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      Metagenomic biomarker discovery and explanation

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          Abstract

          This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.

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          Most cited references53

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          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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            In silico prediction of protein-protein interactions in human macrophages

            Background: Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results: We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion: Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level.
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              Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

              T. Golub (1999)
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                Author and article information

                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central
                1465-6906
                1465-6914
                2011
                24 June 2011
                : 12
                : 6
                : R60
                Affiliations
                [1 ]Department of Biostatistics, 677 Huntington Avenue, Harvard School of Public Health, Boston, MA 02115, USA
                [2 ]Department of Molecular Genetics, 245 First Street, The Forsyth Institute, Cambridge, MA 02142, USA
                [3 ]Department of Oral Medicine, Infection and Immunity, 188 Longwood Ave, Harvard School of Dental Medicine, Boston, MA 02115, USA
                [4 ]Microbial Sequencing Center, 7 Cambridge Center, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
                [5 ]Department of Immunology and Infectious Diseases, 665 Huntington Avenue, Harvard School of Public Health, Boston, MA 02115, USA
                [6 ]Department of Medicine, 75 Francis Street, Harvard Medical School, Boston, MA 02115, USA
                [7 ]Department of Medical Oncology, 44 Binney Street, Dana-Farber Cancer Institute, MA 02215, USA
                Article
                gb-2011-12-6-r60
                10.1186/gb-2011-12-6-r60
                3218848
                21702898
                dc8c80f8-202b-44fe-899a-9218267d4ccf
                Copyright ©2011 Segata 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
                : 4 April 2011
                : 31 May 2011
                : 24 June 2011
                Categories
                Method

                Genetics
                Genetics

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