The Perseus computational platform for comprehensive analysis of (prote)omics data

Nature Methods

Volumn 13, Issue 9, 2016, Pages 731-740

  Tyanova, Stefka ^a   Temu, Tikira ^a   Sinitcyn, Pavel ^a   Carlson, Arthur ^a   Hein, Marco Y ^b   Geiger, Tamar ^c   Mann, Matthias ^a   Cox, Jürgen ^a  

^a MAX PLANCK INSTITUTE OF BIOCHEMISTRY   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; PROTEIN;

ALGORITHM; COMPREHENSIVE ANALYSIS; CROSS OMICS DATA ANALYSIS; DATA ANALYSIS; HUMAN; MACHINE LEARNING; MEDICAL DOCUMENTATION; NONHUMAN; PATTERN RECOGNITION; PERSEUS COMPUTATIONAL PLATFORM; PRIORITY JOURNAL; PROGNOSIS; PROTEIN ANALYSIS; PROTEIN INTERACTION; PROTEIN PROCESSING; PROTEOMICS; REVIEW; SOFTWARE; TIME SERIES ANALYSIS; WORKFLOW; BIOLOGY; CHEMISTRY; COMPUTER GRAPHICS; MASS SPECTROMETRY; PROCEDURES; PROTEIN DATABASE;

COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; DATABASES, PROTEIN; MACHINE LEARNING; MASS SPECTROMETRY; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEINS; PROTEOMICS; SOFTWARE; WORKFLOW;

EID: 84976274986     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3901     Document Type: Review

References (76)

