Article (Scientific journals)
Recon3D enables a three-dimensional view of gene variation in human metabolism.
Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C. et al.
2018In Nature Biotechnology, 36 (3), p. 272-281
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Abstract :
[en] 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.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Brunk, Elizabeth
Sahoo, Swagatika
Zielinski, Daniel C.
Altunkaya, Ali
Drager, Andreas
Mih, Nathan
Gatto, Francesco
Nilsson, Avlant
Preciat Gonzalez, German Andres ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Aurich, Maike Kathrin
Prlic, Andreas
Sastry, Anand
Danielsdottir, Anna D.
Heinken, Almut Katrin ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Noronha, Alberto ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Rose, Peter W.
Burley, Stephen K.
Fleming, Ronan MT ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Nielsen, Jens
Thiele, Ines ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Palsson, Bernhard O.
More authors (11 more) Less
External co-authors :
yes
Language :
English
Title :
Recon3D enables a three-dimensional view of gene variation in human metabolism.
Publication date :
2018
Journal title :
Nature Biotechnology
ISSN :
1546-1696
Publisher :
Nature Publishing Group, United Kingdom
Volume :
36
Issue :
3
Pages :
272-281
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBilu :
since 11 May 2018

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