SPECTRA: An integrated knowledge base for comparing tissue and tumor-specific PPI networks in human

Frontiers in Bioengineering and Biotechnology

Volumn 3, Issue MAY, 2015, Pages

  Micale, Giovanni ^a   Ferro, Alfredo ^b   Pulvirenti, Alfredo ^b   Giugno, Rosalba ^b  

^a UNIVERSITY OF PISA   (Italy)

^b UNIVERSITY OF CATANIA   (Italy)

Author keywords

Database; Interactions; Proteins; Tissue; Tumor

Indexed keywords

EID: 84948768299     PISSN: None     EISSN: 22964185     Source Type: Journal    
DOI: 10.3389/fbioe.2015.00058     Document Type: Article

References (70)

1. Adamcsek, B., Palla, G., Farkas, I., Derenyi, I., and Vicsek, T. (2006). Cfinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22, 1021-1023. doi:10.1093/bioinformatics/btl039
2. Alaimo, S., Pulvirenti, A., Giugno, R., and Ferro, A. (2013). Drug-target interaction prediction through domain-tuned network-based inference. Bioinformatics 29, 2004-2008. doi:10.1093/bioinformatics/btt307
3. Bader, G., and Hogue, C. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4:2. doi:10.1186/1471-2105-4-2
4. Banks, E., Nabieva, E., Peterson, R., and Singh, M. (2008). Netgrep: fast network schema searches in interactomes. Genome Biol. 9, R138. doi:10.1186/gb-2008-9-9-r138
5. Barrett, T., Wilhite, S., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., et al. (2013). Ncbi geo: archive for functional genomics data sets-update. Nucleic Acids Res. 41, D991-D995. doi:10.1093/nar/gks1193
6. Barshir, R., Basha, O., Eluk, A., Smoly, I., Lan, A., and Yeger-Lotem, E. (2013). The tissuenet database of human tissue protein-protein interactions. Nucleic Acids Res. 41, D841-D844. doi:10.1093/nar/gks1198
7. Barshir, R., Shwartz, O., Smoly, I., and Yeger-Lotem, E. (2014). Comparative analysis of human tissue interactomes reveals factors leading to tissue-specific manifestation of hereditary diseases. PLoS Comput. Biol. 10:e1003632. doi:10.1371/journal.pcbi.1003632
8. Bartek, J., Petrek, M., Vojtesek, B., Bartkova, J., Kovarik, J., and Rejthar, A. (1987). Hla-dr antigens on differentiating human mammary gland epithelium and breast tumours. Br. J. Cancer 56, 727-733. doi:10.1038/bjc.1987.278
9. Bolstad, B., Irizarry, R., Astrand, M., and Speed, T. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185-193. doi:10.1093/bioinformatics/19.2.185
10. Bossi, A., and Lehner, B. (2009). Tissue specificity and the human protein interaction network. Mol. Syst. Biol. 5, 260. doi:10.1038/msb.2009.17
11. Bruckner, S., Huffner, F., Karp, R., Shamir, R., and Sharan, R. (2010). Topology-free querying of protein interaction networks. J. Comput. Biol. 17, 237-252. doi:10.1089/cmb.2009.0170
12. Cabusora, L., Sutton, E., Fulmer, A., and Forst, C. (2005). Differential network expression during drug and stress response. Bioinformatics 21, 2898-2905. doi:10.1093/bioinformatics/bti440
13. Hamosh, A., Scott, A., Amberger, J., Bocchini, C., and McKusick, V. (2005). Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33, D514-D517. doi:10.1093/nar/gki033
14. Chen, G., and Wang, J. (2012). Identifying functional modules in tissue specific protein interaction network. IEEE Int. Conf. Bioinform. Biomed. Workshops 2012, 581-586. doi:10.1109/BIBMW.2012.6470204
15. Cook, S. (1971). "The complexity of theorem-proving procedures," in Proc. 3rd ACM Symposium on Theory of Computing (New York: ACM), 151-158. doi:10.1145/800157.805047
16. Csermerly, P., Korcsmaros, T., Kiss, H., London, G., and Nussinov, R. (2013). Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol. Ther. 138, 333-408. doi:10.1016/j.pharmthera.2013.01.016
