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Analyzing human as well as animal microbiota composition has gained growing interest because structural components and metabolites of microorganisms fundamentally influence all aspects of host physiology. Originally dominated by culture-dependent methods for exploring these ecosystems, the development of molecular techniques such as high throughput sequencing has dramatically increased our knowledge. Because many studies of the microbiota are based on the bacterial 16S ribosomal RNA (rRNA) gene targets, they can, at least in principle, be compared to determine the role of the microbiome composition for developmental processes, host metabolism, and physiology as well as different diseases. In our review, we will summarize differences and pitfalls in current experimental protocols, including all steps from nucleic acid extraction to bioinformatical analysis which may produce variation that outweighs subtle biological differences. Future developments, such as integration of metabolomic, transcriptomic, and metagenomic data sets and standardization of the procedures, will be discussed.

Analyzing human as well as animal microbiota composition has gained growing interest because structural components and metabolites of microorganisms fundamentally influence all aspects of host physiology. Originally dominated by culture-dependent methods for exploring these ecosystems, the development of molecular techniques such as high throughput sequencing has dramatically increased our knowledge. Because many studies of the microbiota are based on the bacterial 16S ribosomal RNA (rRNA) gene targets, they can, at least in principle, be compared to determine the role of the microbiome composition for developmental processes, host metabolism, and physiology as well as different diseases. In our review, we will summarize differences and pitfalls in current experimental protocols, including all steps from nucleic acid extraction to bioinformatical analysis which may produce variation that outweighs subtle biological differences. Future developments, such as integration of metabolomic, transcriptomic, and metagenomic data sets and standardization of the procedures, will be discussed.