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Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data

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Proteome Bioinformatics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1549))

Abstract

High-throughput proteomics studies generate large amounts of data. Biological interpretation of these large scale datasets is often challenging. Over the years, several computational tools have been developed to facilitate meaningful interpretation of large-scale proteomics data. In this chapter, we describe various analyses that can be performed and bioinformatics tools and resources that enable users to do the analyses. Many Web-based and stand-alone tools are relatively user-friendly and can be used by most biologists without significant assistance.

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References

  1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25(1):25–29. doi:10.1038/75556

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pathan M, Keerthikumar S, Ang CS, Gangoda L, Quek CY, Williamson NA, Mouradov D, Sieber OM, Simpson RJ, Salim A, Bacic A, Hill AF, Stroud DA, Ryan MT, Agbinya JI, Mariadason JM, Burgess AW, Mathivanan S (2015) FunRich: an open access standalone functional enrichment and interaction network analysis tool. Proteomics 15(15):2597–2601. doi:10.1002/pmic.201400515

    Article  CAS  PubMed  Google Scholar 

  3. Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(5):P3

    Article  PubMed  Google Scholar 

  4. Kanehisa M, Goto S (2000) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28(1):27–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Nishimura D (2004) BioCarta. Biotech Software Internet Report 2:117–120. doi: 10.1089/152791601750294344

  6. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102(43):15545–15550. doi:10.1073/pnas.0506580102

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey KJ, Riss J, Barrett JC, Weinstein JN (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol 4(4):R28

    Article  PubMed  PubMed Central  Google Scholar 

  8. Beissbarth T, Speed TP (2004) GOstat: find statistically overrepresented gene ontologies within a group of genes. Bioinformatics 20(9):1464–1465. doi:10.1093/bioinformatics/bth088

    Article  CAS  PubMed  Google Scholar 

  9. Martin D, Brun C, Remy E, Mouren P, Thieffry D, Jacq B (2004) GOToolBox: functional analysis of gene datasets based on Gene Ontology. Genome Biol 5(12):R101. doi:10.1186/gb-2004-5-12-r101

    Article  PubMed  PubMed Central  Google Scholar 

  10. Castillo-Davis CI, Hartl DL (2003) GeneMerge—post-genomic analysis, data mining, and hypothesis testing. Bioinformatics 19(7):891–892

    Article  CAS  PubMed  Google Scholar 

  11. Boyle EI, Weng S, Gollub J, Jin H, Botstein D, Cherry JM, Sherlock G (2004) GO::TermFinder—open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 20(18):3710–3715. doi:10.1093/bioinformatics/bth456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Du Z, Zhou X, Ling Y, Zhang Z, Su Z (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38(Web Server Issue):64–70. doi:10.1093/nar/gkq310

    Article  Google Scholar 

  13. Al-Shahrour F, Minguez P, Tarraga J, Medina I, Alloza E, Montaner D, Dopazo J (2007) FatiGO +: a functional profiling tool for genomic data. Integration of functional annotation, regulatory motifs and interaction data with microarray experiments. Nucleic Acids Res 35(Web Server Issue):91–96. doi:10.1093/nar/gkm260

    Article  Google Scholar 

  14. Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 33(Database issue):D428–D432. doi:10.1093/nar/gki072

    Article  CAS  PubMed  Google Scholar 

  15. Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S, Vingron M, Roechert B, Roepstorff P, Valencia A, Margalit H, Armstrong J, Bairoch A, Cesareni G, Sherman D, Apweiler R (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32(Database issue):D452–D455. doi:10.1093/nar/gkh052

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kandasamy K, Mohan SS, Raju R, Keerthikumar S, Kumar GS, Venugopal AK, Telikicherla D, Navarro JD, Mathivanan S, Pecquet C, Gollapudi SK, Tattikota SG, Mohan S, Padhukasahasram H, Subbannayya Y, Goel R, Jacob HK, Zhong J, Sekhar R, Nanjappa V, Balakrishnan L, Subbaiah R, Ramachandra YL, Rahiman BA, Prasad TS, Lin JX, Houtman JC, Desiderio S, Renauld JC, Constantinescu SN, Ohara O, Hirano T, Kubo M, Singh S, Khatri P, Draghici S, Bader GD, Sander C, Leonard WJ, Pandey A (2010) NetPath: a public resource of curated signal transduction pathways. Genome Biol 11(1):R3. doi:10.1186/gb-2010-11-1-r3

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mi H, Poudel S, Muruganujan A, Casagrande JT, Thomas PD (2016) PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44(D1):D336–D342. doi:10.1093/nar/gkv1194

    Article  PubMed  Google Scholar 

  18. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucleic Acids Res 31(1):258–261

    Article  Google Scholar 

  19. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G (2002) MINT: a molecular INTeraction database. FEBS Lett 513(1):135–140

