Skip to main content
SpringerLink
Log in
Book cover

Proteogenomics pp 65–75Cite as

Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies

  • Chapter
  • First Online:

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 926))

Abstract

Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Aebersold, R., & Mann, M. (2003). Mass spectrometry-based proteomics. Nature, 422(6928), 198–207. doi:10.1038/nature01511.

    Article  CAS  PubMed  Google Scholar 

  • Afgan, E., Chapman, B., & Taylor, J. (2012). CloudMan as a platform for tool, data, and analysis distribution. BMC Bioinformatics, 13, 315. doi:10.1186/1471-2105-13-315.

    Article  PubMed  PubMed Central  Google Scholar 

  • Alfaro, J. A., Sinha, A., Kislinger, T., & Boutros, P. C. (2014). Onco-proteogenomics: cancer proteomics joins forces with genomics. Nature Methods, 11(11), 1107–1113. doi:10.1038/nmeth.3138.

    Article  CAS  PubMed  Google Scholar 

  • Altelaar, A. F., Munoz, J., & Heck, A. J. (2013). Next-generation proteomics: towards an integrative view of proteome dynamics. Nature Reviews Genetics, 14(1), 35–48. doi:10.1038/nrg3356.

    Article  CAS  PubMed  Google Scholar 

  • Apweiler, R., Bairoch, A., Wu, C. H., Barker, W. C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M. J., Natale, D. A., O’Donovan, C., Redaschi, N., & Yeh, L. S. (2004). UniProt: The Universal Protein knowledgebase. Nucleic Acids Research, 32(Database issue), D115–D119. doi:10.1093/nar/gkh131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Barsnes, H., & Martens, L. (2013). Crowdsourcing in proteomics: Public resources lead to better experiments. Amino Acids, 44(4), 1129–1137. doi:10.1007/s00726-012-1455-z.

    Article  CAS  PubMed  Google Scholar 

  • Beausoleil, S. A., Villen, J., Gerber, S. A., Rush, J., & Gygi, S. P. (2006). A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nature Biotechnology, 24(10), 1285–1292. doi:10.1038/nbt1240.

    Article  CAS  PubMed  Google Scholar 

  • Bern, M., & Kil, Y. J. (2011). Comment on “Unbiased statistical analysis for multi-stage proteomic search strategies”. Journal of Proteome Research, 10(4), 2123–2127. doi:10.1021/pr101143m.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Boekel, J., Chilton, J. M., Cooke, I. R., Horvatovich, P. L., Jagtap, P. D., Kall, L., Lehtio, J., Lukasse, P., Moerland, P. D., & Griffin, T. J. (2015). Multi-omic data analysis using Galaxy. Nature Biotechnology, 33(2), 137–139. doi:10.1038/nbt.3134.

    Article  CAS  PubMed  Google Scholar 

  • Bromenshenk, J. J., Henderson, C. B., Wick, C. H., Stanford, M. F., Zulich, A. W., Jabbour, R. E., Deshpande, S. V., McCubbin, P. E., Seccomb, R. A., Welch, P. M., Williams, T., Firth, D. R., Skowronski, E., Lehmann, M. M., Bilimoria, S. L., Gress, J., Wanner, K. W., & Cramer, R. A., Jr. (2010). Iridovirus and microsporidian linked to honey bee colony decline. PLoS One, 5(10), e13181. doi:10.1371/journal.pone.0013181.

    Article  PubMed  PubMed Central  Google Scholar 

  • Chick, J. M., Kolippakkam, D., Nusinow, D. P., Zhai, B., Rad, R., Huttlin, E. L., & Gygi, S. P. (2015). A mass-tolerant database search identifies a large proportion of unassigned spectra in shotgun proteomics as modified peptides. Nature Biotechnology, 33(7), 743–749. doi:10.1038/nbt.3267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Colaert, N., Degroeve, S., Helsens, K., & Martens, L. (2011). Analysis of the resolution limitations of peptide identification algorithms. Journal of Proteome Research, 10(12), 5555–5561. doi:10.1021/pr200913a.

