Molecular Biotechnology

, Volume 48, Issue 1, pp 87–95

Human Protein Reference Database and Human Proteinpedia as Discovery Resources for Molecular Biotechnology



In the recent years, research in molecular biotechnology has transformed from being small scale studies targeted at a single or a small set of molecule(s) into a combination of high throughput discovery platforms and extensive validations. Such a discovery platform provided an unbiased approach which resulted in the identification of several novel genetic and protein biomarkers. High throughput nature of these investigations coupled with higher sensitivity and specificity of Next Generation technologies provided qualitatively and quantitatively richer biological data. These developments have also revolutionized biological research and speed of data generation. However, it is becoming difficult for individual investigators to directly benefit from this data because they are not easily accessible. Data resources became necessary to assimilate, store and disseminate information that could allow future discoveries. We have developed two resources—Human Protein Reference Database (HPRD) and Human Proteinpedia, which integrate knowledge relevant to human proteins. A number of protein features including protein–protein interactions, post-translational modifications, subcellular localization, and tissue expression, which have been studied using different strategies were incorporated in these databases. Human Proteinpedia also provides a portal for community participation to annotate and share proteomic data and uses HPRD as the scaffold for data processing. Proteomic investigators can even share unpublished data in Human Proteinpedia, which provides a meaningful platform for data sharing. As proteomic information reflects a direct view of cellular systems, proteomics is expected to complement other areas of biology such as genomics, transcriptomics, molecular biology, cloning, and classical genetics in understanding the relationships among multiple facets of biological systems.


Bioinformatics Signaling pathways Mass spectrometry Molecular diagnostics Disease markers 


