Sequencing 16S ribosomal RNA variable regions to study bacterial diversity

16S ribosomal RNA (or 16S rRNA) is a component of the 30S small subunit of prokaryotic ribosomes. The genes coding for it are referred to as 16S rRNA and are used in reconstructing phylogenies.

NGS-based 16S rRNA sequencing is a culture-free technique to infer the entire microbial community within a sample. It is based on:

  • The presence of 16S gene in all bacteria and the universal conservation of some regions. It allows the design of primer pairs able to amplify specific regions from practically all 16S bacterial genes
  • The specificity of some 16S gene variable regions that makes possible the bacteria identification at sufficiently informative taxonomic rank

The usefulness and applicability of 16S studies are impressive but the experimental assay and the bioinformatics analysis are complex and it is important to consider all the aspects to do the integral design of the project to get better results. 

These points are crucial to getting a sufficiently specific identification of bacteria using a 16S sequencing approach:

  • The sequencing coverage. To detect even minority-bacteria is needed to reach a sufficient sequencing resolution or coverage. NGS technologies make this kind of analysis possible as they provide higher throughput at lower cost.
  • The read lengthThe higher the length, the more precise the taxonomic assignment is. If we want to have a taxonomical assignment at the species level we need to find unique species-specific sequences able to unequivocally identify the presence of each species. Larger sequences allow the taxonomic assignment to more specific taxonomical ranks.
  • The error rate of the sequences. The sequence variations in the 16S variable regions are subtle and sequence errors can cause miss-assignments (if expected error probability is not sufficiently considered) or unspecific assignments if the possible errors are correctly taking into account.

Multiplexing the assay

Using the new barcoding methods many samples can be sequenced in the same sequencing run improving the cost-effectiveness and the time to results of 16S sequencing. Multiplexing enables new possibilities of application for studying many different types of samples with also diverse objectives that could not be faced with traditional methods.

STEPS of a 16S metagenomics project

STEPS of a 16S metagenomics project


These are the main steps in a 16S sequencing project for metagenomics samples.

  1. Sampling and DNA Extraction
  2. 16S library preparation:
  • PCR amplification of the 16S rRNA region to be analyzed
  • DNA Barcoding to multiplex the sequencing assay
  1. Sequencing
  2. Bioinformatics analysis
  3. Interpretation of the results

We work to get good results in all these steps of the project. We can help you to do the experimental design of your project or follow the specific previously defined design that you want to do.

We will offer the best options of sequencing considering the goals of your project, the number of samples and your requirements about sequencing coverage, turnaround times and cost.​


Our analysis is exhaustive and read-specific because we do the taxonomic assignment for each sequenced read. We do the assignment based on the similarity with reference 16S sequences. The precision of the assignment depends on:

  • The precision of the assignments that the 16S reference sequences in the database have
  • The similarity with the reference sequence from which we infer the assignment
  • The algorithm of assignment

Our new version of MG7 is more advanced and counts with a new database of 16S reference sequences and a more precise algorithm for taxonomic assignment. See description in MG7: Microbiomes page. 

Era7 MG7 16S microbiome analysis service

Deliverables of MG7 16S analysis Service

We assign each sequence read to a taxon of the taxonomy tree and we provide you with a rich set of deliverables with the results.

To give you different perspectives of the results we provide you with 8 different types of abundance values to evaluate the frequencies and abundance of each type of bacterial and archaeal organism:

  • Lowest Common Ancestor Algorithm (LCA):
    • Direct Assignment, Absolute Values
    • Direct Assignment, Percentage Values
    • Cumulative Assignment, Absolute Values
    • Cumulative Assignment, Percentage Values
  • Best BLAST Hit Algorithm (BBH):
    • Direct Assignment, Absolute Values
    • Direct Assignment, Percentage Values
    • Cumulative Assignment, Absolute Values
    • Cumulative Assignment, Percentage Values

Tables of taxa abundance

  • Abundance tables per sample
  • All the ranks in a complete table
  • Abundances for each rank
  • Abundance tables per each defined group of samples
  • Abundance tables for all the samples together

Analysis of diversity indexes

The Shannon-Wiener and Simpson’s Diversity indexes are calculated for each sample.

Comparison of groups of samples 

We provide statistical analysis for the study of differences between groups of samples. We use for it open tools based on R software from CRAN (The Comprehensive R Archive Network). In each case we apply the most appropriate approaches.  Some example of the type of statistical analysis provided for the comparison of groups of samples:

  • Univariate statistics (fold change analysis, t-tests, volcano plots, one-way ANOVA, correlation analysis)
  • Multivariate statistics (principal component analysis , partial least squares discriminant analysis)
  • Clustering (dendrograms, heatmaps, K-means clustering, self organizing feature maps)
  • Supervised classification (random forests, support vector machine) 


  • Different types of charts with the possibility of providing interactive visualizations (See our reseacresearchct BIOGRAPHIKA  about interactive visualizations)
  • Complete results in compliant formats
  • Technical reports ready to scientific publication


We provide interactive charts (heat-maps, bar charts, pie charts, frequency trees) in which the client can explore interactively the results.

Once the bioinformatics analysis is done there is not anybody but you to interpret the results.  We know that massive results are not easy to understand but graphical interactive visualizations of your data can help you to interpret the results. We provide you with interactive visualizations and tools to explore the results and to define new charts with different parameters selected by you.

We are continuously adding new visualizations for helping you at the interpretation step.

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