    Article  CAS  PubMed  Google Scholar 

  • Coordinators, N. R. (2016). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 44(D1), D7–D19. doi:10.1093/nar/gkv1290.

    Article  Google Scholar 

  • Cox, J., & Mann, M. (2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnology, 26(12), 1367–1372. doi:10.1038/nbt.1511.

    Article  CAS  PubMed  Google Scholar 

  • Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011). Andromeda: A peptide search engine integrated into the MaxQuant environment. Journal of Proteome Research, 10(4), 1794–1805. doi:10.1021/pr101065j.

    Article  CAS  PubMed  Google Scholar 

  • Craig, R., & Beavis, R. C. (2004). TANDEM: Matching proteins with tandem mass spectra. Bioinformatics, 20(9), 1466–1467. doi:10.1093/bioinformatics/bth092.

    Article  CAS  PubMed  Google Scholar 

  • Craig, R., Cortens, J. P., & Beavis, R. C. (2004). Open source system for analyzing, validating, and storing protein identification data. Journal of Proteome Research, 3(6), 1234–1242. doi:10.1021/pr049882h.

    Article  CAS  PubMed  Google Scholar 

  • Crappe, J., Ndah, E., Koch, A., Steyaert, S., Gawron, D., De Keulenaer, S., De Meester, E., De Meyer, T., Van Criekinge, W., Van Damme, P., & Menschaert, G. (2015). PROTEOFORMER: Deep proteome coverage through ribosome profiling and MS integration. Nucleic Acids Research, 43(5), e29. doi:10.1093/nar/gku1283.

    Article  PubMed  Google Scholar 

  • Croft, D., O’Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., Jupe, S., Kalatskaya, I., Mahajan, S., May, B., Ndegwa, N., Schmidt, E., Shamovsky, V., Yung, C., Birney, E., Hermjakob, H., D’Eustachio, P., & Stein, L. (2011). Reactome: A database of reactions, pathways and biological processes. Nucleic Acids Research, 39(Database issue), D691–D697. doi:10.1093/nar/gkq1018.

    Article  CAS  PubMed  Google Scholar 

  • Desiere, F., Deutsch, E. W., King, N. L., Nesvizhskii, A. I., Mallick, P., Eng, J., Chen, S., Eddes, J., Loevenich, S. N., & Aebersold, R. (2006). The PeptideAtlas project. Nucleic Acids Research, 34(Database issue), D655–D658. doi:10.1093/nar/gkj040.

    Article  CAS  PubMed  Google Scholar 

  • Deutsch, E. W., Mendoza, L., Shteynberg, D., Farrah, T., Lam, H., Tasman, N., Sun, Z., Nilsson, E., Pratt, B., Prazen, B., Eng, J. K., Martin, D. B., Nesvizhskii, A. I., & Aebersold, R. (2010). A guided tour of the trans-proteomic pipeline. Proteomics, 10(6), 1150–1159. doi:10.1002/pmic.200900375.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Diament, B. J., & Noble, W. S. (2011). Faster SEQUEST searching for peptide identification from tandem mass spectra. Journal of Proteome Research, 10(9), 3871–3879. doi:10.1021/pr101196n.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Domon, B., & Aebersold, R. (2006). Mass spectrometry and protein analysis. Science, 312(5771), 212–217. doi:10.1126/science.1124619.

    Article  CAS  PubMed  Google Scholar 

  • Dorfer, V., Pichler, P., Stranzl, T., Stadlmann, J., Taus, T., Winkler, S., & Mechtler, K. (2014). MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. Journal of Proteome Research, 13(8), 3679–3684. doi:10.1021/pr500202e.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Elias, J. E., & Gygi, S. P. (2010). Target-decoy search strategy for mass spectrometry-based proteomics. Methods in Molecular Biology, 604, 55–71. doi:10.1007/978-1-60761-444-9_5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Eng, J. K., Jahan, T. A., & Hoopmann, M. R. (2013). Comet: An open-source MS/MS sequence database search tool. Proteomics, 13(1), 22–24. doi:10.1002/pmic.201200439.

