1 Ravel, J. et al. Vaginal microbiome of reproductive-age women. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4680–4687 (2011).

2 Ley, R. E. et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005).

3 Aas, J., Gessert, C. E. & Bakken, J. S. Recurrent Clostridium difficile colitis: case series involving 18 patients treated with donor stool administered via a nasogastric tube. Clin. Infect. Dis. 36, 580–585 (2003).

4 Sartor, R. B. Microbial influences in inflammatory bowel diseases. Gastroenterology 134, 577–594 (2008).

5 Kinross, J. M., Darzi, A. W. & Nicholson, J. K. Gut microbiome–host interactions in health and disease. Genome Med. 3, 14 (2011).

6 Peterson, J. et al. The NIH Human Microbiome Project. Genome Res. 19, 2317–2323 (2009).

7 Blaser, M. J. Harnessing the power of the human microbiome. Proc. Natl Acad. Sci. USA 107, 6125–6126 (2010).

8 Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010). This is a large-scale study aimed at characterizing the functionality encoded in the gut microbiome. This work defined a minimal set of functions that are present in all of the sampled individuals.

9 Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009). This paper was the first to establish that the microbial communities harboured across the human body are personalized but vary substantially across body sites and over time.

10 Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009).

11 Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011). This is the densest time-series analysis of variation in the human microbiota that has been carried out so far. This study also proved the usefulness of newer DNA sequencers to provide deeper insights into the microbiota by recapturing previous results in variability across body sites and time using a different sequencing technology.

12 Shi, Y., Tyson, G. W. & DeLong, E. F. Metatranscriptomics reveals unique microbial small RNAs in the ocean's water column. Nature 459, 266–269 (2009).

13 Maron, P. A., Ranjard, L., Mougel, C. & Lemanceau, P. Metaproteomics: a new approach for studying functional microbial ecology. Microb. Ecol. 53, 486–493 (2007).

14 Clayton, T. A., Baker, D., Lindon, J. C., Everett, J. R. & Nicholson, J. K. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc. Natl Acad. Sci. USA 106, 14728–14733 (2009). The authors of this paper suggest a link between a person's microbiome and their ability to metabolize a common drug, paracetamol (acetaminophen).

15 DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

16 Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).

17 Cole, J. R. et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).

18 Bellemain, E. et al. ITS as an environmental DNA barcode for fungi: an in silico approach reveals potential PCR biases. BMC Microbiol. 10, 189 (2010).

19 Hayashi, H., Sakamoto, M. & Benno, Y. Evaluation of three different forward primers by terminal restriction fragment length polymorphism analysis for determination of fecal Bifidobacterium spp. in healthy subjects. Microbiol. Immunol. 48, 1–6 (2004).

20 Bergmann, G. T. et al. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol. Biochem. 43, 1450–1455 (2011).

21 Liu, Z., DeSantis, T. Z., Andersen, G. L. & Knight, R. Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers. Nucleic Acids Res. 36, e120 (2008).

22 Walters, W. A. et al. PrimerProspector: de novo design and taxonomic analysis of barcoded polymerase chain reaction primers. Bioinformatics 27, 1159–1161 (2011).

23 Marchesi, J. R. Prokaryotic and eukaryotic diversity of the human gut. Adv. Appl. Microbiol. 72, 43–62 (2010).

24 Parfrey, L. W., Walters, W. A. & Knight, R. Microbial eukaryotes in the human microbiome: ecology, evolution, and future directions. Front. Microbiol. 2, 153 (2011).

25 Ott, S. J. et al. Fungi and inflammatory bowel diseases: alterations of composition and diversity. Scand. J. Gastroenterol. 43, 831–841 (2008).

26 Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, e1000713 (2010).

27 Vestheim, H. & Jarman, S. N. Blocking primers to enhance PCR amplification of rare sequences in mixed samples — a case study on prey DNA in Antarctic krill stomachs. Front. Zool. 5, 12 (2008).

28 Haynes, M. & Rohwer, F. in Metagenomics of the Human Body (ed. Nelson, K. E.) 63–77 (Springer, New York, 2011).

29 Virgin, H. W., Wherry, E. J. & Ahmed, R. Redefining chronic viral infection. Cell 138, 30–50 (2009).

30 Breitbart, M. et al. Viral diversity and dynamics in an infant gut. Res. Microbiol. 159, 367–373 (2008).

31 Reyes, A. et al. Viruses in the faecal microbiota of monozygotic twins and their mothers. Nature 466, 334–338 (2010).

32 Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, e177 (2007).

33 Koenig, J. E. et al. Succession of microbial consortia in the developing infant gut microbiome. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4578–4585 (2011). This paper describes a two-year longitudinal study of the development of the gut microbiota in an infant. This work provides a detailed analysis of the relationship between life events and changes in microbiome composition and function.

34 Oliver, K. M., Degnan, P. H., Hunter, M. S. & Moran, N.A. Bacteriophages encode factors required for protection in a symbiotic mutualism. Science 325, 992–994 (2009).