  1. 1.
    Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., et al. (2008). High-quality binary protein interaction map of the yeast interactome network. Science, 322, 104–110.CrossRefGoogle Scholar
  2. 2.
    Kawasumi, M., & Nghiem, P. (2007). Chemical genetics: Elucidating biological systems with small-molecule compounds. Journal of Investigative Dermatology, 127, 1577.CrossRefGoogle Scholar
  3. 3.
    Shim, J. S., & Kwon, H. J. (2004). Chemical genetics for therapeutic target mining. Expert Opinion on Therapeutic Targets, 8, 653–661.CrossRefGoogle Scholar
  4. 4.
    Dupre, A., Boyer-Chatenet, L., Sattler, R. M., Modi, A. P., Lee, J.-H., Nicolette, M. L., et al. (2008). A forward chemical genetic screen reveals an inhibitor of the Mre11-Rad50-Nbs1 complex. Nature Chemical Biology, 4, 119.CrossRefGoogle Scholar
  5. 5.
    Koga, H. (2006). Establishment of the platform for reverse chemical genetics targeting novel protein–protein interactions. Molecular BioSystems, 2, 159–164.CrossRefGoogle Scholar
  6. 6.
    Chaerkady, R., & Pandey, A. (2008). Applications of proteomics to lab diagnosis. Annual Review of Pathology: Mechanisms of Disease, 3, 485–498.CrossRefGoogle Scholar
  7. 7.
    Chaerkady, R., Harsha, H. C., Nalli, A., Gucek, M., Vivekanandan, P., Akhtar, J., et al. (2008). A quantitative proteomic approach for identification of potential biomarkers in hepatocellular carcinoma. Journal of Proteome Research, 7, 4289–4298.CrossRefGoogle Scholar
  8. 8.
    Gronborg, M., Kristiansen, T. Z., Iwahori, A., Chang, R., Reddy, R., Sato, N., et al. (2006). Biomarker discovery from pancreatic cancer secretome using a differential proteomic approach. Molecular and Cellular Proteomics, 5, 157–171.CrossRefGoogle Scholar
  9. 9.
    Vermeulen, M., Hubner, N. C., & Mann, M. (2008). High confidence determination of specific protein–protein interactions using quantitative mass spectrometry. Current Opinion in Biotechnology, 19, 331.CrossRefGoogle Scholar
  10. 10.
    Leitner, F., Krallinger, M., Rodriguez-Penagos, C., Hakenberg, J., Plake, C., Kuo, C. J., et al. (2008). Introducing meta-services for biomedical information extraction. Genome Biology, 9(Suppl 2), S6.CrossRefGoogle Scholar
  11. 11.
    Orchard, S., Hermjakob, H., & Apweiler, R. (2005). Annotating the human proteome. Molecular and Cellular Proteomics, 4, 435–440.CrossRefGoogle Scholar
  12. 12.
    Mueller, M., Martens, L., & Apweiler, R. (2007). Annotating the human proteome: Beyond establishing a parts list. Biochimica et Biophysica Acta (BBA)—Proteins & Proteomics, 1774, 175.CrossRefGoogle Scholar
  13. 13.
    Orchard, S., & Hermjakob, H. (2008). The HUPO proteomics standards initiative—Easing communication and minimizing data loss in a changing world. Briefings in Bioinformatics, 9, 166–173.CrossRefGoogle Scholar
  14. 14.
    Peri, S., Navarro, J. D., Amanchy, R., Kristiansen, T. Z., Jonnalagadda, C. K., Surendranath, V., et al. (2003). Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Research, 13, 2363–2371.CrossRefGoogle Scholar
  15. 15.
    Peri, S., Navarro, J. D., Kristiansen, T. Z., Amanchy, R., Surendranath, V., Muthusamy, B., et al. (2004). Human protein reference database as a discovery resource for proteomics. Nucleic Acids Research, 32, D497–D501.CrossRefGoogle Scholar
  16. 16.
    Mishra, G. R., Suresh, M., Kumaran, K., Kannabiran, N., Suresh, S., Bala, P., et al. (2006). Human protein reference database—2006 update. Nucleic Acids Research, 34, D411–D414.CrossRefGoogle Scholar
  17. 17.
    Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., et al. (2009). Human protein reference database—2009 update. Nucleic Acids Research, 37, D767–D772.CrossRefGoogle Scholar
  18. 18.
    Wheeler, D. L., Barrett, T., Benson, D. A., Bryant, S. H., Canese, K., Chetvernin, V., et al. (2008). Database resources of the National Center for Biotechnology Information. Nucleic Acids Research, 36, D13–D21.CrossRefGoogle Scholar
  19. 19.
    Mathivanan, S., Periaswamy, B., Gandhi, T. K., Kandasamy, K., Suresh, S., Mohmood, R., et al. (2006). An evaluation of human protein–protein interaction data in the public domain. BMC Bioinformatics, 7(Suppl 5), S19.CrossRefGoogle Scholar
  20. 20.
    Ceol, A., Chatr Aryamontri, A., Licata, L., Peluso, D., Briganti, L., Perfetto, L., et al. (2010). MINT, the molecular interaction database: 2009 update. Nucleic Acids Research, 38, D532–D539.CrossRefGoogle Scholar
  21. 21.
    Aranda, B., Achuthan, P., Alam-Faruque, Y., Armean, I., Bridge, A., Derow, C., et al. (2010). The IntAct molecular interaction database in 2010. Nucleic Acids Research, 38, D525–D531.CrossRefGoogle Scholar
  22. 22.
    Alfarano, C., Andrade, C. E., Anthony, K., Bahroos, N., Bajec, M., Bantoft, K., et al. (2005). The biomolecular interaction network database and related tools 2005 update. Nucleic Acids Research, 33, D418–D424.CrossRefGoogle Scholar
  23. 23.
    Salwinski, L., Miller, C. S., Smith, A. J., Pettit, F. K., Bowie, J. U., & Eisenberg, D. (2004). The database of interacting proteins: 2004 update. Nucleic Acids Research, 32, D449–D451.CrossRefGoogle Scholar
  24. 24.
    Pagel, P., Kovac, S., Oesterheld, M., Brauner, B., Dunger-Kaltenbach, I., Frishman, G., et al. (2005). The MIPS mammalian protein–protein interaction database. Bioinformatics, 21, 832–834.CrossRefGoogle Scholar
  25. 25.
    Beuming, T., Skrabanek, L., Niv, M. Y., Mukherjee, P., & Weinstein, H. (2005). PDZBase: A protein–protein interaction database for PDZ-domains. Bioinformatics, 21, 827–828.CrossRefGoogle Scholar
