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Recon3D enables a three-dimensional view of gene variation in human metabolism
Open accessAbstract
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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Cites
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
Mutational heterogeneity in cancer and the search for new cancer-associated genes
Lawrence, M. S., Stojanov, P., Polak, P., Kryukov, G. V., Cibulskis, K., Sivachenko, A., Carter, S. L., Stewart, C., Mermel, C. H., Roberts, S. A., et al. (2013) · Nature · doi:10.1038/nature12213 · Cited by 5,461
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016
Comprehensive genomic characterization of squamous cell lung cancers
The Cancer Genome Atlas Research Network (2012) · Nature · doi:10.1038/nature11404 · Cited by 3,994
What is flux balance analysis?
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010) · Nature Biotechnology · doi:10.1038/nbt.1614 · Cited by 3,903
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Adzhubei, I., Jordan, D. M., & Sunyaev, S. R. (2013) · Current Protocols in Human Genetics · doi:10.1002/0471142905.hg0720s76 · Cited by 3,705
Kris, M. G., Natale, R. B., Herbst, R. S., Lynch, Jr., Prager, D., Belani, C. P., Schiller, J. H., Kelly, K., Spiridonidis, H., Sandler, A., et al. (2003) · JAMA · doi:10.1001/jama.290.16.2149 · Cited by 2,517
A protocol for generating a high-quality genome-scale metabolic reconstruction
Thiele, I., & Palsson, B. Ø. (2010) · Nature Protocols · doi:10.1038/nprot.2009.203 · Cited by 1,789
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Thiele, I., Swainston, N., Fleming, R. M. T., Hoppe, A., Sahoo, S., Aurich, M. K., Haraldsdottir, H., Mo, M. L., Rolfsson, O., Stobbe, M. D., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2488 · Cited by 1,019
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Pinu, F. R., Beale, D. J., Paten, A. M., Kouremenos, K., Swarup, S., Schirra, H. J., & Wishart, D. (2019) · Metabolites · doi:10.3390/metabo9040076 · Cited by 723
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Robinson, J. L., Kocabaş, P., Wang, H., Cholley, P.-E., Cook, D., Nilsson, A., Anton, M., Ferreira, R., Domenzain, I., Billa, V., et al. (2020) · Science Signaling · doi:10.1126/scisignal.aaz1482 · Cited by 442
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Fang, X., Lloyd, C. J., & Palsson, B. O. (2020) · Nature Reviews Microbiology · doi:10.1038/s41579-020-00440-4 · Cited by 292
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Self-citationMoretti, S., Tran, V. D. T., Mehl, F., Ibberson, M., & Pagni, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa992 · Cited by 177
Zhou, Y., Hou, Y., Shen, J., Mehra, R., Kallianpur, A., Culver, D. A., Gack, M. U., Farha, S., Zein, J., Comhair, S., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000970 · Cited by 180
Hertel, J., Harms, A. C., Heinken, A., Baldini, F., Thinnes, C. C., Glaab, E., Vasco, D. A., Pietzner, M., Stewart, I. D., Wareham, N. J., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.035 · Cited by 162
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Dugourd, A., Kuppe, C., Sciacovelli, M., Gjerga, E., Gabor, A., Emdal, K. B., Vieira, V., Bekker‐Jensen, D. B., Kranz, J., Bindels, E. M. J., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209730 · Cited by 149
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Lewis, J. E., Forshaw, T. E., Boothman, D. A., Furdui, C. M., & Kemp, M. L. (2021) · Cell Systems · doi:10.1016/j.cels.2020.12.001 · Cited by 61
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Self-citationCheng, Y., Schlosser, P., Hertel, J., Sekula, P., Oefner, P. J., Spiekerkoetter, U., Mielke, J., Freitag, D. F., Schmidts, M., GCKD Investigators, et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20877-8 · Cited by 37
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Gurazada, S. G. R., Kennedy, H. M., Braatz, R. D., Mehrman, S. J., Polson, S. W., & Rombel, I. T. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108506 · Cited by 9
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Metadata
- Authors
- Brunk, E., Sahoo, S., Zielinski, D. C., Altunkaya, A., Dräger, A., Mih, N., Gatto, F., Nilsson, A., Preciat Gonzalez, G. A., Aurich, M. K., Prlić, A., Sastry, A., Danielsdottir, A. D., Heinken, A., Noronha, A., Rose, P. W., Burley, S. K., Fleming, R. M. T., Nielsen, J., Thiele, I., & Palsson, B. O.
- Venue
- Nature Biotechnology
- Published
- 2018-02-19
- Volume
- 36
- Issue
- 3
- Pages
- 272-281
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2789128799
- Cited by
- 752
- References
- 13
- FWCI
- 22.21
- Type
- journal article
- Language
- en
- Open access
- green
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