    Article  CAS  PubMed  Google Scholar 

  • Everett, L. J., Bierl, C., & Master, S. R. (2010). Unbiased statistical analysis for multi-stage proteomic search strategies. Journal of Proteome Research, 9(2), 700–707. doi:10.1021/pr900256v.

    Article  CAS  PubMed  Google Scholar 

  • Fan, J., Saha, S., Barker, G., Heesom, K. J., Ghali, F., Jones, A. R., Matthews, D. A., & Bessant, C. (2015). Galaxy integrated Omics: Web-based standards-compliant workflows for proteomics informed by transcriptomics. Molecular & Cellular Proteomics, 14(11), 3087–3093. doi:10.1074/mcp.O115.048777.

    Article  CAS  Google Scholar 

  • French, W. R., Zimmerman, L. J., Schilling, B., Gibson, B. W., Miller, C. A., Townsend, R. R., Sherrod, S. D., Goodwin, C. R., McLean, J. A., & Tabb, D. L. (2015). Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard’s msConvert. Journal of Proteome Research, 14(2), 1299–1307. doi:10.1021/pr500886y.

    Article  CAS  PubMed  Google Scholar 

  • Giardine, B., Riemer, C., Hardison, R. C., Burhans, R., Elnitski, L., Shah, P., Zhang, Y., Blankenberg, D., Albert, I., Taylor, J., Miller, W., Kent, W. J., & Nekrutenko, A. (2005). Galaxy: A platform for interactive large-scale genome analysis. Genome Research, 15(10), 1451–1455. doi:10.1101/gr.4086505.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Griss, J., Foster, J. M., Hermjakob, H., & Vizcaino, J. A. (2013). PRIDE Cluster: Building a consensus of proteomics data. Nature Methods, 10(2), 95–96. doi:10.1038/nmeth.2343.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Helsens, K., Timmerman, E., Vandekerckhove, J., Gevaert, K., & Martens, L. (2008). Peptizer, a tool for assessing false positive peptide identifications and manually validating selected results. Molecular & Cellular Proteomics, 7(12), 2364–2372. doi:10.1074/mcp.M800082-MCP200.

    Article  CAS  Google Scholar 

  • Henry, V. J., Bandrowski, A. E., Pepin, A. S., Gonzalez, B. J., & Desfeux, A. (2014). OMICtools: An informative directory for multi-omic data analysis. Database. doi:10.1093/database/bau069.

    PubMed  PubMed Central  Google Scholar 

  • Hubbard, T., Barker, D., Birney, E., Cameron, G., Chen, Y., Clark, L., Cox, T., Cuff, J., Curwen, V., Down, T., Durbin, R., Eyras, E., Gilbert, J., Hammond, M., Huminiecki, L., Kasprzyk, A., Lehvaslaiho, H., Lijnzaad, P., Melsopp, C., Mongin, E., Pettett, R., Pocock, M., Potter, S., Rust, A., Schmidt, E., Searle, S., Slater, G., Smith, J., Spooner, W., Stabenau, A., Stalker, J., Stupka, E., Ureta-Vidal, A., Vastrik, I., & Clamp, M. (2002). The Ensemble genome database project. Nucleic Acids Research, 30(1), 38–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hulstaert, N., Reisinger, F., Rameseder, J., Barsnes, H., Vizcaino, J. A., & Martens, L. (2013). Pride-asap: Automatic fragment ion annotation of identified PRIDE spectra. Journal of Proteomics, 95, 89–92. doi:10.1016/j.jprot.2013.04.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jaffe, J. D., Berg, H. C., & Church, G. M. (2004). Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics, 4(1), 59–77. doi:10.1002/pmic.200300511.

