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      Biogeography and individuality shape function in the human skin metagenome

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      1 , 1 , 1 , 1 , NISC Comparative Sequencing Program 2 , 3 , , 1 ,
      Nature

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          Summary

          The varied topography of human skin offers a unique opportunity to study how the body’s microenvironments influence the functional and taxonomic composition of microbial communities. Phylogenetic marker gene-based studies have identified many bacteria and fungi that colonize distinct skin niches. Here, metagenomic analyses of diverse body sites in healthy humans demonstrate that local biogeography and strong individuality define the skin microbiome. We developed a relational analysis of bacterial, fungal, and viral communities, which showed not only site-specificity but also individual signatures. We further identified strain-level variation of dominant species as heterogeneous and multiphyletic. Reference-free analyses captured the uncharacterized metagenome through the development of a multi-kingdom gene catalog, which was used to uncover genetic signatures of species lacking reference genomes. This work is foundational for human disease studies investigating inter-kingdom interactions, metabolic changes, and strain tracking and defines the dual influence of biogeography and individuality on microbial composition and function.

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          Most cited references22

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial "pan-genome".

            The development of efficient and inexpensive genome sequencing methods has revolutionized the study of human bacterial pathogens and improved vaccine design. Unfortunately, the sequence of a single genome does not reflect how genetic variability drives pathogenesis within a bacterial species and also limits genome-wide screens for vaccine candidates or for antimicrobial targets. We have generated the genomic sequence of six strains representing the five major disease-causing serotypes of Streptococcus agalactiae, the main cause of neonatal infection in humans. Analysis of these genomes and those available in databases showed that the S. agalactiae species can be described by a pan-genome consisting of a core genome shared by all isolates, accounting for approximately 80% of any single genome, plus a dispensable genome consisting of partially shared and strain-specific genes. Mathematical extrapolation of the data suggests that the gene reservoir available for inclusion in the S. agalactiae pan-genome is vast and that unique genes will continue to be identified even after sequencing hundreds of genomes.
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              Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

              Background In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                23 August 2014
                2 October 2014
                01 April 2015
                : 514
                : 7520
                : 59-64
                Affiliations
                [1 ]Translational and Functional Genomics Branch, National Human Genome Research Institute, NIH, Bethesda, MD
                [2 ]NIH Intramural Sequencing Center, National Human Genome Research Institute, Bethesda, MD
                [3 ]Dermatology Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD
                Author notes
                []Corresponding authors. Dr. Heidi H. Kong, Dermatology Branch, MSC 1908, 10 Center Drive, Bethesda, MD 20892, konghe@ 123456mail.nih.gov , office: 301-402-7452, fax: 301-496-5370, Dr. Julia A. Segre, NHGRI, MSC 4442, 49 Convent Drive, Bethesda, MD 20892, jsegre@ 123456mail.nih.gov , office: 301-402-2314, fax: 301-402-4929
                Article
                NIHMS622920
                10.1038/nature13786
                4185404
                25279917
                bea07094-a26c-4fbb-adac-2ff89034a7b3
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