1. Altelaar, A.F., Munoz, J. & Heck, A.J. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat. Rev. Genet. 14, 35-48 (2013).
2. Cox, J. & Mann, M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem. 80, 273-299 (2011).
3. Eng, J.K., McCormack, A.L. & Yates, J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976-989 (1994).
4. Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551-3567 (1999).
5. Geer, L.Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958-964 (2004).
6. Craig, R. & Beavis, R.C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466-1467 (2004).
7. Bern, M., Cai, Y. & Goldberg, D. Lookup peaks: a hybrid of de novo sequencing and database search for protein identification by tandem mass spectrometry. Anal. Chem. 79, 1393-1400 (2007).
8. Craig, R., Cortens, J.P. & Beavis, R.C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234-1242 (2004).
9. Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787-797 (2007).
10. Deutsch, E.W. et al. Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin. Appl. 9, 745-754 (2015).
11. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367-1372 (2008).
12. Vizcaino, J.A. et al. The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41, D1063-D1069 (2013).
13. Vizcaíno, J.A. et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat. Biotechnol. 32, 223-226 (2014).
14. de Godoy, L.M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251-1254 (2008).
15. Hebert, A.S. et al. The one hour yeast proteome. Mol. Cell. Proteomics 13, 339-347 (2014).
16. Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011).
17. Beck, M. et al. The quantitative proteome of a human cell line. Mol. Syst. Biol. 7, 549 (2011).
18. Munoz, J. et al. The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells. Mol. Syst. Biol. 7, 550 (2011).
19. Mann, M., Kulak, N.A., Nagaraj, N. & Cox, J. The coming age of complete, accurate, and ubiquitous proteomes. Mol. Cell 49, 583-590 (2013).
20. Wísniewski, J.R., Hein, M.Y., Cox, J. & Mann, M.A. 'Proteomic ruler' for protein copy number and concentration estimation without spike-in standards. Mol. Cell. Proteomics 13, 3497-3506 (2014).
21. Cox, J. et al. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, yermed MaxLFQ. Mol. Cell. Proteomics 13, 2513-2526 (2014).
22. Geiger, T., Cox, J., Ostasiewicz, P., Wisniewski, J.R. & Mann, M. Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat. Methods 7, 383-385 (2010).
23. Tusher, V.G., Tibshirani, R. & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA 98, 5116-5121 (2001).
24. Alter, O., Brown, P.O. & Botstein, D. Singular value decomposition for genome-wide expression data processing and modeling. Proc. Natl. Acad. Sci. USA 97, 10101-10106 (2000).
25. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550 (2005).
26. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289-300 (1995).
27. Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115-121 (2015).
28. Beausoleil, S.A., Villén, J., Gerber, S.A., Rush, J. & Gygi, S.P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 24, 1285-1292 (2006).
29. Cox, J. et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J. Proteome Res. 10, 1794-1805 (2011).
30. Olsen, J.V. et al. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 3, ra3 (2010).
31. Sharma, K. et al. Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583-1594 (2014).
32. Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049-D1056 (2015).
33. Hornbeck, P.V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512-D520 (2015).
34. Tyanova, S., Cox, J., Olsen, J., Mann, M. & Frishman, D. Phosphorylation variation during the cell cycle scales with structural propensities of proteins. PLoS Comput. Biol. 9, e1002842 (2013).
35. Hein, M.Y. et al. A human interactome in three quantitative dimensions organized by stoichiometries and abundances. Cell 163, 712-723 (2015).
36. Huttlin, E.L. et al. The BioPlex network: a systematic exploration of the human interactome. Cell 162, 425-440 (2015).
37. Hubner, N.C. et al. Quantitative proteomics combined with BAC TransgeneOmics reveals in vivo protein interactions. J. Cell Biol. 189, 739-754 (2010).
38. Selbach, M. & Mann, M. Protein interaction screening by quantitative immunoprecipitation combined with knockdown (QUICK). Nat. Methods 3, 981-983 (2006).
39. Keilhauer, E.C., Hein, M.Y. & Mann, M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol. Cell. Proteomics 14, 120-135 (2015).
40. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498-2504 (2003).
41. Räschle, M. et al. DNA repair. Proteomics reveals dynamic assembly of repair complexes during bypass of DNA cross-links. Science 348, 1253671 (2015).
42. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273-3297 (1998).
43. Gauthier, N.P. et al. Cyclebase.org - A comprehensive multi-organism online database of cell-cycle experiments. Nucleic Acids Res. 36, D854-D859 (2008).
44. Eser, P. et al. Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression. Mol. Syst. Biol. 10, 717 (2014).
45. Partch, C.L., Green, C.B. & Takahashi, J.S. Molecular architecture of the mammalian circadian clock. Trends Cell Biol. 24, 90-99 (2014).
46. Robles, M.S., Cox, J. & Mann, M. In vivo quantitative proteomics reveals a key contribution of post-transcriptional mechanisms to the circadian regulation of liver metabolism. PLoS Genet. 10, e1004047 (2014).
47. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63 (2009).
48. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
49. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15-21 (2013).
50. 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).
51. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621-628 (2008).
52. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337-342 (2011).
53. Aviner, R., Shenoy, A., Elroy-Stein, O. & Geiger, T. Uncovering hidden layers of cell cycle regulation through integrative multi-omic analysis. PLoS Genet. 11, e1005554 (2015).
54. Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710-D716 (2016).
55. Cox, J. & Mann, M. 1D and 2D annotation enrichment: a statistical method integrating quantitative proteomics with complementary high-throughput data. BMC Bioinformatics 13, S12 (2012).
56. Deeb, S.J. et al. Machine learning-based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol. Cell. Proteomics 14, 2497-2460 (2015).
57. Iglesias-Gato, D. et al. The proteome of primary prostate cancer. Eur. Urol. 69, 942-952 (2016).
58. Tyanova, S. et al. Proteomic maps of breast cancer subtypes. Nat Commun. 7, 10259 (2016).
59. Vapnik, V.N. The Nature of Statistical Learning Theory (Springer, 1995).
60. Chang, C.-C. & Lin, C.-J. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1-27 (2011).
61. Hastie, T., Tibshirani, R. & Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, 2001).
62. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, Article17 (2005).
63. Ideker, T. & Krogan, N.J. Differential network biology. Mol. Syst. Biol. 8, 565 (2012).
64. Creixell, P. et al. Pathway and network analysis of cancer genomes. Nat. Methods 12, 615-621 (2015).
65. Hoops, S. et al. COPASI-a COmplex PAthway SImulator. Bioinformatics 22, 3067-3074 (2006).
66. Angermann, B.R. et al. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat. Methods 9, 283-289 (2012).
67. Cowan, A.E., Moraru, II., Schaff, J.C., Slepchenko, B.M. & Loew, L.M. Spatial modeling of cell signaling networks. Methods Cell Biol. 110, 195-221 (2012).
68. Croft, D. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 42, D472-D477 (2014).
69. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457-D462 (2016).
70. Tyanova, S. et al. Visualization of LC-MS/MS proteomics data in MaxQuant. Proteomics 15, 1453-1456 (2015).
71. Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299-314 (1996).
72. Troyanskaya, O. et al. Missing value estimation methods for DNA microarrays. Bioinformatics 17, 520-525 (2001).
73. Liew, A.W., Law, N.F. & Yan, H. Missing value imputation for gene expression data: computational techniques to recover missing data from available information. Brief. Bioinform. 12, 498-513 (2011).
74. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204-D212 (2015).
75. Hosp, F. et al. A double-barrel liquid chromatography-tandem mass spectrometry (LC-MS/MS) system to quantify 96 interactomes per day. Mol. Cell. Proteomics 14, 2030-2041 (2015).
76. Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012).