    Article  CAS  PubMed  Google Scholar 

  • Jagtap, P. D., Johnson, J. E., Onsongo, G., Sadler, F. W., Murray, K., Wang, Y., Shenykman, G. M., Bandhakavi, S., Smith, L. M., & Griffin, T. J. (2014). Flexible and accessible workflows for improved proteogenomic analysis using the Galaxy framework. Journal of Proteome Research, 13(12), 5898–5908. doi:10.1021/pr500812t.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Keller, A., Nesvizhskii, A. I., Kolker, E., & Aebersold, R. (2002). Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Analytical Chemistry, 74(20), 5383–5392.

    Article  CAS  PubMed  Google Scholar 

  • Kessner, D., Chambers, M., Burke, R., Agus, D., & Mallick, P. (2008). ProteoWizard: Open source software for rapid proteomics tools development. Bioinformatics, 24(21), 2534–2536. doi:10.1093/bioinformatics/btn323.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kim, S., & Pevzner, P. A. (2014). MS-GF+ makes progress towards a universal database search tool for proteomics. Nature Communications, 5, 5277. doi:10.1038/ncomms6277.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Knudsen, G. M., & Chalkley, R. J. (2011). The effect of using an inappropriate protein database for proteomic data analysis. PLoS One, 6(6), e20873. doi:10.1371/journal.pone.0020873.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kohlbacher, O., Reinert, K., Gropl, C., Lange, E., Pfeifer, N., Schulz-Trieglaff, O., & Sturm, M. (2007). TOPP–the OpenMS proteomics pipeline. Bioinformatics, 23(2), e191–e197. doi:10.1093/bioinformatics/btl299.

    Article  CAS  PubMed  Google Scholar 

  • Lange, E., Gropl, C., Reinert, K., Kohlbacher, O., & Hildebrandt, A. (2006). High-accuracy peak picking of proteomics data using wavelet techniques. In Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing, pp. 243–254.

    Google Scholar 

  • Ma, K., Vitek, O., & Nesvizhskii, A. I. (2012). A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinformatics, 13(Suppl 16), S1. doi: 10.1186/1471-2105-13-S16-S1

    Google Scholar 

  • Martens, L., Hermjakob, H., Jones, P., Adamski, M., Taylor, C., States, D., Gevaert, K., Vandekerckhove, J., & Apweiler, R. (2005a). PRIDE: The proteomics identifications database. Proteomics, 5(13), 3537–3545. doi:10.1002/pmic.200401303.

    Article  CAS  PubMed  Google Scholar 

  • Martens, L., Nesvizhskii, A. I., Hermjakob, H., Adamski, M., Omenn, G. S., Vandekerckhove, J., & Gevaert, K. (2005b). Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories. Proteomics, 5(13), 3501–3505. doi:10.1002/pmic.200401302.

    Article  CAS  PubMed  Google Scholar 

  • Menschaert, G., & Fenyo, D. (2015). Proteogenomics from a bioinformatics angle: A growing field. Mass Spectrometry Reviews. doi:10.1002/mas.21483.

    PubMed  Google Scholar 

  • Muth, T., Benndorf, D., Reichl, U., Rapp, E., & Martens, L. (2013a). Searching for a needle in a stack of needles: Challenges in metaproteomics data analysis. Molecular BioSystems, 9(4), 578–585. doi:10.1039/c2mb25415h.

    Article  CAS  PubMed  Google Scholar 

  • Muth, T., Peters, J., Blackburn, J., Rapp, E., & Martens, L. (2013b). ProteoCloud: A full-featured open source proteomics cloud computing pipeline. Journal of Proteomics, 88, 104–108. doi:10.1016/j.jprot.2012.12.026.