    Article  CAS  PubMed  Google Scholar 

  20. Linding R, Jensen LJ, Pasculescu A, Olhovsky M, Colwill K, Bork P, Yaffe MB, Pawson T (2008) NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res 36(Database issue):D695–D699. doi:10.1093/nar/gkm902

    CAS  PubMed  Google Scholar 

  21. Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, Rashmi BP, Shanker K, Padma N, Niranjan V, Harsha HC, Talreja N, Vrushabendra BM, Ramya MA, Yatish AJ, Joy M, Shivashankar HN, Kavitha MP, Menezes M, Choudhury DR, Ghosh N, Saravana R, Chandran S, Mohan S, Jonnalagadda CK, Prasad CK, Kumar-Sinha C, Deshpande KS, Pandey A (2004) Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 32(Database issue):D497–D501. doi:10.1093/nar/gkh070

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Diella F, Cameron S, Gemund C, Linding R, Via A, Kuster B, Sicheritz-Ponten T, Blom N, Gibson TJ (2004) Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 5:79. doi:10.1186/1471-2105-5-79

    Article  PubMed  PubMed Central  Google Scholar 

  23. Garavelli JS (2004) The RESID database of protein modifications as a resource and annotation tool. Proteomics 4(6):1527–1533. doi:10.1002/pmic.200300777

    Article  CAS  PubMed  Google Scholar 

  24. Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E (2015) PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res 43(Database issue):D512–D520. doi:10.1093/nar/gku1267

    Article  PubMed  Google Scholar 

  25. Gupta R, Birch H, Rapacki K, Brunak S, Hansen JE (1999) O-GLYCBASE version 4.0: a revised database of O-glycosylated proteins. Nucleic Acids Res 27(1):370–372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Creasy DM, Cottrell JS (2004) Unimod: protein modifications for mass spectrometry. Proteomics 4(6):1534–1536. doi:10.1002/pmic.200300744

    Article  CAS  PubMed  Google Scholar 

  27. Gnad F, Ren S, Cox J, Olsen JV, Macek B, Oroshi M, Mann M (2007) PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol 8(11):R250. doi:10.1186/gb-2007-8-11-r250

    Article  PubMed  PubMed Central  Google Scholar 

  28. Huang HD, Lee TY, Tzeng SW, Horng JT (2005) KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Res 33(Web Server Issue):226–229. doi:10.1093/nar/gki471

    Article  Google Scholar 

  29. Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294(5):1351–1362. doi:10.1006/jmbi.1999.3310

    Article  CAS  PubMed  Google Scholar 

  30. Iakoucheva LM, Radivojac P, Brown CJ, O'Connor TR, Sikes JG, Obradovic Z, Dunker AK (2004) The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res 32(3):1037–1049. doi:10.1093/nar/gkh253

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Kiemer L, Bendtsen JD, Blom N (2005) NetAcet: prediction of N-terminal acetylation sites. Bioinformatics 21(7):1269–1270. doi:10.1093/bioinformatics/bti130

    Article  CAS  PubMed  Google Scholar 

  32. Lee TY, Hsu JB, Lin FM, Chang WC, Hsu PC, Huang HD (2010) N-Ace: using solvent accessibility and physicochemical properties to identify protein N-acetylation sites. J Comput Chem 31(15):2759–2771. doi:10.1002/jcc.21569

    Article  CAS  PubMed  Google Scholar 

  33. Suo SB, Qiu JD, Shi SP, Sun XY, Huang SY, Chen X, Liang RP (2012) Position-specific analysis and prediction for protein lysine acetylation based on multiple features. PLoS One 7(11), e49108. doi:10.1371/journal.pone.0049108

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Li Y, Wang M, Wang H, Tan H, Zhang Z, Webb GI, Song J (2014) Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features. Sci Rep 4:5765. doi:10.1038/srep05765

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Radivojac P, Vacic V, Haynes C, Cocklin RR, Mohan A, Heyen JW, Goebl MG, Iakoucheva LM (2010) Identification, analysis, and prediction of protein ubiquitination sites. Proteins 78(2):365–380. doi:10.1002/prot.22555

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Tung CW, Ho SY (2008) Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 9:310. doi: 10.1186/1471-2105-9-310

  37. Lee H, Yi GS, Park JC (2008) E3Miner: a text mining tool for ubiquitin-protein ligases. Nucleic Acids Res 36(Web Server Issue):416–422. doi:10.1093/nar/gkn286

    Article  Google Scholar 

  38. Chen Z, Zhou Y, Song J, Zhang Z (2013) hCKSAAP_UbSite: improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties. Biochim Biophys Acta 1834(8):1461–1467. doi:10.1016/j.bbapap.2013.04.006

    Article  CAS  PubMed  Google Scholar 

  39. Qiu WR, Xiao X, Lin WZ, Chou KC (2015) iUbiq-Lys: prediction of lysine ubiquitination sites in proteins by extracting sequence evolution information via a gray system model. J Biomol Struct Dyn 33(8):1731–1742. doi:10.1080/07391102.2014.968875

