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Amplicon Metagenomics - 16S/ITS
- Explore the taxonomic community composition – bacteria or fungi – of your samples
- Compare taxonomic shifts within an experimental setup
- Optimize your next sampling experiment
Considerations before starting a amplicon metagenomics project:
- Which specific primer pair answers the questions?
- What is the amplicon length?
- Sequencing depth?
- Amount of samples?
- Sample complexity?
- Experimental setup (replicates and conditions)?
Let us guide you – from design to analysis
Example projects using amplicon metagenomics:
- Detect nutrition dependent gut bacterial community shifts
- Investigate the genetic diversity of archaea in deep sea hydrothermal vent environments
- Observe community shifts in a bioreactor setup
- Characterize the fungi spores from air
Applications related to amplicon metagenomics:
- Shotgun metagenomics
- Shotgun metatranscriptomics
For further reading and a detailed technical description, please download our Application 16S/ITS Metagenomics Sequencing (see related downloads).
The analysis of microbial communities by amplicon sequencing of specific marker genes such as the prokaryotic 16S rRNA gene or fungal internal transcribed spacer regions (ITS) addresses the following major questions:
- How diverse is the microbial community including its richness and evenness (α-diversity)? (see Figure 1)
- Which organisms are present in the microbial community? (see Table 1)
- Are the communities in different samples or under certain conditions equal or are there differences and which organisms are differentially abundant (β-diversity)? (see Figure 2 and Table 2)
Our 16S/ITS metagenomics analysis module provides you with the answers to these main questions. Additionally our whole 16S/ITS metagenomics workflow has been validated starting from DNA extraction over library preparation and sequencing to the bioinformatics analysis. If you are interested in our validation process feel free to contact us.
Figure 1: Alpha diversity measures for the analyzed community including observed richness, Chao 1 indices representing the estimated richness and the Shannon diversity indices.
Figure 2: PCA based on UniFrac distances displaying inter-sample similarities. Samples were grouped by two conditions.