    Article  CAS  PubMed  Google Scholar 

  • Nesvizhskii, A. I. (2010). A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. Journal of Proteomics, 73(11), 2092–2123. doi:10.1016/j.jprot.2010.08.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nesvizhskii, A. I. (2014). Proteogenomics: Concepts, applications and computational strategies. Nature Methods, 11(11), 1114–1125. doi:10.1038/nmeth.3144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Nesvizhskii, A. I., & Aebersold, R. (2005). Interpretation of shotgun proteomic data: The protein inference problem. Molecular & Cellular Proteomics, 4(10), 1419–1440. doi:10.1074/mcp.R500012-MCP200.

    Article  CAS  Google Scholar 

  • Noble, W. S. (2015). Mass spectrometrists should search only for peptides they care about. Nature Methods, 12(7), 605–608. doi:10.1038/nmeth.3450.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Olexiouk, V., Crappe, J., Verbruggen, S., Verhegen, K., Martens, L., & Menschaert, G. (2016). sORFs.org: A repository of small ORFs identified by ribosome profiling. Nucleic Acids Research, 44(D1), D324–D329. doi:10.1093/nar/gkv1175.

    Article  PubMed  Google Scholar 

  • Pemovska, T., Kontro, M., Yadav, B., Edgren, H., Eldfors, S., Szwajda, A., Almusa, H., Bespalov, M. M., Ellonen, P., Elonen, E., Gjertsen, B. T., Karjalainen, R., Kulesskiy, E., Lagstrom, S., Lehto, A., Lepisto, M., Lundan, T., Majumder, M. M., Marti, J. M., Mattila, P., Murumagi, A., Mustjoki, S., Palva, A., Parsons, A., Pirttinen, T., Ramet, M. E., Suvela, M., Turunen, L., Vastrik, I., Wolf, M., Knowles, J., Aittokallio, T., Heckman, C. A., Porkka, K., Kallioniemi, O., & Wennerberg, K. (2013). Individualized systems medicine strategy to tailor treatments for patients with chemorefractory acute myeloid leukemia. Cancer Discovery, 3(12), 1416–1429. doi:10.1158/2159-8290.CD-13-0350.

    Article  CAS  PubMed  Google Scholar 

  • Pruitt, K. D., Tatusova, T., & Maglott, D. R. (2005). NCBI Reference Sequence (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Research, 33(Database issue), D501–D504. doi:10.1093/nar/gki025.

    Article  CAS  PubMed  Google Scholar 

  • Risk, B. A., Spitzer, W. J., & Giddings, M. C. (2013). Peppy: Proteogenomic search software. Journal of Proteome Research, 12(6), 3019–3025. doi:10.1021/pr400208w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shanmugam, A. K., & Nesvizhskii, A. I. (2015). Effective leveraging of targeted search spaces for improving peptide identification in Tandem Mass Spectrometry based proteomics. Journal of Proteome Research, 14(12), 5169–5178. doi:10.1021/acs.jproteome.5b00504.

    Article  CAS  PubMed  Google Scholar 

  • Sheynkman, G. M., Johnson, J. E., Jagtap, P. D., Shortreed, M. R., Onsongo, G., Frey, B. L., Griffin, T. J., & Smith, L. M. (2014). Using Galaxy-P to leverage RNA-Seq for the discovery of novel protein variations. BMC Genomics, 15, 703. doi:10.1186/1471-2164-15-703.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sussman, J. L., Lin, D., Jiang, J., Manning, N. O., Prilusky, J., Ritter, O., & Abola, E. E. (1998). Protein Data Bank (PDB): Database of three-dimensional structural information of biological macromolecules. Acta Crystallographica. Section D: Biological Crystallography, 54(Pt 6 Pt 1), 1078–1084.

    Article  CAS  Google Scholar 

  • Tabb, D. L., Fernando, C. G., & Chambers, M. C. (2007). MyriMatch: Highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. Journal of Proteome Research, 6(2), 654–661. doi:10.1021/pr0604054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tateno, Y., Imanishi, T., Miyazaki, S., Fukami-Kobayashi, K., Saitou, N., Sugawara, H., & Gojobori, T. (2002). DNA Data Bank of Japan (DDBJ) for genome scale research in life science. Nucleic Acids Research, 30(1), 27–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Trudgian, D. C., & Mirzaei, H. (2012). Cloud CPFP: A shotgun proteomics data analysis pipeline using cloud and high performance computing. Journal of Proteome Research, 11(12), 6282–6290. doi:10.1021/pr300694b.