17. Da Silva, G., Silva, T., Duarte, R., Neto, N., Carrara, H., Donadi, E. A., et al. (2013). Expression of the classical and nonclassical hla molecules in breast cancer. Int. J. Breast Cancer 2013, 250435. doi:10.1155/2013/250435
18. Dao, P., Wang, K., Collins, C., Ester, M., Lapuk, A., and Sahinalp, S. (2011). Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 27, i205-i213. doi:10.1093/bioinformatics/btr245
19. Desmedt, C., Piette, F., Loi, S., Wang, Y., Lallemand, F., Haibe-Kains, B., et al. (2007). Strong time dependence of the 76-gene prognostic signature for node-negative breast cancer patients in the transbig multicenter independent validation series. Clin. Cancer Res. 13, 3207-3214. doi:10.1158/1078-0432.CCR-06-2765
20. Dezso, Z., Nikolski, Y., Sviridov, E., Shi, W., Serebriyskaya, T., Dosymbekov, D., et al. (2008). A comprehensive functional analysis of tissue specificity of human gene expression. BMC Biol. 6:49. doi:10.1186/1741-7007-6-49
21. Dittrich, M., Klau, G., Rosenwald, A., Dandekar, T., and Muller, T. (2008). Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24, i223-i231. doi:10.1093/bioinformatics/btn161
22. Emig, D., and Albrecht, M. (2011). Tissue-specific proteins and functional implications. J. Proteome Res. 10, 1893-1903. doi:10.1021/pr101132h
23. Ferro, A., Giugno, R., Pigola, G., Pulvirenti, A., Skripin, D., Bader, G., et al. (2007). Netmatch: a cytoscape plugin for searching biological networks. Bioinformatics 23, 910-912. doi:10.1093/bioinformatics/btm032
24. Flannick, J., Novak, A., Srinivasan, B., McAdams, H., and Batzoglou, S. (2006). Graemlin: general and robust alignment of multiple large interaction networks. Genome Res. 16, 1169-1181. doi:10.1101/gr.5235706
25. Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., et al. (2013). String v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-D815. doi:10.1093/nar/gks1094
26. Ge, X., Yamamoto, S., Tsutsumi, S., Midorikawa, Y., Ihara, S., Wang, S. M., et al. (2005). Interpreting expression profiles of cancers by genome-wide survey of breadth of expression in normal tissues. Genomics 86, 127-141. doi:10.1016/j.ygeno.2005.04.008
27. Geman, S., and Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721-741. doi:10.1109/TPAMI.1984.4767596
28. Guo, Y., Sheng, Q., Li, J., Ye, F., Samuels, D., and Shyr, Y. (2013). Large scale comparison of gene expression levels by microarrays and RNAseq using tcga data. PLoS ONE 8:e71462. doi:10.1371/journal.pone.0071462
29. Guo, Z., Li, Y., Gong, X., Yao, C., Ma, W., Wang, D., et al. (2007). Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. Bioinformatics 23, 2121-2128. doi:10.1093/bioinformatics/btm294
30. Havugimana, P., Hart, G., Nepusz, T., Yang, H., Turinsky, A., and Zhihua, A. (2012). A census of human soluble protein complexes. Cell 150, 1068-1081. doi:10.1016/j.cell.2012.08.011
31. Huang, H., Wu, X., Pandey, R., Li, J., Zhao, G., Ibrahim, S., et al. (2012). C2maps: a network pharmacology database with comprehensive disease-gene-drug connectivity relationships. BMC Genomics 13:S17. doi:10.1186/1471-2164-13-S6-S17
32. Ideker, T., Ozier, O., Schwikowski, B., and Siegel, A. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18, S233-S240. doi:10.1093/bioinformatics/18.suppl_1.S233
33. Ivshina, A., Joshy, G., Senko, O., Mow, B., Putti, T. C., Smeds, J., et al. (2006). Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer. Cancer Res. 66, 10292-10301. doi:10.1158/0008-5472.CAN-05-4414
