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      UniFrac – An online tool for comparing microbial community diversity in a phylogenetic context

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      1 , 2 , 3 ,
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Moving beyond pairwise significance tests to compare many microbial communities simultaneously is critical for understanding large-scale trends in microbial ecology and community assembly. Techniques that allow microbial communities to be compared in a phylogenetic context are rapidly gaining acceptance, but the widespread application of these techniques has been hindered by the difficulty of performing the analyses.

          Results

          We introduce UniFrac, a web application available at http://bmf.colorado.edu/unifrac, that allows several phylogenetic tests for differences among communities to be easily applied and interpreted. We demonstrate the use of UniFrac to cluster multiple environments, and to test which environments are significantly different. We show that analysis of previously published sequences from the Columbia river, its estuary, and the adjacent coastal ocean using the UniFrac interface provided insights that were not apparent from the initial data analysis, which used other commonly employed techniques to compare the communities.

          Conclusion

          UniFrac provides easy access to powerful multivariate techniques for comparing microbial communities in a phylogenetic context. We thus expect that it will provide a completely new picture of many microbial interactions and processes in both environmental and medical contexts.

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          Most cited references24

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          Measuring Biological Diversity

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            NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes

            Microbiologists conducting surveys of bacterial and archaeal diversity often require comparative alignments of thousands of 16S rRNA genes collected from a sample. The computational resources and bioinformatics expertise required to construct such an alignment has inhibited high-throughput analysis. It was hypothesized that an online tool could be developed to efficiently align thousands of 16S rRNA genes via the NAST (Nearest Alignment Space Termination) algorithm for creating multiple sequence alignments (MSA). The tool was implemented with a web-interface at . Each user-submitted sequence is compared with Greengenes' ‘Core Set’, comprising ∼10 000 aligned non-chimeric sequences representative of the currently recognized diversity among bacteria and archaea. User sequences are oriented and paired with their closest match in the Core Set to serve as a template for inserting gap characters. Non-16S data (sequence from vector or surrounding genomic regions) are conveniently removed in the returned alignment. From the resulting MSA, distance matrices can be calculated for diversity estimates and organisms can be classified by taxonomy. The ability to align and categorize large sequence sets using a simple interface has enabled researchers with various experience levels to obtain bacterial and archaeal community profiles.
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              Seasonal dynamics of previously unknown fungal lineages in tundra soils.

              The finding that microbial communities are active under snow has changed the estimated global rates of biogeochemical processes beneath seasonal snow packs. We used microbiological and molecular techniques to elucidate the phylogenetic composition of undersnow microbial communities in Colorado, the United States. Here, we show that tundra soil microbial biomass reaches its annual peak under snow, and that fungi account for most of the biomass. Phylogenetic analysis of tundra soil fungi revealed a high diversity of fungi and three novel clades that constitute major new groups of fungi (divergent at the subphylum or class level). An abundance of previously unknown fungi that are active beneath the snow substantially broadens our understanding of both the diversity and biogeochemical functioning of fungi in cold environments.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                2006
                7 August 2006
                : 7
                : 371
                Affiliations
                [1 ]Department of Molecular, Cellular, and Developmental Biology, University of Colorado, Boulder, CO 80309, USA
                [2 ]Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
                [3 ]Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO 80309, USA
                Article
                1471-2105-7-371
                10.1186/1471-2105-7-371
                1564154
                16893466
                97aba5f2-f43c-4af9-bc23-ea837d58668e
                Copyright © 2006 Lozupone et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 May 2006
                : 7 August 2006
                Categories
                Software

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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