Metagenomics Service for Profiling Microbial Communities (16S rDNA & ITS)

Amplicon deep-sequencing using next generation sequencing (NGS) technologies has become a powerful tool to study the diversity of microbial communities. By sequencing parts of the ribosomal DNA (16S rDNA or ITS) derived from environmental samples NGS can generate unprecedented amount of sequence data permitting rapid and profound analysis of microbial communities. However, the processing and evaluation of NGS sequence data is a challenge due to the large amount of data generated.

Importance of Selecting the Appropriate Barcoding Locus
Metagenomic studies are commonly performed by analyzing amplicons from the 16S rDNA in prokaryotes or the internal transcribed spacer regions (ITS) in fungi (Figure 1). Both loci form a mosaic of highly conserved and hypervariable regions, the latter being used for phylogenetic classifications. It is yet not possible to sequence amplicons spanning the entire 16S rDNA (~1.3 kb) or ITS regions because the read length of current NGS technologies is limited. Which parts of the 16S rDNA or ITS are best amplified for the profiling is still under debate, and varies depending on the study objectives, experimental design and type of sample. In general, if you aim to get a fine-scale taxonomic resolution, your objective should be to cover as many variable regions as possible in your PCR. The challenge of any profiling project is, however, to carefully balance the trade-off between the taxonomic resolution and possible bias from PCR amplification resulting in an under-representation or loss of taxonomic entities [1]. Often only a pilot study will help to find the best primer set for a specific research question.

Figure 1. Overview of the ribosomal gene loci most often used for the analysis of microbial com-munities. 1A. Structure of the 16S rDNA for bacterial species showing the 9 different variable regions (V1-V9) and common regions often used for community analysis. 1B. Structure of the internal transcribed spacer regions (ITS) for fungi showing the two variable regions ITS1 and ITS2. Most often ITS2 is used as for profiling fungal communities. Variable regions are green whereas conserved regions are grey.

Microsynth Competences and Services

One of Microsynth’s core competence ar­eas is the profiling of microbial commu­nities based on 16S rDNA analysis. Micro­synth is able to offer its customers a full service covering the entire process from experimental design planning, DNA iso­lation, PCR amplification and sequencing up to a detailed bioinformatics analysis of the generated data (Figure 2).

DNA Isolation: Either the customer pro­vides isolated DNA or outsources this critical step to Microsynth (>13 years of experience in DNA/RNA isolation from various and demanding matrices like plant material, food or stool).

PCR Amplification: The PCR amplifica­tion will follow a two-step PCR protocol using a state-of the-art high-fidelity poly­merase. This two-step PCR is applied in order to increase reproducibility and to improve the production of high-quality multiplex amplicon libraries. PCR prod­ucts are purified, quantified with fluores­cence spectroscopy using Picogreen and pooled in equimolar amounts.

NGS Sequencing: Sequencing is done using Illumina MiSeq sequencing tech­nology. MiSeq allows high-throughput profiling at low costs with the advantage of long reads (up to 570 bp).

Bioinformatics Analyses: Depending on customer requirements Microsynth offers either a basic bioinformatics analy­sis package or an advanced bioinformat­ics analysis package. Both packages are based on the Qiime software [2]. The ba­sic package provides taxonomic profiles and a-diversity measurements (i.e. diver­sity of organisms in one sample). The ad­vanced package builds on the basic package and adds b-diversity measure­ments (i.e. diversity of organisms across samples). Guided by experimental pa­rameters (e.g. different sample condi­tions like temperature, pH, etc.), the com­parative analysis of the advanced package allows to test the experimental hypothesis. 

Figure 2. Typical steps in a profiling project. Depending on researcher needs Microsynth can deliver a non-stop service starting from DNA isolation, over PCR amplification and sequencing to data analysis.

Microbial Profiling with Basic Bioinformatics Package

Data Processing: Input for data analysis are demultiplexed, stitched (combining forward and reverse reads), quality fil­tered and Chimera cleaned fasta reads are used for further analysis.

