234 Chapter 8 At the same time, an advantage of primer amplification is that it allows the use of low DNA template amounts, with a minimum requirement of approximately 200 ng. Additionally, 16S rRNA amplicon analysis is relatively standardized and user-friendly, combining the DADA2 and Phyloseq packages for accurate error correction, taxonomic assignment, microbial community analysis, and visualization, respectively. Another benefit of 16S rRNA gene amplicon sequencing is its cost-effectiveness compared to shotgun metagenomics, at approximately 45 €/sample/primer pair. Metagenomics allowed to hypothesize over the physiological potential of MAGs (all chapters) and metabolic pathways (Chapter 6) and served as a template for mapping metatranscriptomics reads (Chapter 4 and 6) and metaproteomics protein sequences (Chapter 7). The categorization of MAGs is based on the latest Genome Taxonomy Database (GTDB) classification, which does unfortunately not always aligns with the Silva taxonomy. Primer amplification biases are particularly pronounced in 16S rRNA gene analysis, as primers may not fully capture the diversity of the 16S rRNA gene. However, amplification biases can also occur in shotgun metagenomics, especially when only low amounts of DNA are available. For this PhD thesis, both PCR-free (Chapters 3, 5, and 6) and PCR-dependent (Chapters 2, 4, and 7) shotgun metagenomics sequencing methods were used. The total DNA needed for library construction could be lowered from approximately 1000 ng (PCR-free) to 500 ng (PCR-dependent). Nonetheless, PCR-free methods are preferred, as they represent the least biased approach for analyzing a metagenome. Metagenomics was somewhat more expensive compared to 16S rRNA amplicon sequencing, costing approximately 200 €/sample. Metagenomics presents unique analytical challenges compared to 16S rRNA amplicon sequencing. One key difficulty is the lack of standardization in metagenomic workflows, leading to variability in results and interpretation across different studies. Additionally, metagenomics requires significant computational power due to the complexity and volume of data, making it a more resourceintensive approach than 16S analysis. In this study, the metagenomics pipeline was applied consistently across all chapters, ensuring comparability within this
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