The potential clinical impact of the release of two drafts of the human proteome

Expert Review of Proteomics

Volumn 12, Issue 6, 2015, Pages 579-593

  Ezkurdia, Iakes ^a   Calvo, Enrique ^a   Del Pozo, Angela ^b   Vázquez, Jesús ^a   Valencia, Alfonso ^c   Tress, Michael L ^c  

^a CENTRO NACIONAL DE INVESTIGACIONES CARDIOVASCULARES CNIC   (Spain)

^b HOSPITAL UNIVERSITARIO LA PAZ IDIPAZ   (Spain)

^c Structural Biology and Biocomputing Programme   (Spain)

Author keywords

Clinical applications; false discovery rates; human proteome; protein coding genes; proteomics

Indexed keywords

PEPTIDE; PROTEOME; SYNTHETIC PEPTIDE;

AMINO ACID SEQUENCE; GENE IDENTIFICATION; GENETIC CODE; HUMAN GENOME; NONHUMAN; OLFACTORY RECEPTOR; PEPTIDE MAPPING; PROTEIN ANALYSIS; PROTEOMICS; QUALITY CONTROL; REVIEW; GENETICS; HUMAN; PROTEIN DATABASE;

DATABASES, PROTEIN; HUMANS; PEPTIDES; PROTEOME; PROTEOMICS;

EID: 84947495912     PISSN: 14789450     EISSN: 17448387     Source Type: Journal    
DOI: 10.1586/14789450.2015.1103186     Document Type: Review

References (60)

