The emergence of proteome-wide technologies: Systematic analysis of proteins comes of age

Nature Reviews Molecular Cell Biology

Volumn 15, Issue 7, 2014, Pages 453-464

  Breker, Michal ^a   Schuldiner, Maya ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

CELL PROTEIN; PROTEOME; TRANSCRIPTOME;

BIOINFORMATICS; CELL FUNCTION; DYNAMICS; HUMAN; IMMUNODETECTION; MICROFLUIDICS; MICROSCOPY; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DEGRADATION; PROTEIN FUNCTION; PROTEIN MODIFICATION; PROTEIN PURIFICATION; PROTEOMICS; REVIEW; SINGLE CELL ANALYSIS; TURNOVER TIME;

COMBINATORIAL CHEMISTRY TECHNIQUES; HUMANS; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEINS; PROTEOLYSIS; PROTEOMICS;

EID: 84904764792     PISSN: 14710072     EISSN: 14710080     Source Type: Journal    
DOI: 10.1038/nrm3821     Document Type: Review

References (150)

1. The ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816 (2007
2. Valouev, A. et al. Determinants of nucleosome organization in primary human cells. Nature 474, 516-520 (2011
3. Gasch, A. P. et al. Genomic expression programs in the response of yeast cells to environmental changes. Mol. Biol. Cell 11, 4241-4257 (2000
4. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109-126 (2000
5. Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218-223 (2009
6. De Godoy, L. M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251-1254 (2008
7. Newman, J. R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840-846 (2006
8. Soufi, B. et al. Global analysis of the yeast osmotic stress response by quantitative proteomics. Mol. Biosyst. 5, 1337-1346 (2009
9. Huh, W. K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686-691 (2003
10. Zielinska, D. F., Gnad, F., Wisniewski, J. R. & Mann, M. Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell 141, 897-907 (2010
11. Olsen, J. V. & Mann, M. Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteom. 12, 3444-3452 (2013
12. Phanstiel, D. H. et al. Proteomic and phosphoproteomic comparison of human ES and iPS cells. Nature Methods 8, 821-827 (2011
13. Newman, R. H. et al. Construction of human activity-based phosphorylation networks. Mol. Syst. Biol. 9, 655 (2013
14. Wang, C., Wang, M., Zhou, Y., Dupree, J. L. & Han, X. Alterations in mouse brain lipidome after disruption of CST gene: A lipidomics study. Mol. Neurobiol. http://dx.doi.org/10.1007/s12035-013-8626-0 (2014
15. Cimino, J. et al. Towards lipidomics of low-Abundant species for exploring tumor heterogeneity guided by high-resolution mass spectrometry imaging. Int. J. Mol. Sci. 14, 24560-24580 (2013
16. Zhang, Q. & Wakelam, M. J. O. Lipidomics in the analysis of malignancy. Adv. Biol. Regul. 54, 93-98 (2013
17. Cooper, S. J. et al. High-throughput profiling of amino acids in strains of the Saccharomyces cerevisiae deletion collection. Genome Res. 20, 1288-1296 (2010
18. Zenobi, R. Single-cell metabolomics: Analytical and biological perspectives. Science 342, 1243259 (2013
19. Rubakhin, S. S., Romanova, E. V., Nemes, P. & Sweedler, J. V. Profiling metabolites and peptides in single cells. Nature Methods 8, S20-29 (2011
20. Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507-519 (2005
21. Hillenmeyer, M. E. et al. The chemical genomic portrait of yeast: Uncovering a phenotype for all genes. Science 320, 362-365 (2008
22. Tarassov, K. et al. An in vivo map of the yeast protein interactome. Science 320, 1465-1470 (2008
23. Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nature Rev. Genet. 13, 227-232 (2012
24. Causton, H. C. et al. Remodeling of yeast genome expression in response to environmental changes. Mol. Biol. Cell. 12, 323-337 (2001
25. Tkach, J. M. et al. Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nature Cell Biol. 14, 966-976 (2012
26. Breker, M., Gymrek, M. & Schuldiner, M. A novel single-cell screening platform reveals proteome plasticity during yeast stress responses. J. Cell Biol. 200, 839-850 (2013
27. Dénervaud N. et al. A chemostat array enables the spatio-temporal analysis of the yeast proteome. Proc. Natl Acad. Sci. USA 110, 15842-15847 (2013
28. Jung, S. et al. Global analysis of condition-specific subcellular protein distribution and abundance. Mol. Cell. Proteom. 12, 1421-1435 (2013