34. Johnson, W., and Li, C. (2007). Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics 8, 118-127. doi:10.1093/biostatistics/kxj037
35. Kalaev, M., Bafna, V., and Sharan, R. (2009). Fast and accurate alignment of multiple protein networks. J. Comput. Biol. 16, 989-999. doi:10.1089/cmb.2009.0136
36. Kamburov, A., Stelzl, U., Lehrach, H., and Herwig, R. (2013). The consensuspathdb interaction database: 2013 update. Nucleic Acids Res. 41, D793-D800. doi:10.1093/nar/gks1055
37. Kaneko, K., Ishigami, S., Kijma, Y., Funasako, Y., Hirata, M., Okumura, H., et al. (2011). Clinical implication of hla class i expression in breast cancer. BMC Cancer 11:454. doi:10.1186/1471-2407-11-454
38. Karnoub, A., and Weinberg, R. (2007). Chemokine networks and breast cancer metastasis. Breast Dis. 26, 75-85.
39. Lage, K., Hansen, N., Karlberg, E., Eklund, A., Roque, F., Donahoe, P. K., et al. (2008). A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc. Natl. Acad. Sci. U.S.A. 105, 20870-20875. doi:10.1073/pnas.0810772105
40. Liao, C., Lu, K., Baym, M., Singh, R., and Berger, B. (2009). Isorankn: spectral methods for global alignment of multiple protein networks. Bioinformatics 25, 253-258. doi:10.1093/bioinformatics/btp203
41. Licata, L., Briganti, L., Peluso, D., Perfetto, L., Iannuccelli, M., Galeota, E., et al. (2012). Mint, the molecular interaction database: 2012 update. Nucleic Acids Res. 40, D857-D861. doi:10.1093/nar/gkr930
42. Lopes, T., Schaefer, M., Shoemaker, J., Matsuoka, Y., Fontaine, J. F., Neumann, G., et al. (2011). Tissue-specific subnetworks and characteristics of publicly available human protein interaction databases. Bioinformatics 27, 2414-2421. doi:10.1093/bioinformatics/btr414
43. Lukk, M., Kapushesky, M., Nikkila, J., Parkinson, H., Goncalves, A., Huber, W., et al. (2010). A global map of human gene expression. Nat. Biotechnol. 28, 322-324. doi:10.1038/nbt0410-322
44. Magger, O., Waldman, Y., Ruppin, E., and Sharan, R. (2012). Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. PLoS Comput. Biol. 8:e1002690. doi:10.1371/journal.pcbi.1002690
45. McCall, M., Bolstad, B., and Irizarry, R. (2010). Frozen robust multiarray analysis (frma). Biostatistics 11, 242-253. doi:10.1093/biostatistics/kxp059
46. Mete, M., Tang, F., Xu, X., and Nurcan, Y. (2008). A structural approach for finding functional modules from large biological networks. BMC Bioinformatics 9:S19. doi:10.1186/1471-2105-9-S9-S19
47. Micale, G., Pulvirenti, A., Giugno, R., and Ferro, R. (2014a). Gasoline: a greedy and stochastic algorithm for optimal local multiple alignment of interaction networks. PLoS ONE 9:e98750. doi:10.1371/journal.pone.0098750
48. Micale, G., Pulvirenti, A., Giugno, R., and Ferro, R. (2014b). Proteins comparison through probabilistic optimal structure local alignment. Front. Genet. 5:302. doi:10.3389/fgene.2014.00302
49. Muller, A., Homey, B., Soto, H., Ge, N., Catron, D., Buchanan, M. E., et al. (2000). Involvement of chemokine receptors in breast cancer metastasis. Nature 410, 50-56. doi:10.1038/35065016
50. Nersisyan, L., Samsonyan, R., and Arakelyan, A. (2014). Cykeggparser: tailoring kegg pathways to fit into systems biology analysis workflows. F1000Res. 3, 145. doi:10.12688/f1000research.4410.2
51. Orchard, S., Ammari, M., Aranda, B., Brueza, L., Briganti, L., Broackes-Carter, F., et al. (2013). The mintact project-intact as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 42, D358-D363. doi:10.1093/nar/gkt1115
52. Patil, A., Nakai, K., and Nakamura, H. (2011). Hitpredict: a database of quality assessed protein-protein interactions in nine species. Nucleic Acids Res. 39, D744-D749. doi:10.1093/nar/gkq897