1. Kim MS, Pinto SM, Getnet D, et al. A draft map of the human proteome. Nature. 2014; 509: 575-581.
2. Wilhelm M, Schlegl J, Hahne H, et al. Mass-spectrometry-based draft of the human proteome. Nature. 2014; 509: 582-587.
3. Venter JC, Adams MD, Myers EW, et al. The sequence of the human genome. Science. 2001; 291: 1304-1351.
4. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001; 409: 860-921.
5. Koenig T, Menze BH, Kirchner M, et al. Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics. J Proteome Res. 2008; 7: 3708-3717.
6. Cox J, Neuhauser N, Michalski A, et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011; 10: 1794-1805.
7. UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 2015; 43: D204-212.
8. Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Am Soc Mass Spectrom. 1994; 5: 976-989.
9. Pruitt KD, Brown GR, Hiatt SM, et al. RefSeq: an update on mammalian reference sequences. Nucleic Acids Res. 2014; 42: D756-763.
10. Farrah T, Deutsch EW, Hoopmann MR, et al. The state of the human proteome in 2012 as viewed through PeptideAtlas. J Proteome Res. 2013; 12: 162-171.
11. Fenyö D, Eriksson J, Beavis R. Mass spectrometric protein identification using the global proteome machine. Methods Mol Biol. 2010; 673: 189-202.
12. Shiromizu T, Adachi J, Watanabe S, et al. Identification of missing proteins in the neXtProt database and unregistered phosphopeptides in the PhosphoSitePlus database as part of the chromosome-centric human proteome project. J Proteome Res. 2013; 12: 2414-2421.
13. Geiger T, Wehner A, Schaab C, et al. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics. 2012; 11: M111.014050-M111.014050.
14. Ezkurdia I, Juan D, Rodriguez JM, et al. Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet. 2014; 23: 5866-5878.
15. Harrow J, Frankish A, Gonzalez JM, et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 2012; 22: 760-774.
16. NCBI Resource Coordinators. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2015; 43: D16-17.
17. Verbeurgt C, Wilkin F, Tarabichi M, et al. Profiling of olfactory receptor gene expression in whole human olfactory mucosa. PLoS One. 2014; 9: e96333.
18. Deutsch EW, Sun Z, Campbell D, et al. The state of the human proteome in 2014/2015 as viewed through PeptideAtlas: enhancing accuracy and coverage through the AtlasProphet. J Proteome Res. 2015;14:3461-3473.
19. Ezkurdia I, Vázquez J, Valencia A, et al. Analyzing the first drafts of the human proteome. J Proteome Res. 2014; 13: 3854-3855.
20. Ezkurdia I, Vázquez J, Valencia A, et al. Correction to "Analyzing the first drafts of the human proteome". J Proteome Res. 2015; 14: 1991.
21. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007; 4: 207-214.
22. Nesvizhskii AI. A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. J Proteom. 2010; 73: 2092-1223.
23. Serang O, Käll L. Solution to statistical challenges in proteomics is more statistics, not less. J Proteome Res. 2015;14:4099-4103.
24. Reiter L, Claassen M, Schrimpf SP, et al. Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol Cell Proteomics. 2009; 8: 2405-2417.
25. Savitski MM, WIlhelm M, Hahne H, et al. A scalable approach for protein false discovery rate estimation in large proteomic data sets. Mol Cell Proteomics. 2015. DOI: 10.1074/mcp.M114.046995.
26. Gaudet P, Michel PA, Zahn-Zabal M, et al. The neXtProt knowledgebase on human proteins: current status. Nucleic Acids Res. 2015; 43: D764-770.
27. Colaert N, Van Huele C, Degroeve S, et al. Combining quantitative proteomics data processing workflows for greater sensitivity. Nat Methods. 2011; 8: 481-483.
28. Paulo JA. Practical and efficient searching in proteomics: a cross engine comparison. Webmedcentral. 2013; 4: WMCPLS0052.
29. Carr S, Aebersold R, Baldwin M, et al. The need for guidelines in publication of peptide and protein identification data: working group on publication guidelines for peptide and protein identification data. Mol Cell Proteomics. 2004; 3: 531-533.
30. Omenn GS, States DJ, Adamski M, et al. Overview of the HUPO plasma proteome project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics. 2005; 5: 3226-3245.
31. Shteynberg D, Nesvizhskii AI, Moritz RL, et al. Combining results of multiple search engines in proteomics. Mol Cell Proteomics. 2013; 12: 2383-2393.
32. White FM. The potential cost of high-throughput proteomics. Sci Signal. 2011; 4: pe8.
33. Cooper B. The problem with peptide presumption and the downfall of target-decoy false discovery rates. Anal Chem. 2012; 84: 9663-9667.
34. Bonzon-Kulichenko E, Garcia-Marques F, Trevisan-Herraz M, et al. Revisiting peptide identification by high-accuracy mass spectrometry: problems associated with the use of narrow mass precursor windows. J Proteome Res. 2015; 14: 700-710.
35. Omenn GS, Lane L, Lundberg EK, et al. Metrics for the human proteome project 2015: progress on the human proteome and guidelines for high-confidence protein identification. J Proteome Res. 2015;14:3452-3460.
36. Horvatovich P, Lundberg EK, Chen YJ, et al. Quest for missing proteins: update 2015 on chromosome-centric human proteome project. J Proteome Res. 2015;14:3415-3431.
37. Nesvizhskii AI. Proteogenomics: concepts, applications and computational strategies. Nat Methods. 2014; 11: 1114-1125.
38. Krug K, Carpy A, Behrends G, et al. Deep coverage of the Escherichia coli proteome enables the assessment of false discovery rates in simple proteogenomic experiments. Mol Cell Proteomics. 2013; 12: 3420-3430.
39. Ross PL, Huang YN, Marchese JN, et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics. 2004; 3: 1154-1169.
40. Ezkurdia I, Rodriguez JM, Carrillo-de Santa Pau E, et al. Most highly expressed protein-coding genes have a single dominant isoform. J Proteome Res. 2015; 14: 1880-1887.
41. Abascal F, Ezkurdia I, Rodriguez-Rivas J, et al. Alternatively spliced homologous exons have ancient origins and are highly expressed at the protein level. PLoS Comput Biol. 2015; 11: e1004325.
42. Huang Da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009; 4: 44-57.
43. Uhlén M, Fagerberg L, Hallström BM, et al. Proteomics. Tissue-based map of the human proteome. Science. 2015; 347: 1260419.
44. Zhang B, Wang J, Wang X, et al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014; 513: 382-387.
45. Narayanan R. Phenome-genome association studies of pancreatic cancer: new targets for therapy and diagnosis. Cancer Genom Proteom. 2015; 12: 9-19.
46. Narayanan R. Ebola-associated genes in the human genome: implications for novel targets. MOJ Proteom Bioinform. 2015; 1: 00032.
47. Shao S, Guo T, Aebersold R. Mass spectrometry-based proteomic quest for diabetes biomarkers. Biochim Biophys Acta. 2015; 1854: 519-527.
48. Hathout Y. Proteomic methods for biomarker discovery and validation. Are we there yet? Expert Rev Proteom. 2015; 12: 329-331.
49. Aebersold R, Bader GD, Edwards AM, et al. The biology/disease-driven human proteome project (B/D-HPP): enabling protein research for the life sciences community. J Proteome Res. 2013; 12: 23-27.
50. Zhang K, Fu Y, Zeng WF, et al. A note on the false discovery rate of novel peptides in proteogenomics. Bioinformatics. 2015; 31: 3249-3253.
51. Ma J, Ward CC, Jungreis I, et al. Discovery of human sORF-encoded polypeptides (SEPs) in cell lines and tissue. J Proteome Res. 2014; 13: 1757-1765.
52. Vanderperre B, Lucier JF, Bissonnette C, et al. Direct detection of alternative open reading frames translation products in human significantly expands the proteome. PLoS One. 2013; 8: e70698.
53. Brusniak MY, Chu CS, Kusebauch U, et al. An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology. Proteomics. 2012; 12: 1176-1184.
54. Hao Y, Colak R, Teyra J, et al. Semi-supervised learning predicts approximately one third of the alternative splicing isoforms as functional proteins. Cell Rep. 2015; 12: 183-189.
55. Fu Y, Qian X. Transferred subgroup false discovery rate for rare post-translational modifications detected by mass spectrometry. Mol Cell Proteomics. 2014; 13: 1359-1368.
56. Chu Q, Ma J, Saghatelian A. Identification and characterization of sORF-encoded polypeptides. Crit Rev Biochem Mol Biol. 2015; 50: 134-141.
57. Guttman M, Russell P, Ingolia NT, et al. Ribosome profiling provides evidence that large noncoding RNAs do not encode proteins. Cell. 2013; 154: 240-251.
58. Ruiz-Orera J, Messeguer X, Subirana JA, et al. Long non-coding RNAs as a source of new peptides. Elife. 2014; 3: e03523.
59. Griss J, Perez-Riverol Y, Hermjakob H, et al. Identifying novel biomarkers through data mining-a realistic scenario? Proteom Clin Appl. 2015; 9: 437-443.
60. Khatun J, Yu Y, Wrobel JA, et al. Whole human genome proteogenomic mapping for ENCODE cell line data: identifying protein-coding regions. BMC Genom. 2013; 14: 141.