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Recon3D enables a three-dimensional view of gene variation in human metabolism
Author(s) -
Elizabeth Brunk,
Swagatika Sahoo,
Daniel C. Zielinski,
Ali Altunkaya,
Andreas Dräger,
Nathan Mih,
Francesco Gatto,
Avlant Nilsson,
German Preciat,
Maike K. Aurich,
Andreas Prlić,
Anand V. Sastry,
Anna Dröfn Daníelsdóttir,
Almut Heinken,
Alberto Noronha,
Peter W. Rose,
S.K. Burley,
Ronan M. T. Fleming,
Jens Nielsen,
Ines Thiele,
Bernhard Ø. Palsson
Publication year - 2018
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/nbt.4072
Subject(s) - computational biology , metabolic network , gene , biology , human genome , variation (astronomy) , genome , cell metabolism , genetics , metabolism , biochemistry , physics , astrophysics
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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