Correcting for the study bias associated with protein-protein interaction measurements reveals differences between protein degree distributions from different cancer types

Frontiers in Genetics

Volumn 6, Issue Aug, 2015, Pages

  Schaefer, Martin H ^a,b   Serrano, Luis ^a,b,c   Andrade Navarro, Miguel A ^d,e  

^a CENTRE FOR GENOMIC REGULATION CRG   (Spain)

^b UNIVERSITAT POMPEU FABRA   (Spain)

^c INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

^d JOHANNES GUTENBERG UNIVERSITY   (Germany)

^e INSTITUTE OF MOLECULAR BIOLOGY   (Germany)

Author keywords

Cancer genes; Degree distribution; Network analysis; Protein protein interactions; Study bias

Indexed keywords

ONCOPROTEIN;

ARTICLE; EXOME; GENE ONTOLOGY; HUMAN; NEOPLASM; PROTEIN LOCALIZATION; PROTEIN PROTEIN INTERACTION; TUMOR GENE;

EID: 84941004380     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2015.00260     Document Type: Article

References (33)

1. Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., et al. (2000). Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503-511. doi: 10.1038/35000501.
2. Altvater, M., Chang, Y., Melnik, A., Occhipinti, L., Schütz, S., Rothenbusch, U., et al. (2012). Targeted proteomics reveals compositional dynamics of 60S pre-ribosomes after nuclear export. Mol. Syst. Biol. 8, 628. doi: 10.1038/msb.2012.63.
3. Björklund, Å.K., Light, S., Hedin, L., and Elofsson, A. (2008). Quantitative assessment of the structural bias in protein-protein interaction assays. Proteomics 8, 4657-4667. doi: 10.1002/pmic.200800150.
4. Calvano, S. E., Xiao, W., Richards, D. R., Felciano, R. M., Baker, H. V., Cho, R. J., et al. (2005). A network-based analysis of systemic inflammation in humans. Nature 437, 1032-1037. doi: 10.1038/nature03985.
5. Chatr-aryamontri, A., Ceol, A., Palazzi, L. M., Nardelli, G., Schneider, M. V., Castagnoli, L., et al. (2007). MINT: the molecular INTeraction database. Nucleic Acids Res. 35, D572-D574. doi: 10.1093/nar/gkl950.
6. Coulomb, S., Bauer, M., Bernard, D., and Marsolier-Kergoat, M.-C. (2005). Gene essentiality and the topology of protein interaction networks. Proc. Biol. Sci. 272, 1721-1725. doi: 10.1098/rspb.2005.3128.
7. Defoiche, J., Zhang, Y., Lagneaux, L., Willems, L., and Macallan, D. C. (2010). In vivo ribosomal RNA turnover is down-regulated in leukaemic cells in chronic lymphocytic leukaemia. Br. J. Haematol. 151, 192-195. doi: 10.1111/j.1365-2141.2010.08334.x.
8. Dickerson, J., Pinney, J., and Robertson, D. (2010). The biological context of HIV-1 host interactions reveals subtle insights into a system hijack. BMC Syst. Biol. 4:80. doi: 10.1186/1752-0509-4-80.
9. Fromont-Racine, M., Senger, B., Saveanu, C., and Fasiolo, F. (2003). Ribosome assembly in eukaryotes. Gene 313, 17-42. doi: 10.1016/S0378-1119(03)00629-2.
10. Futschik, M. E., Chaurasia, G., and Herzel, H. (2007). Comparison of human protein-protein interaction maps. Bioinformatics 23, 605-611. doi: 10.1093/bioinformatics/btl683.
11. Gavin, A. C., Bosche, M., Krause, R., Grandi, P., Marzioch, M., Bauer, A., et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141-147. doi: 10.1038/415141a.
12. Gillis, J., Ballouz, S., and Pavlidis, P. (2014). Bias tradeoffs in the creation and analysis of protein-protein interaction networks. J. Proteomics 100, 44-54. doi: 10.1016/j.jprot.2014.01.020.
13. Gillis, J., and Pavlidis, P. (2011). The impact of multifunctional genes on "guilt by association" analysis. PLoS ONE 6:e17258. doi: 10.1371/journal.pone.0017258.
14. Hakes, L., Pinney, J. W., Robertson, D. L., and Lovell, S. C. (2008). Protein-protein interaction networks and biology-what's the connection? Nat. Biotechnol. 26, 69-72. doi: 10.1038/nbt0108-69.