29. Lee, M. V. et al. A dynamic model of proteome changes reveals new roles for transcript alteration in yeast. Mol. Syst. Biol. 7, 514 (2011
30. Washburn, M. P. et al. Protein pathway and complex clustering of correlated mRNA and protein expression analyses in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 100, 3107-3112 (2003
31. Khan, Z. et al. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342, 1100-1104 (2013
32. Schrimpf, S. P. et al. Comparative functional analysis of the Caenorhabditis elegans and Drosophila melanogaster proteomes. PLoS Biol. 7, e48 (2009
33. Laurent, J. M. et al. Protein abundances are more conserved than mRNA abundances across diverse taxa. Proteomics 10, 4209-4212 (2010
34. Vogel, C. Evolution. Protein expression under pressure. Science 342, 1052-1053 (2013
35. Walther, T. C., Olsen, J. V. & Mann, M. Yeast expression proteomics by high-resolution mass spectrometry. Methods Enzym. 470, 259-280 (2010
36. Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737-741 (2003
37. Uhlen, M. et al. Towards a knowledge-based Human Protein Atlas. Nature Biotech. 28, 1248-1250 (2010
38. Navani, S. The human protein atlas. J. Obstet. Gynecol. India. 61, 27-31 (2011
39. Stadler, C. et al. RNA- and antibody-based profiling of the human proteome with focus on chromosome 19. J. Proteome Res. 13, 2019-2027 (2014
40. Edfors, F. et al. Immuno-proteomics using polyclonal antibodies and stable isotope labeled affinity-purified recombinant proteins. Mol. Cell. Proteom. http://dx. doi.org/10.1074/mcp. M113.034140 (2014
41. Mann, M., Kulak, N. a, Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol. Cell. 49, 583-590 (2013
42. Altelaar, A. F. M., Munoz, J. & Heck, A. J. R. Next-generation proteomics: Towards an integrative view of proteome dynamics. Nature Rev. Genet. 14, 35-48 (2013
43. Bensimon, A., Heck, A. J. R. & Aebersold, R. Mass spectrometry-based proteomics and network biology. Annu. Rev. Biochem. 81, 379-405 (2012
44. Walzthoeni, T., Leitner, A., Stengel, F. & Aebersold, R. Mass spectrometry supported determination of protein complex structure. Curr. Opin. Struct. Biol. 23, 252-260 (2013
45. Meissner, F., Scheltema, R. a, Mollenkopf, H.-J. & Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475-478 (2013
46. Sabidó, E., Selevsek, N. & Aebersold, R. Mass spectrometry-based proteomics for systems biology. Curr. Opin. Biotechnol. 23, 591-597 (2012
47. Picotti, P. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266-270 (2013
48. Maier, T. et al. Quantification of mRNA and protein and integration with protein turnover in a bacterium. Mol. Syst. Biol. 7, 511 (2011
49. Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011
50. Geiger, T. et al. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell. Proteom. 12, 1709-1722 (2013
51. Low, T. Y. et al. Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis. Cell Rep. 5, 1469-1478 (2013
52. Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011
53. Schmidt, A. et al. Absolute quantification of microbial proteomes at different states by directed mass spectrometry. Mol. Syst. Biol. 7, 510 (2011
54. Nagaraj, N. et al. System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top orbitrap. Mol. Cell. Proteomics 11, M111.013722 (2012
55. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174-1189 (2010
56. Wisniewski, J. R. et al. Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol. Syst. Biol. 8, 611 (2012
57. Hebert, A. S. et al. The one hour yeast proteome. Mol. Cell. Proteom. 13, 339-347 (2013
58. Baerenfaller, K. et al. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320, 938-941 (2008
59. Branca, R. M. M. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature Methods 11, 59-62 (2014
60. Passarelli, M. K. & Ewing, A. G. Single-cell imaging mass spectrometry. Curr. Opin. Chem. Biol. 17, 854-859 (2013
61. Ibáñez, A. J. et al. Mass spectrometry-based metabolomics of single yeast cells. Proc. Natl Acad. Sci. USA 110, 8790-8794 (2013
62. Gefen, O. & Balaban, N. Q. The importance of being persistent: Heterogeneity of bacterial populations under antibiotic stress. FEMS Microbiol. Rev. 33, 704-717 (2009
