7,566 research outputs found

    Joint Analysis of Multiple Metagenomic Samples

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    The availability of metagenomic sequencing data, generated by sequencing DNA pooled from multiple microbes living jointly, has increased sharply in the last few years with developments in sequencing technology. Characterizing the contents of metagenomic samples is a challenging task, which has been extensively attempted by both supervised and unsupervised techniques, each with its own limitations. Common to practically all the methods is the processing of single samples only; when multiple samples are sequenced, each is analyzed separately and the results are combined. In this paper we propose to perform a combined analysis of a set of samples in order to obtain a better characterization of each of the samples, and provide two applications of this principle. First, we use an unsupervised probabilistic mixture model to infer hidden components shared across metagenomic samples. We incorporate the model in a novel framework for studying association of microbial sequence elements with phenotypes, analogous to the genome-wide association studies performed on human genomes: We demonstrate that stratification may result in false discoveries of such associations, and that the components inferred by the model can be used to correct for this stratification. Second, we propose a novel read clustering (also termed “binning”) algorithm which operates on multiple samples simultaneously, leveraging on the assumption that the different samples contain the same microbial species, possibly in different proportions. We show that integrating information across multiple samples yields more precise binning on each of the samples. Moreover, for both applications we demonstrate that given a fixed depth of coverage, the average per-sample performance generally increases with the number of sequenced samples as long as the per-sample coverage is high enough

    Identification of Carbohydrate Metabolism Genes in the Metagenome of a Marine Biofilm Community Shown to Be Dominated by Gammaproteobacteria and Bacteroidetes

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    Polysaccharides are an important source of organic carbon in the marine environment and degradation of the insoluble and globally abundant cellulose is a major component of the marine carbon cycle. Although a number of species of cultured bacteria are known to degrade crystalline cellulose, little is known of the polysaccharide hydrolases expressed by cellulose-degrading microbial communities, particularly in the marine environment. Next generation 454 Pyrosequencing was applied to analyze the microbial community that colonizes and degrades insoluble polysaccharides in situ in the Irish Sea. The bioinformatics tool MG-RAST was used to examine the randomly sampled data for taxonomic markers and functional genes, and showed that the community was dominated by members of the Gammaproteobacteria and Bacteroidetes. Furthermore, the identification of 211 gene sequences matched to a custom-made database comprising the members of nine glycoside hydrolase families revealed an extensive repertoire of functional genes predicted to be involved in cellulose utilization. This demonstrates that the use of an in situ cellulose baiting method yielded a marine microbial metagenome considerably enriched in functional genes involved in polysaccharide degradation. The research reported here is the first designed to specifically address the bacterial communities that colonize and degrade cellulose in the marine environment and to evaluate the glycoside hydrolase (cellulase and chitinase) gene repertoire of that community, in the absence of the biases associated with PCR-based molecular techniques

    Microbial community pattern detection in human body habitats via ensemble clustering framework

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    The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.Comment: BMC Systems Biology 201
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