What types of samples can be used for 16S rRNA sequencing?

Bacterial 16S rRNA sequencing can be performed on a variety of sample types, including environmental samples (soil, water), clinical samples (tissues, fluids), and industrial samples. CD Genomics offers customized solutions for different sample types to ensure accurate and relevant results.

Can you handle large-scale projects?

Yes, CD Genomics is equipped to handle large-scale projects, including those requiring deep coverage and high-resolution data. Our advanced sequencing technologies and expert team ensure that even extensive projects are completed efficiently and with high quality.

How many samples per group are sufficient for analysis?

Determining the number of samples per group is crucial for sequencing experiment design and subsequent data analysis. Replicates play a vital role in ensuring robust and reliable results. Here are several key reasons for including multiple sample replicates: 1. Eliminate Intra-Group Error and Detect Outliers: The presence of outliers can significantly skew sequencing results. Through subsequent data analysis, abnormal samples can be identified and excluded to maintain accuracy. 2. Enhance Result Reliability: Increasing the number of samples helps to mitigate background variability, thereby boosting the confidence in the results. General Recommendations For model organisms with minimal individual variability (such as rats, mice, chickens), it's advisable to have at least 5 biological replicates per group, with a recommendation of 10 biological replicates to ensure robust results. Specific Cases For human gut fecal samples, due to significant individual differences influenced by factors such as environment, diet, genetics, health status, age, and sex, a larger sampling size is recommended. Each group should include a minimum of 30 samples to ensure statistical power and persuasive results. Even when pooling samples for library construction and sequencing, multiple replicates are essential. Practical Advice It's generally advisable to include more biological replicates. In cases of high individual variability or unexpected sample processing issues, having additional samples allows for the exclusion of problematic ones without compromising overall sample size. If the need arises to resubmit samples, it can delay the process, and inconsistencies between different batches may affect reproducibility.

What to do when results from different statistical methods disagree?

Discrepancies among results from various statistical methods can arise due to differences in the analytical approaches used by different software. For instance, LEfSe employs rank-sum tests and linear discriminant analysis (LDA), whereas other tools like Metastats might use different statistical tests. If Metastats indicates a significant difference in the abundance of a particular microorganism, while LEfSe does not, researchers should consider their study's specific context to determine the most appropriate interpretation of results. Despite the variances, all these statistical methods provide credible insights, and selecting the result that best aligns with the research objectives is crucial.

Inquiry

Request a Quote

Home
Services
Microbial Diversity Analysis
Bacterial 16S rRNA Sequencing

Bacterial 16S rRNA Sequencing

Service Overview

Our Bacterial 16S rRNA Sequencing service provides in-depth analysis of bacterial communities using the 16S ribosomal RNA gene. This universal gene, with its conserved and variable regions, allows precise identification and classification of bacterial species. We utilize advanced next-generation and third-generation sequencing technologies, including Illumina and PacBio platforms, to deliver comprehensive microbial diversity and community structure insights.

Our Advantages:

Expertise in dealing with various types of environmental and human microbiome samples.
Accredited lab with the highest standard of proficiency and quality assessment at every step of the process.
Fast turnaround time and highly experienced staff.
Next-generation sequencing (NGS) and third-generation sequencing platforms.

Request a Quote

What is 16S rRNA Sequencing for Bacteria

Bacterial 16S rRNA sequencing stands as a cornerstone in microbial ecology, facilitating the intricate examination of bacterial communities via their ribosomal RNA genes. The 16S ribosomal RNA (rRNA) gene, a pivotal part of the bacterial ribosome integral to protein synthesis, spans roughly 1540 base pairs (bp), containing both highly conserved and variable regions. These variable regions generate distinct fingerprints for different bacterial species, enabling accurate taxonomic identification and phylogenetic analysis.

Given its universal presence across bacterial species, the 16S rRNA gene serves as an optimal target for exploring microbial diversity and community composition. The sequencing process entails extracting DNA from environmental or clinical samples, amplifying the 16S rRNA gene with specific primers, and sequencing the amplified segments using high-throughput technologies. The sequences obtained are then aligned against reference databases to accurately identify and classify bacterial species.