63. Balázsi, G., van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: From microbes to mammals. Cell 144, 910-925 (2011
64. Rubakhin, S. S., Lanni, E. J. & Sweedler, J. V. Progress toward single cell metabolomics. Curr. Opin. Biotechnol. 24, 95-104 (2013
65. Tanner, S. D., Baranov, V. I., Ornatsky, O. I., Bandura, D. R. & George, T. C. An introduction to mass cytometry: Fundamentals and applications. Cancer Immunol. Immunother. 62, 955-965 (2013
66. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687-696 (2011
67. Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nature Biotech. 30, 858-867 (2012
68. Bar-Even, A. et al. Noise in protein expression scales with natural protein abundance. Nature Genet. 38, 636-643 (2006
69. Handfield, L.-F., Chong, Y. T., Simmons, J., Andrews, B. J. & Moses, A. M. Unsupervised clustering of subcellular protein expression patterns in high-throughput microscopy images reveals protein complexes and functional relationships between proteins. PLoS Comput. Biol. 9, e1003085 (2013
70. Rimon, N. & Schuldiner, M. Getting the whole picture: Combining throughput with content in microscopy. J. Cell Sci. 124, 3743-3751 (2011
71. Sigal, A. et al. Generation of a fluorescently labeled endogenous protein library in living human cells. Nature Protoc. 2, 1515-1527 (2007
72. Frenkel-Morgenstern, M. et al. Dynamic Proteomics: A database for dynamics and localizations of endogenous fluorescently-tagged proteins in living human cells. Nucleic Acids Res. 38, D508-D512 (2010
73. Sigal, A. et al. Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. Nature Methods 3, 525-531 (2006
74. Geva-Zatorsky, N. et al. Protein dynamics in drug combinations: A linear superposition of individual-drug responses. Cell 140, 643-651 (2010
75. Hu, P. et al. Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol. 7, e96 (2009
76. Butland, G. et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433, 531-537 (2005
77. Taniguchi, Y. et al. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science 329, 533-538 (2010
78. Maerkl, S. J. Next generation microfluidic platforms for high-throughput protein biochemistry. Curr. Opin. Biotechnol. 22, 59-65 (2011
79. Milo, R. What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35, 1050-1055 (2013
80. Simpson, C. E. & Ashe, M. P. Adaptation to stress in yeast: To translate or not? Biochem. Soc. Trans. 40, 794-799 (2012
81. Besse, F. & Ephrussi, A. Translational control of localized mRNAs: Restricting protein synthesis in space and time. Nature Rev. Mol. Cell Biol. 9, 971-980 (2008
82. Sonenberg, N. & Hinnebusch, A. G. Regulation of translation initiation in eukaryotes: Mechanisms and biological targets. Cell 136, 731-745 (2009
83. Kuersten, S., Radek, A., Vogel, C. & Penalva, L. O. F. Translation regulation gets its "omics" moment. Wiley Interdiscip. Rev. RNA 4, 617-630 (2013
84. Brar, G. A. et al. High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335, 552-557 (2012
85. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147, 789-802 (2011
86. Stern-Ginossar, N. et al. Decoding human cytomegalovirus. Science 338, 1088-1093 (2012
87. Stumpf, C. R., Moreno, M. V., Olshen, A. B., Taylor, B. S. & Ruggero, D. The translational landscape of the mammalian cell cycle. Mol. Cell. 52, 574-582 (2013
88. Gingold, H. & Pilpel, Y. Determinants of translation efficiency and accuracy. Mol. Syst. Biol. 7, 481 (2011
89. Ravid, T. & Hochstrasser, M. Diversity of degradation signals in the ubiquitin-proteasome system. Nature Rev. Mol. Cell. Biol. 9, 679-690 (2008
90. Belle, A., Tanay, A., Bitincka, L., Shamir, R. & O'Shea, E. K. Quantification of protein half-lives in the budding yeast proteome. Proc. Natl Acad. Sci. USA 103, 13004-13009 (2006
91. Eden, E. et al. Proteome half-life dynamics in living human cells. Science. 331, 764-768 (2011
92. Geva-Zatorsky, N. et al. Using bleach-chase to measure protein half-lives in living cells. Nature Protoc. 7, 801-811 (2012
93. Khmelinskii, A. et al. Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nature Biotech. 30, 708-714 (2012
94. Lippincott-Schwartz, J. & Patterson, G. H. Photoactivatable fluorescent proteins for diffraction-limited and super-resolution imaging. Trends Cell Biol. 19, 555-565 (2009
