NetDecoder: A network biology platform that decodes context-specific biological networks and gene activities

Nucleic Acids Research

Volumn 44, Issue 10, 2016, Pages

  Da Rocha, Edroaldo Lummertz ^a   Ung, Choong Yong ^a   Mcgehee, Cordelia D ^a   Correia, Cristina ^a   Li, Hu ^a  

^a MAYO GRADUATE SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTOME;

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BREAST CANCER; CASE STUDY; CELL FUNCTION; COMPARATIVE STUDY; COMPUTER PROGRAM; CONTROLLED STUDY; DISEASE CLASSIFICATION; DYSLIPIDEMIA; GENE IDENTIFICATION; GENETIC DATABASE; GENETIC REGULATION; GENOME; NETWORK BIOLOGY PLATFORM; PHENOTYPE; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; SCORING SYSTEM; BIOLOGY; BREAST TUMOR; FACTUAL DATABASE; FEMALE; GENE REGULATORY NETWORK; GENETICS; HUMAN; METABOLISM; PROCEDURES; SIGNAL TRANSDUCTION; SOFTWARE;

ALGORITHMS; ALZHEIMER DISEASE; BREAST NEOPLASMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DYSLIPIDEMIAS; FEMALE; GENE REGULATORY NETWORKS; HUMANS; PROTEIN INTERACTION MAPS; SIGNAL TRANSDUCTION; SOFTWARE; TRANSCRIPTOME;

EID: 84973547977     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkw166     Document Type: Article

References (41)

