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      Recon3D: A Resource Enabling A Three-Dimensional View of Gene Variation in Human Metabolism


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          Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

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          A protocol for generating a high-quality genome-scale metabolic reconstruction.

          Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.
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            Is Open Access

            Ensembl BioMarts: a hub for data retrieval across taxonomic space

            For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/
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              Network pharmacology.


                Author and article information

                Nat Biotechnol
                Nat. Biotechnol.
                Nature biotechnology
                22 January 2018
                19 February 2018
                March 2018
                19 August 2018
                : 36
                : 3
                : 272-281
                [a ]Department of Bioengineering, University of California San Diego CA 92093
                [b ]The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
                [c ]Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
                [e ]RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
                [f ]Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, 72076 Tübingen, Germany
                [g ]Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
                [h ]Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
                Author notes
                [* ]correspondence should be addressed to: I.T. ( ines.thiele@ 123456gmail.com ) and B.O.P ( palsson@ 123456eng.ucsd.edu )

                Current address: Department of Chemical Engineering, Indian Institute of Technology Madras, India 600036


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