16S rDNA Sequencing

16S rDNA sequencing can be used to detect a species’ classification, abundance, population structure, system evolution, and colony community of bacteria in environmental samples. Synbio Technologies next-generation sequencing technology can easily determine the variable V3 and V4 regions of the 16S rDNA gene. The goal of this sequencing is to detect the sequence variation and abundance of the 16S target region of environmental samples. The sequencing technology is capable of parallel sequencing of multiple samples, making it suitable for the identification of the most bacteria.

Competitive Advantages

  • Accurate Identification: Defining the bacterial flora as “species”, and even low abundance species are included.
  • Advanced Analysis and Algorithm: Combined the latest Greengene database with our self-built database to provide accurate analysis of between-group variance, colony community, and community evolution.
  • Higher Efficiency and Lower Cost: Compared with traditional methods of bacterial colony identification while reducing cost.

16S rDNA Sequencing Work Flow 

16S-rDNA-sequencing

Service Specifications

Sample TypeSequencing Model Sampling Requirements Turnaround Time
Bacteria or DNA, total DNA amount is recommended > 20 ng (no host, impurities or contamination)MiSeq, PE250/300 >0.04M clean dataNo obvious degradation and protein contamination, OD260/280 ≥1.5, OD260/230 ≥1.0, concentration ≥30 ng/μL. DNA sample: soluble in nuclease free ddH2O water in 1.5ml tube, sealed with sealing film and frozen transport.Standard Delivery : 60 business days. For specific projects, please consult.

Data Analysis

Standard AnalysisAdvanced Analysis
  1. Original data processing and statistics
  2. Variable region validation
  3. Operation Taxonomic unit clustering and analysis
  4. Classification and abundance analysis of single species
  5. Analysis of species richness in diverse species
  6. Alpha diversity analysis
  7. Beta diversity analysis
  1. Main factor analysis of the samples difference
  2. Component significant difference analysis
  3. System evolutionary tree construction
  4. The personalized analysis