95. Claydon, A. J. & Beynon, R. Proteome dynamics: Revisiting turnover with a global perspective. Mol. Cell. Proteom. 11, 1551-1565 (2012
96. Claydon, A. J., Thom, M. D., Hurst, J. L. & Beynon, R. J. Protein turnover: Measurement of proteome dynamics by whole animal metabolic labelling with stable isotope labelled amino acids. Proteomics. 12, 1194-1206 (2012
97. Doherty, M. K., Hammond, D. E., Clague, M. J., Gaskell, S. J. & Beynon, R. J. Turnover of the human proteome: Determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 8, 104-112 (2009
98. Hodas, J. J. L. et al. Dopaminergic modulation of the hippocampal neuropil proteome identified by bioorthogonal noncanonical amino acid tagging (BONCAT). Proteomics 12, 2464-2476 (2012
99. Schwanhäusser, B., Gossen, M., Dittmar, G. & Selbach, M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9, 205-209 (2009
100. Bagert, J. D. et al. Quantitative, time-resolved proteomic analysis by combining bioorthogonal noncanonical amino acid tagging and pulsed stable isotope labeling by amino acids in cell culture. Mol. Cell. Proteom. 13, 1352-1358 (2014
101. Sobczyk, G. J., Wang, J. & Weijer, C. J. SILAC-based proteomic quantification of chemoattractant-induced cytoskeleton dynamics on a second to minute timescale. Nature Commun. 5, 3319 (2014
102. Jayapal, K. P. et al. Multitagging proteomic strategy to estimate protein turnover rates in dynamic systems. J. Proteome Res. 9, 2087-2097 (2010
103. Howden, A. J. M. et al. QuaNCAT: Quantitating proteome dynamics in primary cells. Nature Methods 10, 343-346 (2013
104. Merbl, Y., Refour, P., Patel, H., Springer, M. & Kirschner, M. W. Profiling of ubiquitin-like modifications reveals features of mitotic control. Cell 152, 1160-1172 (2013
105. Roth, A. F. et al. Global analysis of protein palmitoylation in yeast. Cell 125, 1003-1013 (2006
106. Wan, J., Roth, A. F., Bailey, A. O. & Davis, N. G. Palmitoylated proteins: Purification and identification. Nature Protoc. 2, 1573-1584 (2007
107. Zhang, Y., Kweon, H. K., Shively, C., Kumar, A. & Andrews, P. C. Towards systematic discovery of signaling networks in budding yeast filamentous growth stress response using interventional phosphorylation data. PLoS Comput. Biol. 9, e1003077 (2013
108. Schmidt, A. et al. Quantitative phosphoproteomics reveals the role of protein arginine phosphorylation in the bacterial stress response. Mol. Cell. Proteomics 13, 537-550 (2013
109. Robitaille, A. M. et al. Quantitative phosphoproteomics reveal mTORC1 activates De novo pyrimidine synthesis. Science 339, 1320-1323 (2013
110. Rigbolt, K. T. G. et al. System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation. Sci. Signal. 4, rs3 (2011
111. Zoumaro-Djayoon, A. D. et al. Investigating the role of FGF-2 in stem cell maintenance by global phosphoproteomics profiling. Proteomics. 11, 3962-3971 (2011
112. Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell. 127, 635-648 (2006
113. Kim, W. et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell. 44, 325-340 (2011
114. Merbl, Y. & Kirschner, M. W. Large-scale detection of ubiquitination substrates using cell extracts and protein microarrays. Proc. Natl Acad. Sci. USA 106, 2543-2548 (2009
115. Peters, L. Z., Hazan, R., Breker, M., Schuldiner, M. & Ben-Aroya, S. Formation and dissociation of proteasome storage granules are regulated by cytosolic pH. J. Cell Biol. 201, 663-671 (2013
116. Ahmad, Y., Boisvert, F.-M., Lundberg, E., Uhlen, M. & Lamond, A. I. Systematic analysis of protein pools, isoforms, and modifications affecting turnover and subcellular localization. Mol. Cell. Proteomics 11, M111.013680 (2012
117. Boisvert, F.-M. et al. A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol. Cell. Proteomics 11, M111.011429 (2012
118. Uetz, P. et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623-627 (2000
119. Gisler, S. M. et al. Monitoring protein-protein interactions between the mammalian integral membrane transporters and PDZ-interacting partners using a modified split-ubiquitin membrane yeast two-hybrid system. Mol. Cell. Proteom. 7, 1362-1377 (2008
120. Miernyk, J. A. & Thelen, J. J. Biochemical approaches for discovering protein-protein interactions. Plant J. 53, 597-609 (2008
