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SMRT-Based 16S rRNA Sequencing


With years of experience in genomics services, we are dedicated to offering accurate and affordable full-length 16S rRNA gene sequencing by utilizing PacBio’s single molecule real-time (SMRT) sequencing technology. SMRT-based 16S rRNA sequencing makes it possible to identify those individual members of complex microbial communities collected from human feces, mouse feces, soil, etc.

Our Advantages:
  • Deliver the highest consensus accuracy with unprecedented read lengths.
  • Streamline your workflow with rapid targeted (16S rRNA gene) approaches.
  • Full-length 16S rRNA gene sequencing containing more information compared with its variable regions.
  • Improve sequence assembly for large and complex genomes.
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Introduction to Our SMRT-based 16S rRNA Sequencing Platform

The 16S rRNA gene, around 1,500 bp in length, is highly conserved between different species of archaea and bacteria. It contains nine hypervariable regions (V1 – V9) that vary dramatically in length, ranging from about 30~100 base pairs. These hypervariable regions can be used to identify and characterize the diverse bacterial and archaeal strains for microbial diversity, function, and evolution analysis. The PacBio SMRT technology is a long-read sequencing technology with an average read length about 10~18 Kb, which can cover all variable areas of the 16S rRNA. Our SMRT-based 16S rRNA sequencing can accurately amplify the full-length 16S rRNA gene with validated universal primer pairs that target the highly conserved regions flanking the 16S rRNA gene. Compared with short-read sequencing data only from variable regions, our full-length 16S rRNA sequencing enables more accurate species/strain classification and identification.

SMRT-based 16S rRNA sequencing enables us to identify or characterize the prokaryotic species and explore microbial diversity in complex microbial communities. It is also committed to advancing microbial taxonomy and phylogenetic studies. In the medical field, it can be used to analyze the global microbial communities in both healthy and diseased groups for biomarker discovery, disease diagnosis, and target discovery. SMRT-based 16S rRNA sequencing has been applied in a range of fields, including tracking of genetic changes, monitoring of food sources, reflection of biomass concentration, marine environmental conservation, surveillance and response to infectious disease outbreaks, and industrial manufacturing, etc.

SMRT-based 16S rRNA sequencing workflow

Bioinformatics Analysis

Our general bioinformatics analysis workflow includes raw data processing, taxonomic assignment, diversity analysis, functional prediction, correlation analysis, and evolutionary analysis, which is flexible to your needs.


Pipeline Content
Raw data processing Filtering and trimming of poor-quality sequence
Taxonomic assignment Operation taxonomic unit (OTU) clustering and filtering, PCA, Venn diagram, rank abundance curve, etc.
Diversity analysis Alpha diversity, beta diversity, meta-analysis
Functional prediction PICRUSt, FAPROTAX, Tax4fun, FunGuild
Correlation analysis CCA analysis, VPA analysis, Network analysis
Evolutionary analysis Construction of phylogenetic trees

Sample Requirement

Sampling kits: We provide a range of microbial sampling kits for clients, including MicroCollect™ oral sample microbial collection products and MicroCollect™ stool sample collection products.

Deliverables: Raw data files in BAM format, demultiplex CCS reads in FASTQ format, quality-control dashboard, statistic data, and your designated bioinformatics analysis report.

References

  1. BM. Jones and AB. Kustka. A quantitative SMRT cell sequencing method for ribosomal amplicons. Journal of Microbiological Methods. 2017; 135:77–84.
  2. H Mori, et al. VITCOMIC: visualization tool for taxonomic compositions of microbial communities based on 16S rRNA gene sequences. BMC Bioinformatics. 2010; 11:332.
  3. JJ. Mosher, et al. Improved performance of the PacBio SMRT technology for 16S rDNA sequencing. Journal of Microbiological Methods. 2014; 104: 59-60.

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