1. Solovieff, N., Cotsapas, C., Lee, P.H., Purcell, S.M., and Smoller, J.W. (2013). Pleiotropy in complex traits: challenges, and strategies. Nat. Rev. Genet., 14, 483-495
2. Stearns, F.W. (2010). One hundred years of pleiotropy: a retrospective. Genetics, 186, 767-773
3. Alberts, B. (1998). The cell as a collection of protein machines: preparing the next generation of molecular biologists. Cell, 92, 291-294
4. Fry, C.J., and Farnham, P.J. (1999). Context-dependent transcriptional regulation. J. Biol. Chem., 274, 29583-29586
5. Caldon, C.E., Daly, R.J., Sutherland, R.L., and Musgrove, E.A. (2006). Cell cycle control in breast cancer cells. J. Cell. Biochem., 97, 261-274
6. Letting, D.L., Chen, Y.Y., Rakowski, C., Reedy, S., and Blobel, G.A. (2004). Context-dependent regulation of GATA-1 by friend of GATA-1. Proc. Natl. Acad. Sci. U.S.A., 101, 476-481
7. Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., Paulovich, A., Pomeroy, S.L., Golub, T.R., Lander, E.S., et al. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U.S.A., 102, 15545-15550
8. Draghici, S., Khatri, P., Tarca, A.L., Amin, K., Done, A., Voichita, C., Georgescu, C., and Romero, R. (2007). A systems biology approach for pathway level analysis. Genome Res., 17, 1537-1545
9. Khatri, P., Sirota, M., and Butte, A.J. (2012). Ten years of pathway analysis: current approaches, and outstanding challenges. PLoS Comput. Biol., 8, e1002375
10. Jacunski, A., and Tatonetti, N.P. (2013). Connecting the dots: applications of network medicine in pharmacology, and disease. Clin. Pharmacol. Ther., 94, 659-669
11. Havugimana, P.C., Hart, G.T., Nepusz, T., Yang, H., Turinsky, A.L., Li, Z., Wang, P.I., Boutz, D.R., Fong, V., Phanse, S., et al. (2012). A census of human soluble protein complexes. Cell, 150, 1068-1081
12. Marsh, J.A., Hernández, H., Hall, Z., Ahnert, S.E., Perica, T., Robinson, C.V., and Teichmann, S.A. (2013). Protein complexes are under evolutionary selection to assemble via ordered pathways. Cell, 153, 461-470
13. Dutkowski, J., Kramer, M., Surma, M.A., Balakrishnan, R., Cherry, J.M., Krogan, N.J., and Ideker, T. (2013). A gene ontology inferred from molecular networks. Nat. Biotech., 31, 38-45
14. Cahan, P., Li, H., Morris, S.A., Lummertz da Rocha, E., Daley, G.Q., and Collins, J.J. (2014). CellNet: network biology applied to stem cell engineering. Cell, 158, 903-915
15. Pavlidis, P., and Noble, W.S. (2001). Analysis of strain, and regional variation in gene expression in mouse brain. Genome Biol., 2, RESEARCH0042
16. Ford, L. R., and Fulkerson, D. R. (1956). Maximal flow through a network. Can. J. Math., 8, 399-404
17. Prim, R. C. (1957), Shortest connection networks, and some generalizations, Bell Syst. Tech. J., 36, 1389-1401
18. Naderi, A., Teschendorff, A.E., Barbosa-Morais, N.L., Pinder, S.E., Green, A.R., Powe, D.G., Robertson, J.F., Aparicio, S., Ellis, I.O., Brenton, J.D., et al. (2007). A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene, 26, 1507-1516
19. Ferrari, N., Mohammed, Z.M., Nixon, C., Mason, S.M., Mallon, E., McMillan, D.C., Morris, J.S., Cameron, E.R., Edwards, J., and Blyth, K. (2014). Expression of RUNX1 correlates with poor patient prognosis in triple negative breast cancer. PLoS One, 9, e100759
20. Clarke, C., Madden, S.F., Doolan, P., Aherne, S.T., Joyce, H., O?Driscoll, L., Gallagher, W.M., Hennessy, B.T., Moriarty, M., Crown, J., et al. (2013). Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis, 34, 2300-2308
21. Fuxman Bass, J.I., Diallo, A., Nelson, J., Soto, J.M., Myers, C.L., and Walhout, A.J. (2013). Using networks to measure similarity between genes: association index selection. Nat. Methods, 10, 1169-1176
22. Hanahan, D. andWeinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell, 144, 646-674
23. Lane, D.P. (1999). p53, guardian of the genome. Nature, 358, 15-16
24. Dickson, D.W. (1997). Neuropathological diagnosis of Alzheimer?s disease: a perspective from longitudinal clinicopathological studies. Neurobiol. Aging, 18, S21-S26
25. Braak, H., and Braak, E. (1998). Evolution of neuronal changes in the course of Alzheimer?s disease. J. Neural. Transm. Suppl., 53, 127-140
26. van ?t Veer, L.J., Dai, H., van De Vijver, M.J., He, Y.D., Hart, A.A., Mao, M., Peterse, H.L., van der Kooy, K., Marton, M.J., Witteveen, A.T., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536
27. Gong, Z., Yao, S., Zirpoli, G., David Cheng, T.Y., Roberts, M., Khoury, T., Ciupak, G., Davis, W., Pawlish, K., Jandorf, L., et al. (2015). Genetic variants in one-carbon metabolism genes, and breast cancer risk in European American, and African American women. Int. J. Cancer., 137, 666-677
28. Heneka, M.T., Carson, M.J., El Khoury, J., Landreth, G.E., Brosseron, F., Feinstein, D.L., Jacobs, A.H., Wyss-Coray, T., Vitorica, J., Ransohoff, R.M., et al. (2015). Neuroinflammation in Alzheimer?s disease. Lancet Neurol., 14, 388-405
29. Scheper, W., and Hoozemans, J.J. (2015). The unfolded protein response in neurodegenerative diseases: a neuropathological perspective. Acta Neuropathol., 130, 315-331
30. Provias, J., and Jeynes, B. (2011). Correlation analysis of capillary APOE, VEGF, and eNOS expression in Alzheimer brains. Curr. Alzheimer Res., 8, 197-202
31. Lu, Y.S., Chou, C.H., Tzen, K.Y., Gao, M., Cheng, A.L., Kulp, S.K., and Cheng, J.C. (2012). Radiosensitizing effect of a phenylbutyrate-derived histone deacetylase inhibitor in hepatocellular carcinoma. Int. J. Radiat. Oncol. Biol. Phys., 83, e181-e189
32. Levenson, J.M., O?Riordan, K.J., Brown, K.D., Trinh, M.A., Molfese, D.L., and Sweatt, J.D. (2004). Regulation of histone acetylation during memory formation in the hippocampus. J. Biol. Chem., 279, 40545-40559
33. Korzus, E., Rosenfeld, M.G., and Mayford, M. (2004). CBP histone acetyltransferase activity is a critical component of memory consolidation. Neuron, 24, 961-972
34. Guan, J.S., Haggarty, S.J., Giacometti, E., Dannenberg, J.H., Joseph, N., Gao, J., Nieland, T.J., Zhou, Y., Wang, X., Mazitschek, R., et al. (2009). HDAC2 negatively regulates memory formation, and synaptic plasticity. Nature, 459, 55-60
35. Andruska, N.D., Zheng, X., Yang, X., Mao, C., Cherian, M.M., Mahapatra, L., Helferich, W.G., and Shapiro, D.J. (2015). Estrogen receptor - inhibitor activates the unfolded protein response, blocks protein synthesis, and induces tumor regression. Proc. Natl. Acad. Sci. U.S.A., 112, 4737-4742
36. Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M.K., Chuang, R., Jaehnig, E.J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., et al. (2010). Rewiring of genetic networks in response to DNA damage. Science, 330, 1385-1389
37. Ideker, T., and Krogan, N.J. (2012). Differential network biology. Mol. Syst. Biol., 8, 565-573
38. Sinha, A., and Nagarajaram, H.A. (2014). Nodes occupying central positions in human tissue specific PPI networks are enriched with many splice variants. Proteomics, 14, 2242-2248
39. Sinha, A., and Nagarajaram, H.A. (2013). Effect of alternative splicing on the degree centrality of nodes in protein-protein interaction networks of Homo sapiens. J. Poteome Res., 12, 1980-1988
40. Zhong, Q., Simonis, N., Li, Q.R., Charloteaux, B., Heuze, F., Klitgord, N., Tam, S., Yu, H., Venkatesan, K., Mou, D., et al. (2009). Edgetic perturbation models of human inherited disorders. Mol. Syst. Biol., 5, 321-330
41. Dreze, M., Charloteaux, B., Milstein, S., Vidalain, P.O., Yildirim, M.A., Zhong, Q., Svrzikapa, N., Romero, V., Laloux, G., Brasseur, R., et al. (2009). ?Edgetic? perturbation of a C. elegans BCL2 ortholog. Nat. Methods, 6, 843-849