121. Popescu, S. C. et al. MAPK target networks in Arabidopsis thaliana revealed using functional protein microarrays. Genes Dev. 23, 80-92 (2009
122. Von Mering, C. et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417, 399-403 (2002
123. Mika, S. & Rost, B. Protein-protein interactions more conserved within species than across species. PLoS Comput. Biol. 2, e79 (2006
124. Rajagopala, S. V. et al. The binary protein-protein interaction landscape of Escherichia coli. Nature Biotech. 32, 285-290 (2014
125. Braun, P., Aubourg, S., Van Leene, J., De Jaeger, G. & Lurin, C. Plant protein interactomes. Annu. Rev. Plant Biol. 64, 161-187 (2013
126. Walhout, A. J. et al. Protein interaction mapping in C. elegans using proteins involved in vulval development. Science 287, 116-122 (2000
127. Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science 302, 1727-1736 (2003
128. Goodman, S. R., Daescu, O., Kakhniashvili, D. G. & Zivanic, M. The proteomics and interactomics of human erythrocytes. Exp. Biol. Med. 238, 509-518 (2013
129. Rual, J.-F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173-1178 (2005
130. Havugimana, P. C. et al. A census of human soluble protein complexes. Cell 150, 1068-1081 (2012
131. Zhang, S., Ma, C. & Chalfie, M. Combinatorial marking of cells and organelles with reconstituted fluorescent proteins. Cell. 119, 137-144 (2004
132. Sung, M.-K. & Huh, W.-K. In vivo quantification of protein-protein interactions in Saccharomyces cerevisiae using bimolecular fluorescence complementation assay. J. Microbiol. Methods. 83, 194-201 (2010
133. Johnsson, N. Analyzing protein-protein interactions in the post-interactomic era. Are we ready for the endgame? Biochem. Biophys. Res. Commun. 445, 739-745 (2014
134. Rao, V. S., Srinivas, K., Sujini, G. N. & Kumar, G. N. S. Protein-protein interaction detection: Methods and analysis. Int. J. Proteom. 2014, 147648 (2014
135. Ngounou Wetie, A. G. et al. Protein-protein interactions: Switch from classical methods to proteomics and bioinformatics-based approaches. Cell. Mol. Life Sci. 71, 205-228 (2013
136. Vlasblom, J., Jin, K., Kassir, S. & Babu, M. Exploring mitochondrial system properties of neurodegenerative diseases through interactome mapping. J. Proteomics 100, 8-24 (2013
137. Hagen, N., Bayer, K., Roesch, K. & Schindler, M. The intra viral protein interaction network of hepatitis C virus. Mol. Cell. Proteom. http://dx.doi. org/10.1074/mcp.M113.036301 (2014
138. Moya, I. M. & Halder, G. Discovering the Hippo pathway protein-protein interactome. Cell Res. 24, 137-138 (2014
139. Guney, E. & Oliva, B. Analysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes. PLoS ONE 9, e94686 (2014
140. Soler-López, M., Zanzoni, A., Lluís, R., Stelzl, U. & Aloy, P. Interactome mapping suggests new mechanistic details underlying Alzheimer's disease. Genome Res. 21, 364-376 (2011
141. Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273-299 (2011
142. Woo, S. et al. Proteogenomic database construction driven from large scale RNA-seq data. J. Proteome Res. 13, 21-28 (2014
143. Shi, Z., Wang, J. & Zhang, B. NetGestalt: Integrating multidimensional omics data over biological networks. Nature Methods 10, 597-598 (2013
144. Talwar, P. et al. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease. BMC Genomics 15, 199 (2014
145. Borchers, C. H. et al. The Human Proteome Organization Chromosome 6 Consortium: Integrating chromosome-centric and biology/disease driven strategies. J. Proteom. 100, 60-67 (2014
146. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 1293-1307 (2012
147. Rubinstein, A. D. & Kimchi, A. Life in the balance - a mechanistic view of the crosstalk between autophagy and apoptosis. J. Cell Sci. 125, 5259-5268 (2012
148. Zeng, L. et al. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141, 682-691 (2010
149. Wisniewski, J. R., Zougman, A., Nagaraj, N. & Mann, M. Universal sample preparation method for proteome analysis. Nature Methods 6, 359-362 (2009
150. Gorini, G., Nunez, Y. O. & Mayfield, R. D. Integration of miRNA and protein profiling reveals coordinated neuroadaptations in the alcohol-dependent mouse brain. PLoS ONE 8, e82565 (2013