15. Ideker, T., and Krogan, N. J. (2012). Differential network biology. Mol. Syst. Biol. 8, 565. doi: 10.1038/msb.2011.99.
16. Ivanic, J., Yu, X., Wallqvist, A., and Reifman, J. (2009). Influence of protein abundance on high-throughput protein-protein interaction detection. PLoS ONE 4:e5815. doi: 10.1371/journal.pone.0005815.
17. Jensen, L. J., and Bork, P. (2008). Not Comparable, but complementary. Science 322, 56-57. doi: 10.1126/science.1164801.
18. Jonsson, P. F., and Bates, P. A. (2006). Global topological features of cancer proteins in the human interactome. Bioinformatics 22, 2291-2297. doi: 10.1093/bioinformatics/btl390.
19. Kamburov, A., Pentchev, K., Galicka, H., Wierling, C., Lehrach, H., and Herwig, R. (2011). ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 39, D712-D717. doi: 10.1093/nar/gkq1156.
20. Kerrien, S., Alam-Faruque, Y., Aranda, B., Bancarz, I., Bridge, A., Derow, C., et al. (2007). IntAct-open source resource for molecular interaction data. Nucleic Acids Res. 35, D561-D565. doi: 10.1093/nar/gkl958.
21. Lawrence, M. S., Stojanov, P., Mermel, C. H., Robinson, J. T., Garraway, L. A., Golub, T. R., et al. (2014). Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495-501. doi: 10.1038/nature12912.
22. Nie, Y., and Yu, J. (2013). Mining breast cancer genes with a network based noise-tolerant approach. BMC Syst. Biol. 7:49. doi: 10.1186/1752-0509-7-49.
23. Rambaldi, D., Giorgi, F. M., Capuani, F., Ciliberto, A., and Ciccarelli, F. D. (2008). Low duplicability and network fragility of cancer genes. Trends Genet. 24, 427-430. doi: 10.1016/j.tig.2008.06.003.
24. Rolland, T., Tsan, M., Charloteaux, B., Pevzner, S. J., Zhong, Q., Sahni, N., et al. (2014). A proteome-scale map of the human interactome network. Cell 159, 1212-1226. doi: 10.1016/j.cell.2014.10.050.
25. Rubin, A. D. (1971). Defective control of ribosomal RNA processing in stimulated leukemic lymphocytes. J. Clin. Invest. 50, 2485-2497. doi: 10.1172/JCI106749.
26. Schaefer, M. H., Fontaine, J.-F., Vinayagam, A., Porras, P., Wanker, E. E., and Andrade-Navarro, M. A. (2012). HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS ONE 7:e31826. doi: 10.1371/journal.pone.0031826.
27. Schaefer, M. H., Lopes, T. J. S., Mah, N., Shoemaker, J. E., Matsuoka, Y., Fontaine, J.-F., et al. (2013). Adding protein context to the human protein-protein interaction network to reveal meaningful interactions. PLoS Comput. Biol. 9:e1002860. doi: 10.1371/journal.pcbi.1002860.
28. Turner, B., Razick, S., Turinsky, A. L., Vlasblom, J., Crowdy, E. K., Cho, E., et al. (2010). iRefWeb: interactive analysis of consolidated protein interaction data and their supporting evidence. Database (Oxford) 2010:ba9023. doi: 10.1093/database/baq023.
29. Venkatesan, K., Rual, J. F., Vazquez, A., Stelzl, U., Lemmens, I., Hirozane-Kishikawa, T., et al. (2009). An empirical framework for binary interactome mapping. Nat. Methods 6, 83-90. doi: 10.1038/nmeth.1280.
30. Von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S. G., Fields, S., et al. (2002). Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399-403. doi: 10.1038/nature750.
31. Wachi, S., Yoneda, K., and Wu, R. (2005). Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21, 4205-4208. doi: 10.1093/bioinformatics/bti688.
32. Xu, J., and Li, Y. (2006). Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics 22, 2800-2805. doi: 10.1093/bioinformatics/btl467.
33. Zotenko, E., Mestre, J., O'Leary, D. P., and Przytycka, T. M. (2008). Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput. Biol. 4:e1000140. doi: 10.1371/journal.pcbi.1000140.