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Product Brief

16S rDNA is a DNA sequence encoding the rRNA of the small ribosomal subunit in prokaryotes, which bears many important biological functions. It has 9 hypervariable regions (V1-V9) and 10 conserved regions. The conserved regions reflect the phylogenetic relationships among species, while the hypervariable regions reflect the specificity among species. So through analysis The taxonomic characteristics of bacteria can be obtained from the sequence of 16S rDNA variable region. Combined with high-throughput sequencing, we can study microbial composition and community function in environmental or clinical samples.

Bacterial 16S rDNA sequencing workflow

Kong HH., Trends in Molecular Medicine. 2011

Our advantages


One Added Reads number: the number of Reads tested is 100000. Compared with Miseq, it can detect more sequence sequences and rare microorganisms.

2. higher data quality New generation HiQ Better than Q30 level of perfect data quality has greater reaction system, longer base reading length and high fidelity base recognition ability.

3. higher detection sensitivity: uniform coverage and high detection sensitivity. And higher detection fluxes, compared to PGM, more microbial species were identified accurately.

4. a wider coverage: it can cover a wider range of variable areas, greatly improving the accuracy and sensitivity of subsequent annotation of species.



Sample requirements

DNA sample

One concentration It is more than 50ng/ L and its volume is more than 60 L.

2. OD260/280 is between 1.8-2.0.

Three Electrophoretic detection was not obvious. The genome of RNA is clear and complete without degradation.

Four Please mark the sample number clearly when sending the sample. Parafilm membrane seal;

5. avoid repeated freezing and thawing during preservation.

6. dry ice transportation for sample delivery.

Environmental samples (such as soil, plant roots, seawater, intestines, etc.) should be characterized in detail, such as soil: greater hardness, closer to stone, from riverside, soft and so on.

Tissue sample extraction Should DNA be identified as endophytic or exophytic?


Experiment flow



1. customer samples: soil, plant roots, seawater, air, faeces, saliva, etc.

2. DNA extraction and quality control: QIAamp DNA Stool Mini Kit kit;

Three target area PCR: Primers are 16s-V3 region, Barcode specific primers (338F-518R);

4. Library Construction: life's Ion Plus Fragment Library Kit library kit;

5. on board sequencing: strong ice recommended life Ion S5 sequencer, data volume: 30000 reads.



Data analysis process

Result example

1. Species identification
After obtaining the core sequence of each OTU (operational taxonomic units) and Silva Library ( Http://www.arb-silva.de/ The aligned (15S, SSU) ribosomal sequences were compared with the database, and the species identification and abundance of each OTU were obtained.

Species richness in Krona samples

Zamanzadeh M et al., Water Reseqrch. 2016

Note: the graph shows two samples. The abundance of each taxon in MD and MD+R, the number before each taxa, represents the reads of the taxa. Total number Percentage of reads numbers



2. Microbial community structure analysis
The OTU sequences were compared with databases such as GreenGenes, Silva and RDP, and the number of reads was analyzed, and the variation of microbial species and abundance in different samples were analyzed.

Microbial community structure analysis

Garcia-Mena et al., Microb Ecol, 2016

Note: the map shows a normal diet. Differences in microbial community structure of alimentary tract under the condition of SCD and high fat feeding (HFD)


3. Dilution curve analysis
A certain number of reads was randomly selected from the sample, and the number of species represented by these reads was counted. Comparing the richness of species in different data of sequencing data, it can also be used to illustrate whether the quantity of sequencing data is reasonable.

Dilution curve analysis results

Note: When the curve tends to be flat, it shows that the amount of sequencing data is reasonable, and more data will yield only a small amount of new data. OTU, on the contrary, indicates that further sequencing may result in more new OUT.



4. Alpha diversity analysis
Alpha diversity (sample diversity) refers to the diversity of a specific region or ecosystem. The commonly used metrics include Chao1 richness estimation (Chao1 richness estimator), Shannon Wiener diversity index (Shannon-wiener diversity index), Simpson diversity index (Simpson diversity index), etc. The abundance indices of bacteria were Chao1 and ACE, and Shannon and Simpson were the indicators of microbial diversity.

Alpha diversity

Note: the map shows different samples. Chao1 richness estimation


5. Beta diversity analysis
Beta diversity (sample difference analysis) calculation mainly reflects the degree of difference among different samples, and the measurement of species composition changes at time and space scales is an important part of biodiversity, and is closely related to many problems of ecology and evolutionary biology. Beta diversity calculation can be divided into quantitative index and qualitative index according to whether the relative abundance of OTU can be considered. All the differences in diversity among samples were all beta diversity analysis.

Beta diversity analysis results

Micha Kolasa et al., Microbial Ecology. 2019

Note: the graph shows the diversity of samples.



6, LefSe analysis
First, a nonparametric factor Kruskal-Wallis rank sum test was used to detect the species with different abundance among different groups (a). Then, a group of Wilcoxon rank sum tests were used to analyze the difference between the groups with significant difference in the previous step. Finally, linear discriminant analysis (LDA) was used to reduce the data and evaluate the influence of the species with significant difference. LDA score).

LefSe analysis

Lelouvier et al., Hepatology, 2016

Note: the map shows the difference of microbial populations in two groups of stool samples.


7, PICRUSt analysis
Using STAMP software, we can directly use the biom files from QIIME and PICRUSt's KEGG and Ko files to analyze and predict the number and composition of fine functional genes, and analyze the differences between samples and groups, and give statistical significance and confidence interval analysis results. This analysis further extends our differences in the sample community to the level of each gene, which is the combination of microbes and functions.

PICRUSt analysis results

Note: the figure shows differences between groups. KO function entry



8. Species enrichment analysis
The CCREPE algorithm is used to analyze the total number of the original 16S rDNA sequencing data. Then rank correlation analysis and statistical tests are performed to calculate the correlation among the species. After that, the data with the highest correlation are selected according to the absolute value of SimScore in all species, and the co expression analysis network diagram is drawn based on Cytoscap.

Species co enrichment analysis

Odamaki et al., BMC Microbiology, 2016

Note: the graph shows. The Intergenera relationships of 9 co enrichment groups. Different color dots represent a co enriched population. The dot size indicates the abundance of the genus.



Advanced data analysis

Examples of literature

[1] Hussain M, Bonilla-Rosso G, Kwong Chung CKC, et al. High al., 2019 M 13. (19)


[2] Kolasa M, cibior R, Mazur MA, et al. How Hosts Hosts, M, M, 2019


[3] Xia X, Zhang P, He L, et al., Effects of Effects, 2019, 26.


[4] Hill C J, Lynch D B, Murphy K, et al. et 24 C, 2017, 5 (1): 4.


[5] Zamanzadeh M, Hagen L H, Svensson K, et al. Anaerobic, et 2016, 96.


[6] Odamaki T, Kato K, Sugahara H, et al. Age-related changes, al. 2016, 16 (1): 90.


[7] Garc a-Mena J, Murugesan S, P e rez-Mu oz oz AA, et AA, a-Mena 2016, 72 (1):


[8] Lelouvier B, Servant F, Sandrine Pa et SS e, et al. Changes al., B 2016, 64 (6):

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