53. Peri, S., Navarro, J., Kristiansen, T., Amanchy, R., Surendranath, V., Muthusamy, B., et al. (2004). Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 32, D497-D501. doi:10.1093/nar/gkh070
54. Pro, B., and Dang, N. (2004). Cd26/dipeptidyl peptidase iv and its role in cancer. Histol. Histopathol. 19, 1345-1351.
55. Rajagopalan, D., and Agarwal, P. (2005). Inferring pathways from gene lists using a literature-derived network of biological relationships. Bioinformatics 21, 788-793. doi:10.1093/bioinformatics/bti069
56. Razick, S., Magklaras, G., and Donaldson, I. (2008). irefindex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9:405. doi:10.1186/1471-2105-9-405
57. Rhrissorrakrai, K., and Gunsalus, K. (2011). Mine: module identification in networks. BMC Bioinformatics 12:192. doi:10.1186/1471-2105-12-192
58. Rustici, G., Kolesnikov, N., Brandizi, M., Burdett, T., Dylag, M., Emam, I., et al. (2013). Arrayexpress update-trends in database growth and links to data analysis tools. Nucleic Acids Res. 41, D987-D990. doi:10.1093/nar/gks1174
59. Sahraeian, S. M. E., and Yoon, B. (2013). Smetana: accurate and scalable algorithm for probabilistic alignment of large-scale biological networks. PLoS ONE 8:e67995. doi:10.1371/journal.pone.0067995
60. Shannon, P., Markiel, A., Ozier, O., Baliga, N., Wang, J., Ramage, D., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504. doi:10.1101/gr.1239303
61. Sohler, F., Hanisch, D., and Zimmer, R. (2004). New methods for joint analysis of biological networks and expression data. Bioinformatics 20, 1517-1521. doi:10.1093/bioinformatics/bth112
62. Sotiriou, C., Wirapati, P., Loi, S., Harris, A., Fox, S., Smeds, J., et al. (2006). Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl. Cancer Inst. 98, 262-272. doi:10.1093/jnci/djj052
63. Souiai, O., Becker, E., Prieto, C., Benkahla, A., De Las Rivas, J., and Brun, C. (2011). Functional integrative levels in the human interactome recapitulate organ organization. PLoS ONE 6:e22051. doi:10.1371/journal.pone.0022051
64. Stark, C., Breitkreutz, B., Reguly, T., Boucher, L., Breitkreutz, A., and Tyers, M. (2006). Biogrid: a general repository for interaction datasets. Nucleic Acids Res. 34, D535-D539. doi:10.1093/nar/gkj109
65. Su, A., Cooke, M., Ching, K., Hakak, Y., Walker, J., Wiltshire, T., et al. (2002). Large-scale analysis of the human and mouse transcriptomes. Proc. Natl. Acad. Sci. U.S.A. 99, 4465-4470. doi:10.1073/pnas.012025199
66. Su, A., Wiltshire, T., Batalov, S., Lapp, H., Ching, K., Block, D., et al. (2004). A gene atlas of the mouse and human protein-encoding transcriptomes. Proc. Natl. Acad. Sci. U.S.A. 101, 6062-6067. doi:10.1073/pnas.0400782101
67. Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., Jonasson, K., Forsberg, M., et al. (2010). Towards a knowledge-based human protein atlas. Nat. Biotechnol. 28, 1248-1250. doi:10.1038/nbt1210-1248
68. Xenarios, I., Salwinski, L., Duan, X., Higney, P., Kim, S., and Eisenberg, D. (2000). Dip: the database of interacting proteins. Nucleic Acids Res. 28, 289-291. doi:10.1093/nar/28.1.289
69. Xiao, X., Moreno-Moral, A., Rotival, M., Bottolo, L., and Petretto, E. (2014). Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules. PLoS Genet. 10:e1004006. doi:10.1371/journal.pgen.1004006
70. Zhao, J., Lee, S., Huss, M., and Holme, P. (2012). The network organization of cancer-associated protein complexes in human tissues. Sci. Rep. 3, 1583. doi:10.1038/srep01583