35 Caporaso, J. G., Knight, R. & Kelley, S. T. Host-associated and free-living phage communities differ profoundly in phylogenetic composition. PLoS ONE 6, e16900 (2011).

36 Willner, D. et al. Metagenomic analysis of respiratory tract DNA viral communities in cystic fibrosis and non-cystic fibrosis individuals. PLoS ONE 4, e7370 (2009).

37 McOrist, A. L., Jackson, M. & Bird, A. R. A comparison of five methods for extraction of bacterial DNA from human faecal samples. J. Microbiol. Methods 50, 131–139 (2002).

38 Wang, R.-F., Beggs, M. L., Erickson, B. D. & Cerniglia, C. E. DNA microarray analysis of predominant human intestinal bacteria in fecal samples. Mol. Cell. probes 18, 223–234 (2004).

39 Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

40 Turnbaugh, P. J., Bäckhed, F., Fulton, L. & Gordon, J. I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).

41 Fierer, N., Hamady, M., Lauber, C. L. & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl Acad. Sci. USA 105, 17994–17999 (2008).

42 Wu, G. D. et al. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 10, 206–206 (2010). This study shows that long-term dietary patterns are associated with particular enterotypes. Bacteroides spp. were associated with a Western-like diet that is rich in proteins and animal fats, whereas Prevotella spp. were linked with high-carbohydrate diets.

43 Lauber, C. L., Zhou, N., Gordon, J. I., Knight, R. & Fierer, N. Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol. Lett. 307, 80–86 (2010).

44 Amann, R. I., Ludwig, W. & Schleifer, K. H. Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Microbiol. Rev. 59, 143–169 (1995).

45 Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol 73, 5261–5267 (2007).

46 Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

47 Liu, Z., Lozupone, C., Hamady, M., Bushman, F. D. & Knight, R. Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids Res. 35, e120 (2007).

48 Zhou, H. W. et al. BIPES, a cost-effective high-throughput method for assessing microbial diversity. ISME J. 5, 741–749 (2011).

49 Hummelen, R. et al. Deep sequencing of the vaginal microbiota of women with HIV. PLoS ONE 5, e12078 (2010).

50 Lazarevic, V. et al. Metagenomic study of the oral microbiota by Illumina high-throughput sequencing. J. Microbiol. Methods 79, 266–271 (2009).

51 Gloor, G. B. et al. Microbiome profiling by illumina sequencing of combinatorial sequence-tagged PCR products. PLoS ONE 5, e15406 (2010).

52 Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4516–4522 (2011).

53 Bartram, A. K., Lynch, M. D., Stearns, J. C., Moreno-Hagelsieb, G. & Neufeld, J. D. Generation of multimillion-sequence 16S rRNA gene libraries from complex microbial communities by assembling paired-end illumina reads. Appl. Environ. Microbiol. 77, 3846–3852 (2011).

54 Claesson, M. J. et al. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res. 38, e200 (2010).

55 Gilbert, J. A. & Dupont, C. L. Microbial metagenomics: beyond the genome. Ann. Rev. Mar. Sci. 3, 347–371 (2011).

56 Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).

57 Rodrigue, S. et al. Unlocking short read sequencing for metagenomics. PLoS ONE 5, e11840 (2010).

58 Goldberg, S. M. D. et al. A Sanger/pyrosequencing hybrid approach for the generation of high-quality draft assemblies of marine microbial genomes. Proc. Natl Acad. Sci. USA 103, 11240–11245 (2006).

59 Gnerre, S. et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl Acad. Sci. USA 108, 1513–1518 (2011).

60 Huse, S. M., Huber, J. A., Morrison, H. G., Sogin, M. L. & Welch, D. M. Accuracy and quality of massively parallel DNA pyrosequencing. Genome Biol. 8, R143 (2007).

61 Kunin, V., Engelbrektson, A., Ochman, H. & Hugenholtz, P. Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ. Microbiol. 12, 118–123 (2010).

62 Schloss, P. D., Gevers, D., Westcott, S. L. Reducing the effects of PCR and sequencing artifacts on 16S rRNA-based studies. PLoS ONE (in the press).

63 Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504 (2011).

64 Quince, C., Lanzen, A., Davenport, R. J. & Turnbaugh, P. J. Removing noise from pyrosequenced amplicons. BMC bioinformatics 12, 38 (2011).

65 Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

66 Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

67 Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010). This paper introduces QIIME, an open-source software tool that performs the complete analysis of microbial communities. Among other functions, QIIME implements quality filtering of the input raw reads, OTU picking, α- and β-diversity estimates and prediction of OTUs that are significantly associated with categories in the data.

68 Reeder, J. & Knight, R. Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions. Nature Methods 7, 668–669 (2010).

69 Rappe, M. S. & Giovannoni, S. J. The uncultured microbial majority. Annu. Rev. Microbiol. 57, 369–394 (2003).

70 Schloss, P. D. The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies. PLoS Comput. Biol. 6, e1000844 (2010).

71 Meyer, F. et al. The metagenomics RAST server — a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinformatics 9, 386 (2008).