Data Enrichment: The Qiime software in combination with a reference database is used to define operational taxonomic units (OTUs) and to assign each Out the taxonomic identity at different taxonom­ic levels (Figure 3). Output may serve as source for down-stream analysis run by the customer.

a-Diversity: Chao1 and Shannon diver­sity measurements are calculated. Rar­efaction analysis is also performed to es­timate if sampling has been exhaustive (Figure 4).

Figure 3. Example for an interactive HTML taxonomy plot on the Class level. The taxonomy is given for different taxonomic levels (phylum, class, order, family, genus, species).

Figure 4: Rarefaction curves are calculated for each sample based on the OTU computations. Rarefaction curves help to estimate whether bacterial communities were sampled prop¬erly, i.e. enough sequence reads per sample where collected. Rarefaction curves are ex¬pected to reach a plateau if sampling has been exhaustive [3].

Microbial Profiling with Advanced Bioinformatics Package

The Advanced Bioinformatics Analysis in­cludes the results of the basic analysis. In addition, the microbial community struc­tures are compared against the environ­mental parameters provided by the cus­tomer in a phylogenetic context.

The OTU dataset obtained with the basic bioinformatics package serves as input for further analysis.

Data Enrichment: In a first step a phylo­genetic tree is calculated for all OTUs in the dataset (UniFrac)[4]. The phylogenet­ic tree serves as basis for the comparative analysis.

b-Diversity: A qualitative overview of in­tersample diversity is obtained by means of principal coordinate analysis (PCoA, Figure 5A). In addition, significance of pairwise phylogenetic differences among communities is computed and hierarchical UPGMA based clustering is performed (Figure 5B).

Comparative Analysis: A quantitative assessment is performed to test the ex­perimental hypothesis which answers (i) whether the sample categories differ from each other and (ii) whether OTUs are differentially represented based on sample categories (e.g. different sample conditions). 

Figure 5. Example output of the comparative analyses (b-diversity). 5A. 2D plot of a PCoA analysis based on the UniFrac distance matrix. PCoA reduces the dimensionality of a complex dataset and can be used to visualize the relationship of each sample to any other sample in the set.
5B. UPGMA clustering based on the UniFrac distance matrix. UPGMA clustering discovers the hierarchical relationships that underline the samples.

Primer Systems and Their Possible Effects on Profiling Results

Table 1. Summary of most common primer systems for profiling bacterial and fungal communities. Only template-specific sequences and its spanning variable parts are shown. These sequences combined with Illumina adaptor sequences are used in the Illumina Nextera 2-step protocol for the library preparation. EMP = Earth Microbiome Project (; HMP = Human Microbiome Project (http://www.

Figure 6. Analysis of the same environmental sample using different primer systems. In the first case (A) the V123 region was amplified us¬ing primers 9F & 534R (A) and in the second case the V345 region was amplified using primers 341F & 909R (B). This analysis clearly demonstrates that some of the taxa were not amplified using one of the primer sets, and also reveals differences in the amplification efficiency. For example the Archaea were de¬tected using system B but not A. In contrast, the Spirochaetes were detected with primer system A but not B.

[1] Berry et al. (2011) Barcoded primers used in multiplex amplicon pyrosequencing bias amplification, App Env Microb, 77: 7846-7849.
[2] Caporaso et al. (2010) Qiime allows analysis of high-throughput community sequencing data, Nature Methods, 7: 335–336.
[3] Colwell, R.K. & Coddington, J.A. (1994) Estimating terrestrial biodiversity through extrapolation. Philos T Roy Soc B, 345: 101–118.
[4] Lozupone, C. & Knight, R. (2005) UniFrac: a New Phylogenetic Method for Comparing Microbial Communities, Appl Environ Microbiol, 71: 8228-8235.

rechte sp
Contact Form
Interested to discuss your NGS project with an expert or to receive an offer? Then, please fill in our NGS contact form

Related Downloads
AppNote_16S Metagenomics

rechte sp
to the top