We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.

Over the past decade several studies have reported that the gut microbiomes of mammals with similar dietary niches exhibit similar compositional and functional traits. However, these studies rely heavily on samples from captive individuals and often confound host phylogeny, gut morphology, and diet. To more explicitly test the influence of host dietary niche on the mammalian gut microbiome we use 16S rRNA gene amplicon sequencing and shotgun metagenomics to compare the gut microbiota of 18 species of wild non-human primates classified as either folivores or closely related non-folivores, evenly distributed throughout the primate order and representing a range of gut morphological specializations. While folivory results in some convergent microbial traits, collectively we show that the influence of host phylogeny on both gut microbial composition and function is much stronger than that of host dietary niche. This pattern does not result from differences in host geographic location or actual dietary intake at the time of sampling, but instead appears to result from of differences in host physiology. These findings indicate that mammalian gut microbiome plasticity in response to dietary shifts over both the lifespan of an individual host and the evolutionary history of a given host species is constrained by host physiological evolution. Therefore, the gut microbiome cannot be considered separately from host physiology when describing host nutritional strategies and the emergence of host dietary niches.

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In the version of this article initially published, some reference citations were incorrect. The three references to Jupyter Notebooks should have cited Kluyver et al. instead of Gonzalez et al. The reference to Qiita should have cited Gonzalez et al. instead of Schloss et al. The reference to mothur should have cited Schloss et al. instead of McMurdie & Holmes. The reference to phyloseq should have cited McMurdie & Holmes instead of Huber et al. The reference to Bioconductor should have cited Huber et al. instead of Franzosa et al. And the reference to the biobakery suite should have cited Franzosa et al. instead of Kluyver et al. The errors have been corrected in the HTML and PDF versions of the article.

Prochlorococcus is the numerically dominant phototroph in the oligotrophic subtropical ocean and carries out a significant fraction of marine primary productivity. Although field studies have provided evidence for nitrate uptake by Prochlorococcus, little is known about this trait because axenic cultures capable of growth on nitrate have not been available. Additionally, all previously sequenced genomes lacked the genes necessary for nitrate assimilation. Here we introduce three Prochlorococcus strains capable of growth on nitrate and analyze their physiology and genome architecture. We show that the growth of high-light (HL) adapted strains on nitrate is B17% slower than their growth on ammonium. By analyzing 41 Prochlorococcus genomes, we find that genes for nitrate assimilation have been gained multiple times during the evolution of this group, and can be found in at least three lineages. In low-light adapted strains, nitrate assimilation genes are located in the same genomic context as in marine Synechococcus. These genes are located elsewhere in HL adapted strains and may often exist as a stable genetic acquisition as suggested by the striking degree of similarity in the order, phylogeny and location of these genes in one HL adapted strain and a consensus assembly of environmental Prochlorococcus metagenome sequences. In another HL adapted strain, nitrate utilization genes may have been independently acquired as indicated by adjacent phage mobility elements; these genes are also duplicated with each copy detected in separate genomic islands. These results provide direct evidence for nitrate utilization by Prochlorococcus and illuminate the complex evolutionary history of this trait.

We present QIIME 2, an opensource microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.27295v2 | CC BY 4.0 Open Access | rec:

word count: 203 68 3 Main text word count: 3280 69 70 Abstract: Although much work has linked the human microbiome to specific phenotypes and 71 lifestyle variables, data from different projects have been challenging to integrate and the extent 72 of microbial and molecular diversity in human stool remains unknown. Using standardized 73 protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-74 scientists, together with an open research network, we compare human microbiome specimens 75 primarily from the USA, UK, and Australia to one another and to environmental samples. Our 76 results show an unexpected range of beta-diversity in human stool microbiomes as compared to 77 environmental samples, demonstrate the utility of procedures for removing the effects of 78 overgrowth during room-temperature shipping for revealing phenotype correlations, uncover 79 new molecules and kinds of molecular communities in the human stool metabolome, and 80 examine emergent associations among the microbiome, metabolome, and the diversity of plants 81 that are consumed (rather than relying on reductive categorical variables such as veganism, 82 which have little or no explanatory power). We also demonstrate the utility of the living data 83 resource and cross-cohort comparison to confirm existing associations between the microbiome 84 and psychiatric illness, and to reveal the extent of microbiome change within one individual 85 during surgery, providing a paradigm for open microbiome research and education. 86 87Importance: We show that a citizen-science, self-selected cohort shipping samples through the 88 mail at room temperature recaptures many known microbiome results from clinically collected 89 cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the 90 population data, showing that the extent of microbiome change after events such as surgery can 91 4 exceed differences between distinct environmental biomes, and the effect of diverse plants in the 92 diet which we confirm with untargeted metabolomics on hundreds of samples. 93 94 Introduction 95The human microbiome plays a fundamental role in human health and disease. While 96 many studies link microbiome composition to phenotypes, we lack understanding of the 97 boundaries of bacterial diversity within the human population, and the relative importance of 98 lifestyle, health conditions, and diet, to underpin precision medicine or to educate the broader 99 community about this key aspect of human health. 100 We launched the American Gut Project (AGP; http://americangut.org) in November of 101 2012 as a collaboration between the Earth Microbiome Project (EMP) (1) and the Human Food 102 Project (HFP; http://humanfoodproject.com/) to discover the kinds of microbes and microbiomes 103 "in the wild" via a self-selected citizen-scientist cohort. The EMP is tasked with characterizing 104 the global microbial taxonomic and functional diversity, and the HFP is focused on 105 understanding microbial diversity a...

We present QIIME 2, an open-source microbiome data science platform accessible to users spanning the microbiome research ecosystem, from scientists and engineers to clinicians and policy makers. QIIME 2 provides new features that will drive the next generation of microbiome research. These include interactive spatial and temporal analysis and visualization tools, support for metabolomics and shotgun metagenomics analysis, and automated data provenance tracking to ensure reproducible, transparent microbiome data science.