72 Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nature Biotech. 29, 415–420 (2011).

73 Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).

74 Gilbert, J. A. et al. The Earth Microbiome Project: meeting report of the “1 EMP meeting on sample selection and acquisition” at Argonne National Laboratory October 6 2010. Stand. Genomic Sci. 3, 249–253 (2010).

75 Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

76 DeSantis, T. Z. Jr et al. NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res. 34, W394–W399 (2006).

77 Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

78 Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005). This study introduces UniFrac, a phylogenetically aware measure of similarity, and one of the most widely used methods to establish the extent to which different microbial communities resemble each other.

79 Faith, D. P. & Baker, A. M. Phylogenetic diversity (PD) and biodiversity conservation: some bioinformatics challenges. Evolutionary Bioinform. Online 2, 121–128 (2006).

80 Morowitz, M. J. et al. Strain-resolved community genomic analysis of gut microbial colonization in a premature infant. Proc. Natl Acad. Sci. USA 108, 1128–1133 (2011).

81 Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).

82 Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011). In this study, faecal microbiomes were found to cluster into three distinct groups ('enterotypes') with minimal overlap.

83 Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011).

84 Brady, A. & Salzberg, S. PhymmBL expanded: confidence scores, custom databases, parallelization and more. Nature Methods 8, 367 (2011).

85 Mitra, S. et al. Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG. BMC Bioinformatics 12, S21 (2011).

86 Sharpton, T. J. et al. PhylOTU: a high-throughput procedure quantifies microbial community diversity and resolves novel taxa from metagenomic data. PLoS Comput. Biol. 7, e1001061 (2011).

87 von Mering, C. et al. Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315, 1126–1130 (2007).

88 Muller, J. et al. eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations. Nucleic Acids Res. 38, D190–D195 (2010).

89 Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M. & Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 38, D355–D360 (2010).

90 Finn, R. D. et al. The Pfam protein families database. Nucleic Acids Res. 36, D281–D288 (2008).

91 Wooley, J. C., Godzik, A. & Friedberg, I. A primer on metagenomics. PLoS Comput. Biol. 6, e1000667 (2010).

92 Glass, E. et al. Meeting report from the Genomic Standards Consortium (GSC) Workshop 10. Stand. Genom. Sci. 3, 225–231 (2010).

93 Arumugam, M., Harrington, E. D., Foerstner, K. U., Raes, J. & Bork, P. SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics 26, 2977–2978 (2010).

94 Sun, S. et al. Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource. Nucleic Acids Res. 39, D546–D551 (2011).

95 Markowitz, V. M. et al. IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res. 36, D534–D538 (2008).

96 Kristiansson, E., Hugenholtz, P. & Dalevi, D. ShotgunFunctionalizeR: an R-package for functional comparison of metagenomes. Bioinformatics 25, 2737–2738 (2009).

97 Liu, B. & Pop, M. MetaPath: identifying differentially abundant metabolic pathways in metagenomic datasets. BMC Proc. 5, S9 (2011).

98 Chen, K. & Pachter, L. Bioinformatics for whole-genome shotgun sequencing of microbial communities. PLoS Comput. Biol. 1, 106–112 (2005).

99 Kuczynski, J. et al. Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nature Methods 7, 813–819 (2010).

100 Quince, C., Curtis, T. P. & Sloan, W. T. The rational exploration of microbial diversity. ISME J. 2, 997–1006 (2008).

101 Mcpeek, M. A. & Mcpeek, M. A. The consequences of changing the top predator in a food web: a comparative experimental approach. Ecol. Monogr. 68, 1–23 (1998).

102 Khoruts, A., Dicksved, J., Jansson, J. K. & Sadowsky, M. J. Changes in the composition of the human fecal microbiome after bacteriotherapy for recurrent Clostridium difficile-associated diarrhea. J. Clin. Gastroenterol. 44, 354–360 (2010).

103 West, T. E. et al. Toll-like receptor 4 region genetic variants are associated with susceptibility to melioidosis. Genes Immun. 2011, 1–9 (2011).

104 Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).

105 Goodman, A. L. et al. Extensive personal human gut microbiota culture collections characterized and manipulated in gnotobiotic mice. Proc. Natl Acad. Sci. USA 108, 6252–6257 (2011). This paper showed that a substantial proportion of an individual's gut microbiota can be recaptured using anaerobic culturing conditions, both in vitro and in vivo.

106 Paulino, L. C., Tseng, C. H., Strober, B. E. & Blaser, M. J. Molecular analysis of fungal microbiota in samples from healthy human skin and psoriatic lesions. J. Clin. Microbiol. 44, 2933–2941 (2006).

107 Gao, Z., Tseng, C. H., Pei, Z. & Blaser, M. J. Molecular analysis of human forearm superficial skin bacterial biota. Proc. Natl Acad. Sci. USA 104, 2927–2932 (2007).

108 Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).

109 Zoetendal, E. G. et al. Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces. Appl. Environ. Microbiol. 68, 3401–3407 (2002).