence length; Mean_Q: Average quality value.

Result file : 01.qc/data_stat.tsv

5.3 Feature analysis

5.3.1 Feature table construction

The clean sequencing data has been analyzed and aligned to the reference genome. The matrix of aligning results for each sample were summrized in the feature table below.

Table 3 Feature analysis statistics for each sample

Note: sample: sample name; input: number of input sequences; mapped: number of sequences that can be aligned to the reference database; filtered: number of sequences after removing low-abundance features; rared: number of sequences after rarefying to the minimum sequence count.

Result file : 01.qc/feature_stats.tsv

The statistics of the number of features for each sample are shown in the figure below.

Figure 3 Statistics of the number of features for each sample

Note: The x-axis represents samples, and the y-axis represents the number of features.

Result file : 02.asv/barplot/

5.3.2 Analysis of Unique Features

Using Venn diagrams (suitable for 2-5 samples or groups; if the number of samples or groups exceeds 5, randomly select 5 samples or groups for analysis) or petal diagrams (when the number of samples or groups is greater than or equal to 2) can illustrate the number of shared and unique features between different samples or groups, visually representing the overlap of features among different samples or groups. By combining the features with the species they represent, it is possible to identify shared microorganisms in different environments. The results are shown in the figure below.

5.3.2.1 Venn

Figure 4 shows the Venn diagram of unique features

Note: The numbers in the overlapping areas between multiple colors in the figure represent the number of shared features among multiple samples, while the non-overlapping parts represent the number of unique features for each sample.

5.3.2.2 Petal diagrams

Figure 5 shows the Petal diagram of unique features

Note: The number in the central circle represents the count of features common to all samples. The numbers on the outer edges of the petals indicate the count of features unique to each individual sample. The numbers on the inner edges of the petals represent the count of features shared with a subset of other samples.

Result file : 02.asv/venn/

5.3.3 Feature Taxonomic Analysis

For each feature sequence, the corresponding reference database was selected for species annotation. To display the species annotation results, statistics were performed on the feature identification results, as shown in the table below.

Table 4 Number of Categories at Each Level

Note: Sample： Sample name; Kindom, Phylum, Class, Order, Family, Genus, and Species correspond to the number of species classified by boundary, phylum, class, order, family, genus, and species in each sample.

Table 5 Number of Features at Each Level

注：SamplNote: Sample： Sample name; Kindom, Phylum, Class, Order, Family, Genus, and Species correspond to the number of features in each sample that can be classified into different classes, phyla, classes, orders, families, genera, and species.

5.4 Analysis of Species Composition at Various Levels

5.4.1 Bar chart of species composition at different levels

To ensure optimal visualization, only the top 10 taxa in abundance are displayed, while others are merged into “Others” in the figure. “Unclassified” represents species that lack taxonomic annotations.

5.4.1.1 phylum

Figure 6 phylum level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

5.4.1.2 class

Figure 7 class level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

5.4.1.3 order

Figure 8 order level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

5.4.1.4 family

Figure 9 family level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

5.4.1.5 genus

Figure 10 genus level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

5.4.1.6 species

Figure 11 species level species composition bar chart

Note: The horizontal axis represents the sample, and the vertical axis represents the relative abundance. Different colors represent different species.

Result file : 03.taxonomy/barplot/

5.4.2 Species abundance clustering heatmap

A heatmap uses color gradients to represent the magnitude of values in a data matrix and clusters taxa or samples based on abundance similarity. High- and low-abundance taxa are grouped into blocks, reflecting similarities and differences in community composition across samples through color gradients and clustering patterns. In each sample (group), based on the abundance value, 50 top taxa were identified. Then, according to the detailed taxanomy information of each species, heatmap of each taxa (phylum, class, order, family, genus, species) were clustered.