    CAS  PubMed  Google Scholar 

  • Vaudel, M., Sickmann, A., & Martens, L. (2010). Peptide and protein quantification: A map of the minefield. Proteomics, 10(4), 650–670. doi:10.1002/pmic.200900481.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Barsnes, H., Berven, F. S., Sickmann, A., & Martens, L. (2011a). SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics, 11(5), 996–999. doi:10.1002/pmic.201000595.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Burkhart, J. M., Sickmann, A., Martens, L., & Zahedi, R. P. (2011b). Peptide identification quality control. Proteomics, 11(10), 2105–2114. doi:10.1002/pmic.201000704.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Burkhart, J. M., Breiter, D., Zahedi, R. P., Sickmann, A., & Martens, L. (2012a). A complex standard for protein identification, designed by evolution. Journal of Proteome Research, 11(10), 5065–5071. doi:10.1021/pr300055q.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Sickmann, A., & Martens, L. (2012b). Current methods for global proteome identification. Expert Review of Proteomics, 9(5), 519–532. doi:10.1586/epr.12.51.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Sickmann, A., & Martens, L. (2014a). Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics. Biochimica et biophysica acta, 1844(1 Pt A), 12–20. doi:10.1016/j.bbapap.2013.06.019.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Venne, A. S., Berven, F. S., Zahedi, R. P., Martens, L., & Barsnes, H. (2014b). Shedding light on black boxes in protein identification. Proteomics, 14(9), 1001–1005. doi:10.1002/pmic.201300488.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Burkhart, J. M., Zahedi, R. P., Oveland, E., Berven, F. S., Sickmann, A., Martens, L., & Barsnes, H. (2015). PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nature Biotechnology, 33(1), 22–24. doi:10.1038/nbt.3109.

    Article  CAS  PubMed  Google Scholar 

  • Vaudel, M., Verheggen, K., Csordas, A., Raeder, H., Berven, F. S., Martens, L., Vizcaino, J. A., & Barsnes, H. (2016). Exploring the potential of public proteomics data. Proteomics, 16(2), 214–225. doi:10.1002/pmic.201500295.

    Article  CAS  PubMed  Google Scholar 

  • Verheggen, K., Barsnes, H., & Martens, L. (2014). Distributed computing and data storage in proteomics: Many hands make light work, and a stronger memory. Proteomics, 14(4–5), 367–377. doi:10.1002/pmic.201300288.

    Article  CAS  PubMed  Google Scholar 

  • Verheggen, K., Maddelein, D., Hulstaert, N., Martens, L., Barsnes, H., & Vaudel, M. (2015). Pladipus enables universal distributed computing in proteomics bioinformatics. Journal of Proteome Research. doi:10.1021/acs.jproteome.5b00850.

    PubMed  Google Scholar 

  • Vizcaino, J. A., Mueller, M., Hermjakob, H., & Martens, L. (2009). Charting online OMICS resources: A navigational chart for clinical researchers. Proteomics Clinical Applications, 3(1), 18–29. doi:10.1002/prca.200800082.

    Article  CAS  PubMed  Google Scholar 

  • Vizcaino, J. A., Deutsch, E. W., Wang, R., Csordas, A., Reisinger, F., Rios, D., Dianes, J. A., Sun, Z., Farrah, T., Bandeira, N., Binz, P.-A., Xenarios, I., Eisenacher, M., Mayer, G., Gatto, L., Campos, A., Chalkley, R. J., Kraus, H.-J., Albar, J. P., Martinez-Bartolome, S., Apweiler, R., Omenn, G. S., Martens, L., Jones, A. R., & Hermjakob, H. (2014). ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nature Biotechnology, 32(3), 223–226. doi:10.1038/nbt.2839. http://www.nature.com/nbt/journal/v32/n3/abs/nbt.2839.html – supplementary-information.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Volders, P. J., Verheggen, K., Menschaert, G., Vandepoele, K., Martens, L., Vandesompele, J., & Mestdagh, P. (2015). An update on LNCipedia: A database for annotated human lncRNA sequences. Nucleic Acids Research, 43(Database issue), D174–D180. doi:10.1093/nar/gku1060.

