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      Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences

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          Abstract

          Profiling phylogenetic marker genes, such as the 16S rRNA gene, is a key tool for studies of microbial communities but does not provide direct evidence of a community’s functional capabilities. Here we describe PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States), a computational approach to predict the functional composition of a metagenome using marker gene data and a database of reference genomes. PICRUSt uses an extended ancestral-state reconstruction algorithm to predict which gene families are present and then combines gene families to estimate the composite metagenome. Using 16S information, PICRUSt recaptures key findings from the Human Microbiome Project and accurately predicts the abundance of gene families in host-associated and environmental communities, with quantifiable uncertainty. Our results demonstrate that phylogeny and function are sufficiently linked that this ‘predictive metagenomic’ approach should provide useful insights into the thousands of uncultivated microbial communities for which only marker gene surveys are currently available.

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          Most cited references22

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          Temporal dynamics of the human vaginal microbiota.

          Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women's health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.
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            Cross-biome metagenomic analyses of soil microbial communities and their functional attributes.

            For centuries ecologists have studied how the diversity and functional traits of plant and animal communities vary across biomes. In contrast, we have only just begun exploring similar questions for soil microbial communities despite soil microbes being the dominant engines of biogeochemical cycles and a major pool of living biomass in terrestrial ecosystems. We used metagenomic sequencing to compare the composition and functional attributes of 16 soil microbial communities collected from cold deserts, hot deserts, forests, grasslands, and tundra. Those communities found in plant-free cold desert soils typically had the lowest levels of functional diversity (diversity of protein-coding gene categories) and the lowest levels of phylogenetic and taxonomic diversity. Across all soils, functional beta diversity was strongly correlated with taxonomic and phylogenetic beta diversity; the desert microbial communities were clearly distinct from the nondesert communities regardless of the metric used. The desert communities had higher relative abundances of genes associated with osmoregulation and dormancy, but lower relative abundances of genes associated with nutrient cycling and the catabolism of plant-derived organic compounds. Antibiotic resistance genes were consistently threefold less abundant in the desert soils than in the nondesert soils, suggesting that abiotic conditions, not competitive interactions, are more important in shaping the desert microbial communities. As the most comprehensive survey of soil taxonomic, phylogenetic, and functional diversity to date, this study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.
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              Count: evolutionary analysis of phylogenetic profiles with parsimony and likelihood.

              Count is a software package for the analysis of numerical profiles on a phylogeny. It is primarily designed to deal with profiles derived from the phyletic distribution of homologous gene families, but is suited to study any other integer-valued evolutionary characters. Count performs ancestral reconstruction, and infers family- and lineage-specific characteristics along the evolutionary tree. It implements popular methods employed in gene content analysis such as Dollo and Wagner parsimony, propensity for gene loss, as well as probabilistic methods involving a phylogenetic birth-and-death model. Count is available as a stand-alone Java application, as well as an application bundle for MacOS X, at the web site http://www.iro.umontreal.ca/ approximately csuros/gene_content/count.html. It can also be launched using Java Webstart from the same site. The software is distributed under a BSD-style license. Source code is available upon request from the author.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                1087-0156
                1546-1696
                12 September 2013
                25 August 2013
                September 2013
                01 March 2014
                : 31
                : 9
                : 10.1038/nbt.2676
                Affiliations
                [1 ]Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada
                [2 ]Department of Microbiology, Oregon State University, Corvallis, OR, USA
                [3 ]Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
                [4 ]Institute for Genomics and Systems Biology, Argonne National Laboratory, Lemont, IL, USA
                [5 ]BioFrontiers Institute, University of Colorado, Boulder, CO, USA
                [6 ]Department of Computer Science, University of Colorado, Boulder, CO, USA
                [7 ]Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, USA
                [8 ]Biotechnology Institute, University of Minnesota, Saint Paul, MN, USA
                [9 ]Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA
                [10 ]Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
                [11 ]Department of Biological Sciences, Florida International University, Miami Beach, FL, USA
                [12 ]Howard Hughes Medical Institute, Boulder, Colorado, USA
                [13 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
                Author notes
                [*]

                These authors contributed equally.

                Article
                NIHMS510850
                10.1038/nbt.2676
                3819121
                23975157
                11a2c065-0453-4d54-a64b-2073b3414acb
                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U01 HG004866 || HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG005969 || HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG004872 || HG
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: P01 DK078669 || DK
                Funded by: Howard Hughes Medical Institute :
                Award ID: || HHMI_
                Categories
                Article

                Biotechnology
                Biotechnology

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