Applications of Bacterial 16S rRNA Sequencing

Microbial Diversity and Community Analysis
Environmental Monitoring and Conservation
Clinical Diagnostics and Disease Research
Industrial and Agricultural Applications

Service Specifications

Our Bacterial 16S rRNA Sequencing Service

16S rRNA sequencing typically involves the selective PCR amplification of the 16S rRNA gene using specific primers, followed by second-generation or third-generation sequencing. NGS can be performed using platforms such as Illumina MiSeq/HiSeq, Roche 454, or Ion Torrent, while third-generation sequencing leverages PacBio SMRT or Nanopore technologies. Utilizing robust bioinformatics tools, we can conduct essential sequence processing, diversity analysis, taxonomic assignment, and community comparisons. Our powerful 16S rRNA analysis pipeline aligns sequences for phylogenetic assignment using three popular databases: Silva, Green Genes, and the Ribosomal Database Project (RDP).

Bacterial 16S rRNA sequencing aids in determining microbial diversity, abundance, and potential microbial functions. It plays a critical role in identifying bacterial communities across global health and disease populations, contributing significantly to disease diagnosis, biomarker discovery, and target identification. Beyond medical applications, 16S rRNA sequencing is also employed in environmental conservation, agricultural production, oil exploration, and industrial manufacturing.

Bacterial 16S rRNA Sequencing Workflow

The Workflow of Bacterial 16S rRNA Sequencing.

1. Initial Consultation: Begin with an essential consultation with our experts to discuss your samples and project objectives, ensuring well-defined goals and clear expectations.

2. Sample Submission: We provide detailed guidelines on sample handling, processing, and transportation, tailored to the specific sample type and requirements.

3. DNA Quality Control (QC): Prior to library preparation, we rigorously assess the DNA for quantity, quality, and purity.

4. Library Preparation: We construct diverse 16S rRNA libraries conducive to various sequencing platforms, tailored to your project needs.

5. Sequencing: Our comprehensive 16S rRNA sequencing services utilize cutting-edge second- and third-generation sequencing technologies.

6. Bioinformatics Analysis: We offer thorough bioinformatics analysis, encompassing raw data processing, diversity analysis, taxonomic assignments, and community comparisons.

7. Final Report: Delivering a comprehensive and timely final report, we ensure all findings are meticulously documented and accessible.

Technical Parameters:

Sequencing Platform	Sequencing Strategy	Data Volume
HiSeq/MiSeq	PE250	Depending on the specific project requirements, not less than the contracted data volume

Bioinformatics Analysis

Our bioinformatics analysis pipeline is flexible to your needs.

Pipeline	Analysis Content
Basic data processing	Filtering and trimming of poor-quality sequence
Taxonomic assignment	Operation Taxonomic Unit (OTU) clustering and species annotation
Diversity analysis	Classification and abundance analysis of single species
Community comparisons	Measure the difference in bacterial community composition among different samples, and conduct network analysis and correlation analysis
Evolutionary analysis	Construction of phylogenetic tree

Note: The above content includes only a portion of the bioinformatics analysis. For more information or to customize the analysis, please contact us directly.

The Bioinformatics Analysis of Bacterial 16S rRNA Sequencing.

Sample Requirement

Sample Type	Quantity	Concentration	OD260/OD280
Genomic DNA	≥100 ng	≥1 ng/μl	1. 8 - 2. 0

Note:

1. Ensure the DNA is purified, not degraded.

2. Transport nucleic acid samples with sufficient ice packs or dry ice.

3. 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.

4. 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
Custom bioinformatics analysis report

Demo

Partial results of our Bacterial 16S rRNA Sequencing service are shown below:

The Bacterial 16S rRNA Sequencing Results Display.

FAQs

Bacterial 16S rRNA Sequencing FAQ

1. What types of samples can be used for 16S rRNA sequencing?
- Bacterial 16S rRNA sequencing can be performed on a variety of sample types, including environmental samples (soil, water), clinical samples (tissues, fluids), and industrial samples. CD Genomics offers customized solutions for different sample types to ensure accurate and relevant results.
2. Can you handle large-scale projects?
- Yes, CD Genomics is equipped to handle large-scale projects, including those requiring deep coverage and high-resolution data. Our advanced sequencing technologies and expert team ensure that even extensive projects are completed efficiently and with high quality.
3. How many samples per group are sufficient for analysis?
- Determining the number of samples per group is crucial for sequencing experiment design and subsequent data analysis. Replicates play a vital role in ensuring robust and reliable results. Here are several key reasons for including multiple sample replicates:
  
  1. Eliminate Intra-Group Error and Detect Outliers: The presence of outliers can significantly skew sequencing results. Through subsequent data analysis, abnormal samples can be identified and excluded to maintain accuracy.
  
