Recon3D enables a three-dimensional view of gene variation in human metabolism

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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  • Author: Brunk, Elizabeth

    Technical University of Denmark, Denmark

  • Author: Sahoo, Swagatika

    University of Luxembourg, Luxembourg

  • Author: Zielinski, Daniel C.

    University of California at San Diego, United States

  • Author: Altunkaya, Ali

    University of California at San Diego, United States

  • Author: Dräger, Andreas

    University of Tübingen, Germany

  • Author: Mih, Nathan

    University of Tübingen, Germany

  • Author: Gatto, Francesco

    Chalmers University of Technology, Sweden

  • Author: Nilsson, Avlant

    Chalmers University of Technology, Sweden

  • Author: Preciat Gonzalez, German Andres

    University of Luxembourg, Luxembourg

  • Author: Aurich, Maike Kathrin

    University of Luxembourg, Luxembourg

  • Author: Prlic, Andreas

    University of California at San Diego, United States

  • Author: Sastry, Anand

    University of California at San Diego, United States

  • Author: Danielsdottir, Anna D.

    University of Luxembourg, Luxembourg

  • Author: Heinken, Almut

    University of Luxembourg, Luxembourg

  • Author: Noronha, Alberto

    University of Luxembourg, Luxembourg

  • Author: Rose, Peter W.

    University of California at San Diego, United States

  • Author: Burley, Stephen K.

    University of California at San Diego, United States

  • Author: Fleming, Ronan M. T.

    University of Luxembourg, Luxembourg

  • Author: Nielsen, Jens

    Yeast Cell Factories, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, 2800, Kgs. Lyngby, Denmark

  • Author: Thiele, Ines

    University of Luxembourg, Luxembourg

  • Author: Palsson, Bernhard O.

    Network Reconstruction in Silico Biology, Big Data 2 Knowledge, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, 2800, Kgs. Lyngby, Denmark

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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.
Original languageEnglish
JournalNature Biotechnology
Volume36
Issue number3
Pages (from-to)272-281
ISSN1087-0156
DOIs
Publication statusPublished - 2018
CitationsWeb of Science® Times Cited: No match on DOI

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