    Article  PubMed  Google Scholar 

  • Whiteaker, J. R., Halusa, G. N., Hoofnagle, A. N., Sharma, V., MacLean, B., Yan, P., Wrobel, J. A., Kennedy, J., Mani, D. R., Zimmerman, L. J., Meyer, M. R., Mesri, M., Rodriguez, H., Clinical Proteomic Tumor Analysis, C., & Paulovich, A. G. (2014). CPTAC assay portal: A repository of targeted proteomic assays. Nature Methods, 11(7), 703–704. doi:10.1038/nmeth.3002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yates, A., Akanni, W., Amode, M. R., Barrell, D., Billis, K., Carvalho-Silva, D., Cummins, C., Clapham, P., Fitzgerald, S., Gil, L., Giron, C. G., Gordon, L., Hourlier, T., Hunt, S. E., Janacek, S. H., Johnson, N., Juettemann, T., Keenan, S., Lavidas, I., Martin, F. J., Maurel, T., McLaren, W., Murphy, D. N., Nag, R., Nuhn, M., Parker, A., Patricio, M., Pignatelli, M., Rahtz, M., Riat, H. S., Sheppard, D., Taylor, K., Thormann, A., Vullo, A., Wilder, S. P., Zadissa, A., Birney, E., Harrow, J., Muffato, M., Perry, E., Ruffier, M., Spudich, G., Trevanion, S. J., Cunningham, F., Aken, B. L., Zerbino, D. R., & Flicek, P. (2016). Ensemble 2016. Nucleic Acids Research, 44(D1), D710–D716. doi:10.1093/nar/gkv1157.

    Article  PubMed  Google Scholar 

  • Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., Kim, S., Davies, S. R., Wang, S., Wang, P., Kinsinger, C. R., Rivers, R. C., Rodriguez, H., Townsend, R. R., Ellis, M. J., Carr, S. A., Tabb, D. L., Coffey, R. J., Slebos, R. J., Liebler, D. C., & Nci, C. (2014). Proteogenomic characterization of human colon and rectal cancer. Nature, 513(7518), 382–387. doi:10.1038/nature13438.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

H.B. and H.R are supported by Bergen Forskningsstiftelse, and H.R. is further supported by Novo Nordisk Fonden and Western Norway Regional Health Authority. F.B. is supported by the Kristian Gerhard Jebsen foundation.

Conflict of Interest

The authors declare no competing financial interests.

Author information

Authors and Affiliations

  1. Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway

    Marc Vaudel, Harald Barsnes & Frode S. Berven

  2. KG Jebsen Center for Diabetes Research, Department of Clinical Science, University of Bergen, Bergen, Norway

    Marc Vaudel, Harald Barsnes & Helge Ræder

  3. Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway

    Marc Vaudel

  4. Department of Pediatrics, Haukeland University Hospital, Bergen, Norway

    Helge Ræder

Authors
  1. Marc Vaudel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harald Barsnes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Helge Ræder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Frode S. Berven
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marc Vaudel .

Editor information

Editors and Affiliations

  1. Department of Pharmacology & Toxicology, University of Texas Medical Branch, Galveston, Texas, USA

    Ákos Végvári

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Vaudel, M., Barsnes, H., Ræder, H., Berven, F.S. (2016). Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies. In: Végvári, Á. (eds) Proteogenomics. Advances in Experimental Medicine and Biology, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-319-42316-6_5

Download citation

Publish with us

Policies and ethics

Search

Navigation