  2. Enhance Result Reliability: Increasing the number of samples helps to mitigate background variability, thereby boosting the confidence in the results.
  
  General Recommendations
  For model organisms with minimal individual variability (such as rats, mice, chickens), it's advisable to have at least 5 biological replicates per group, with a recommendation of 10 biological replicates to ensure robust results.
  
  Specific Cases
  For human gut fecal samples, due to significant individual differences influenced by factors such as environment, diet, genetics, health status, age, and sex, a larger sampling size is recommended. Each group should include a minimum of 30 samples to ensure statistical power and persuasive results. Even when pooling samples for library construction and sequencing, multiple replicates are essential.
  
  Practical Advice
  It's generally advisable to include more biological replicates. In cases of high individual variability or unexpected sample processing issues, having additional samples allows for the exclusion of problematic ones without compromising overall sample size. If the need arises to resubmit samples, it can delay the process, and inconsistencies between different batches may affect reproducibility.
4. What to do when results from different statistical methods disagree?
- Discrepancies among results from various statistical methods can arise due to differences in the analytical approaches used by different software. For instance, LEfSe employs rank-sum tests and linear discriminant analysis (LDA), whereas other tools like Metastats might use different statistical tests. If Metastats indicates a significant difference in the abundance of a particular microorganism, while LEfSe does not, researchers should consider their study's specific context to determine the most appropriate interpretation of results. Despite the variances, all these statistical methods provide credible insights, and selecting the result that best aligns with the research objectives is crucial.

Customer Case

Elucidating the effects of organic vs. conventional cropping practice and rhizobia inoculation on rhizosphere microbial diversity and yield of peanut
Journal: Environmental Microbiome
Impact factor: 6.2
Published: 18 July 2023

Find out more

Backgrounds

Nitrogen is vital for plant growth but frequently limits agricultural productivity. Sustainable practices like organic farming and biological nitrogen fixation (BNF) offer alternatives to synthetic fertilizers. Peanuts, which rely partially on Bradyrhizobia for nitrogen, often need additional fertilization. This study investigates the impact of organic versus inorganic farming and various rhizobia inoculums on peanut yield and soil microbiomes, including the use of 16S rRNA sequencing to analyze microbial diversity. A high-efficiency Bradyrhizobium strain (Lb8) is employed, and different peanut cultivars are examined to enhance yield and sustainability.

Methods

Sample preparation:

Peanut
Rhizosphere soil
DNA extraction

Method:

16S rRNA sequencing
Illumina NovaSeq

Data Analysis

Alpha diversity analysis
Beta diversity analysis

Results

Alpha Diversity: Organic fields had higher microbial diversity and evenness for certain peanut genotypes compared to inorganic fields. Specific indices (Chao1, Shannon, Simpson) showed greater diversity in organic fields.

Fig 1. Alpha diversity indices (chao1, Simpson, and Shannon) and Pielou's evenness for different genotypes in the organic and inorganic plots for different inoculums.

Beta Diversity: Bacterial communities were more uniform in organic fields, while inorganic fields showed more variability influenced by inoculum sources.

Fig 2. PCoA plot of bacterial community generated by using the weighted UniFrac as distance.
Community Structure: Organic soils had higher Proteobacteria and lower Firmicutes. Inorganic fields had different microbial profiles based on peanut genotypes, affecting overall community structure.

Fig 3. Phylum-level composition of peanut rhizosphere. (Paudel et al., 2023) Fig 3. Phylum composition of peanut rhizosphere.

Conclusion

In this study, the authors compared organic and inorganic cultivation practices for peanuts, finding that organic methods support greater microbial diversity and evenness but can result in lower yields for certain genotypes. While organic cultivation enhances microbial community richness and efficiency, it may also lead to reduced yields compared to inorganic practices, particularly in the presence of certain genotypes and varying nutrient levels.

Reference

Paudel D, Wang L, Poudel R, et al. Elucidating the effects of organic vs. conventional cropping practice and rhizobia inoculation on rhizosphere microbial diversity and yield of peanut. Environmental Microbiome. 2023, 18(1):60.