    Article  CAS  PubMed  Google Scholar 

  40. Du Y, Xu N, Lu M, Li T (2011) hUbiquitome: a database of experimentally verified ubiquitination cascades in humans. Database (Oxford) 2011:bar055. doi:10.1093/database/bar055

    Article  Google Scholar 

  41. Eifler K, Vertegaal AC (2015) Mapping the SUMOylated landscape. FEBS J 282(19):3669–3680. doi:10.1111/febs.13378

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Xue Y, Zhou F, Fu C, Xu Y, Yao X (2006) SUMOsp: a web server for sumoylation site prediction. Nucleic Acids Res 34(Web Server Issue):254–257. doi:10.1093/nar/gkl207

    Article  Google Scholar 

  43. Zhao Q, Xie Y, Zheng Y, Jiang S, Liu W, Mu W, Liu Z, Zhao Y, Xue Y, Ren J (2014) GPS-SUMO: a tool for the prediction of sumoylation sites and SUMO-interaction motifs. Nucleic Acids Res 42(Web Server Issue):325–330. doi:10.1093/nar/gku383

    Article  Google Scholar 

  44. Caragea C, Sinapov J, Silvescu A, Dobbs D, Honavar V (2007) Glycosylation site prediction using ensembles of Support Vector Machine classifiers. BMC Bioinformatics 8:438. doi:10.1186/1471-2105-8-438

    Article  PubMed  PubMed Central  Google Scholar 

  45. Julenius K (2007) NetCGlyc 1.0: prediction of mammalian C-mannosylation sites. Glycobiology 17(8):868–876. doi:10.1093/glycob/cwm050

    Article  CAS  PubMed  Google Scholar 

  46. Hansen JE, Lund O, Tolstrup N, Gooley AA, Williams KL, Brunak S (1998) NetOglyc: prediction of mucin type O-glycosylation sites based on sequence context and surface accessibility. Glycoconj J 15(2):115–130

    Article  CAS  PubMed  Google Scholar 

  47. Gupta R, Jung E, Brunak S (2004) NetNGlyc 1.0 Server. Center for biological sequence analysis, technical university of Denmark (http://wwwcbsdtudk/services/NetNGlyc)

  48. Pierleoni A, Martelli PL, Casadio R (2008) PredGPI: a GPI-anchor predictor. BMC Bioinformatics 9:392. doi:10.1186/1471-2105-9-392

    Article  PubMed  PubMed Central  Google Scholar 

  49. Fankhauser N, Maser P (2005) Identification of GPI anchor attachment signals by a Kohonen self-organizing map. Bioinformatics 21(9):1846–1852. doi:10.1093/bioinformatics/bti299

    Article  CAS  PubMed  Google Scholar 

  50. Obenauer JC, Cantley LC, Yaffe MB (2003) Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res 31(13):3635–3641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chou MF, Schwartz D (2011) Biological sequence motif discovery using motif-x. Curr Protoc Bioinformatics 13:15–24. doi:10.1002/0471250953.bi1315s35

    PubMed  Google Scholar 

  52. Hummel J, Niemann M, Wienkoop S, Schulze W, Steinhauser D, Selbig J, Walther D, Weckwerth W (2007) ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites. BMC Bioinformatics 8:216. doi:10.1186/1471-2105-8-216

    Article  PubMed  PubMed Central  Google Scholar 

  53. Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR (2002) GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 31(1):19–20. doi:10.1038/ng0502-19

    Article  CAS  PubMed  Google Scholar 

  54. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. doi:10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hooper SD, Bork P (2005) Medusa: a simple tool for interaction graph analysis. Bioinformatics 21(24):4432–4433. doi:10.1093/bioinformatics/bti696

    Article  CAS  PubMed  Google Scholar 

  56. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein M, Geiger T, Mann M and Cox J (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 3(9):731–740. doi: 10.1038/nmeth.3901

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Authors and Affiliations

  1. Institute of Bioinformatics, Discoverer Building, International Technology Park, Whitefield, Bangalore, 560066, India

    Krishna Patel, Manika Singh & Harsha Gowda

  2. Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Kollam, India

    Krishna Patel

  3. YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore, India

    Harsha Gowda

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  1. Krishna Patel
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  2. Manika Singh
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  3. Harsha Gowda
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Correspondence to Harsha Gowda .

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Editors and Affiliations

  1. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Shivakumar Keerthikumar

  2. Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia

    Suresh Mathivanan

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Patel, K., Singh, M., Gowda, H. (2017). Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data. In: Keerthikumar, S., Mathivanan, S. (eds) Proteome Bioinformatics. Methods in Molecular Biology, vol 1549. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6740-7_12

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  • DOI: https://doi.org/10.1007/978-1-4939-6740-7_12

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6738-4

  • Online ISBN: 978-1-4939-6740-7

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