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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.

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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

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

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

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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 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

1. 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.