Experimental and analytical tools for studying the human microbiome

• Published in: Nature reviews. Genetics Vol. 13; no. 1; pp. 47 - 58
• Main Authors: Kuczynski, Justin, Lauber, Christian L., Walters, William A., Parfrey, Laura Wegener, Clemente, José C., Gevers, Dirk, Knight, Rob
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.01.2012; Nature Publishing Group
• Subjects: 48; 631/114; 631/208/212/2142; 631/326/2565/2134; Agriculture; Algorithms; Animal Genetics and Genomics; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Cancer Research; Computational biology; Computational Biology - methods; Databases, Nucleic Acid; DNA - genetics; DNA Barcoding, Taxonomic - methods; Gene Function; Genes; Genomes; Genomics - methods; Human Genetics; Humans; Metagenome - genetics; Microbiota; Microbiota (Symbiotic organisms); Microorganisms; Mouth - microbiology; Nucleic Acid Amplification Techniques; review-article; Ribosomal DNA; RNA, Ribosomal, 16S - chemistry; RNA, Ribosomal, 16S - genetics; Skin - microbiology; Software; Taxonomy; United States; United States > US; Colorado
• ISSN: 1471-0056; 1471-0064; 1471-0064
• DOI: 10.1038/nrg3129

Key Points New sequencing technologies and open-source computational tools have enabled rapid progress in research into the human microbiota and the human microbiome. Most recent studies use 16S rDNA gene profiling to assess the organisms that are present in a sample or shotgun metagenomics to get a complete profile of gene content in a given habitat. Bacterial and archaeal communities are currently easy to profile using the 16S rDNA gene sequence: techniques for profiling eukaryotes and viruses are more challenging but are intense areas of interest. Both taxonomic and functional profiling are crucial for obtaining a full picture of the microbiota, although error rates both in sequencing and in functional and taxonomic assignment need to be considered when drawing conclusions. Time series studies are proving to be especially useful for understanding variation in the microbiome, as individuals can vary considerably in their microbiome composition. Thus far, developmental trajectories have only been studied in the gut, although it will be fascinating to extend these studies to other body habitats and to developmental disorders. Clustering sequences into taxonomic groups remains challenging, although the quality of current techniques is sufficient to observe clinically relevant differences among subjects. Public resources for functional annotation of metagenomic data are expanding rapidly; they are providing key enabling technology for large-scale projects, such as the Human Microbiome Project and the Earth Microbiome Project. Studies of the microbiome are rapidly moving from preliminary studies that observe differences among groups to mechanistic and longitudinal studies that allow us to see how and why these differences develop. Personalized culture collections will be especially important in this respect. Studies of the composition, dynamics and function of the human microbiome have taken off in the past two years thanks to the development of new sequencing technologies and advanced algorithms. This article provides a guide to the experimental and analytical best practices in this flourishing field. The human microbiome substantially affects many aspects of human physiology, including metabolism, drug interactions and numerous diseases. This realization, coupled with ever-improving nucleotide sequencing technology, has precipitated the collection of diverse data sets that profile the microbiome. In the past 2 years, studies have begun to include sufficient numbers of subjects to provide the power to associate these microbiome features with clinical states using advanced algorithms, increasing the use of microbiome studies both individually and collectively. Here we discuss tools and strategies for microbiome studies, from primer selection to bioinformatics analysis.