  26. 26.
    Hermjakob, H., Montecchi-Palazzi, L., Bader, G., Wojcik, J., Salwinski, L., Ceol, A., et al. (2004). The HUPO PSI’s molecular interaction format[mdash]a community standard for the representation of protein interaction data. Nature Biotechnology, 22, 177.CrossRefGoogle Scholar
  27. 27.
    Kerrien, S., Orchard, S., Montecchi-Palazzi, L., Aranda, B., Quinn, A. F., Vinod, N., et al. (2007). Broadening the horizon—Level 2.5 of the HUPO-PSI format for molecular interactions. BMC Biology, 5, 44.CrossRefGoogle Scholar
  28. 28.
    John, S. G. (2004). The RESID database of protein modifications as a resource and annotation tool. Proteomics, 4, 1527–1533.CrossRefGoogle Scholar
  29. 29.
    Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., et al. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics, 25, 25.CrossRefGoogle Scholar
  30. 30.
    Kelso, J., Visagie, J., Theiler, G., Christoffels, A., Bardien, S., Smedley, D., et al. (2003). eVOC: A controlled vocabulary for unifying gene expression data. Genome Research, 13, 1222–1230.CrossRefGoogle Scholar
  31. 31.
    Gandhi, T. K., Zhong, J., Mathivanan, S., Karthick, L., Chandrika, K. N., Mohan, S. S., et al. (2006). Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nature Genetics, 38, 285–293.CrossRefGoogle Scholar
  32. 32.
    Rhodes, D. R., Tomlins, S. A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana-Sundaram, S., et al. (2005). Probabilistic model of the human protein–protein interaction network. Nature Biotechnology, 23, 951–959.CrossRefGoogle Scholar
  33. 33.
    Ali, W., & Deane, C. M. (2009). Functionally guided alignment of protein interaction networks for module detection. Bioinformatics, 25, 3166–3173.CrossRefGoogle Scholar
  34. 34.
    Fernandes, L. P., Annibale, A., Kleinjung, J., Coolen, A. C. & Fraternali, F. (2010). Protein networks reveal detection bias and species consistency when analysed by information-theoretic methods. PLoS One, 5, e12083.Google Scholar
  35. 35.
    Ozgur, A., Xiang, Z., Radev, D. R., & He, Y. (2010). Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks. Journal of Biomedicine and Biotechnology, 2010, 426479.CrossRefGoogle Scholar
  36. 36.
    Rual, J. F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., et al. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature, 437, 1173–1178.CrossRefGoogle Scholar
  37. 37.
    Stelzl, U., Worm, U., Lalowski, M., Haenig, C., Brembeck, F. H., Goehler, H., et al. (2005). A human protein–protein interaction network: A resource for annotating the proteome. Cell, 122, 957–968.CrossRefGoogle Scholar
  38. 38.
    Pujana, M. A., Han, J. D., Starita, L. M., Stevens, K. N., Tewari, M., Ahn, J. S., et al. (2007). Network modeling links breast cancer susceptibility and centrosome dysfunction. Nature Genetics, 39, 1338–1349.CrossRefGoogle Scholar
  39. 39.
    Navlakha, S., & Kingsford, C. (2010). The power of protein interaction networks for associating genes with diseases. Bioinformatics, 26, 1057–1063.CrossRefGoogle Scholar
  40. 40.
    Suthram, S., Dudley, J. T., Chiang, A. P., Chen, R., Hastie, T. J., & Butte, A. J. (2010). Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Computational Biology, 6, e1000662.CrossRefGoogle Scholar
  41. 41.
    Zhao, S., & Li, S. (2010). Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One, 5, e11764.CrossRefGoogle Scholar
  42. 42.
    Sompallae, R., Callegari, S., Kamranvar, S. A., & Masucci, M. G. (2010). Transcription profiling of Epstein–Barr virus nuclear antigen (EBNA)-1 expressing cells suggests targeting of chromatin remodeling complexes. PLoS One, 5, e12052.CrossRefGoogle Scholar
  43. 43.
    van Dijk, D., Ertaylan, G., Boucher, C. A., & Sloot, P. M. (2010). Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks. BMC Systems Biology, 4, 96.CrossRefGoogle Scholar
  44. 44.
    Miller, J. A., Horvath, S., & Geschwind, D. H. (2010). Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proceedings of the National Academy of Sciences USA, 107, 12698–12703.CrossRefGoogle Scholar
  45. 45.
    Yang, S. K., Wang, Y. C., Chao, C. C., Chuang, Y. J., Lan, C. Y., & Chen, B. S. (2010). Dynamic cross-talk analysis among TNF-R, TLR-4 and IL-1R signalings in TNFalpha-induced inflammatory responses. BMC Medical Genomics, 3, 19.CrossRefGoogle Scholar
  46. 46.
    Qureshi, A. H., Chaoji, V., Maiguel, D., Faridi, M. H., Barth, C. J., Salem, S. M., et al. (2009). Proteomic and phospho-proteomic profile of human platelets in basal, resting state: Insights into integrin signaling. PLoS One, 4, e7627.CrossRefGoogle Scholar
  47. 47.
    Mosca, E., Bertoli, G., Piscitelli, E., Vilardo, L., Reinbold, R. A., Zucchi, I., et al. (2009). Identification of functionally related genes using data mining and data integration: A breast cancer case study. BMC Bioinformatics, 10(Suppl 12), S8.CrossRefGoogle Scholar
  48. 48.
    Cain, S. A., McGovern, A., Small, E., Ward, L. J., Baldock, C., Shuttleworth, A., et al. (2009). Defining elastic fiber interactions by molecular fishing: An affinity purification and mass spectrometry approach. Molecular and Cellular Proteomics, 8, 2715–2732.CrossRefGoogle Scholar
  49. 49.
    Keerthikumar, S., Bhadra, S., Kandasamy, K., Raju, R., Ramachandra, Y. L., Bhattacharyya, C., et al. (2009). Prediction of candidate primary immunodeficiency disease genes using a support vector machine learning approach. DNA Research, 16, 345–351.CrossRefGoogle Scholar
  50. 50.
    Liu, Y., & Tozeren, A. (2010). Modular composition predicts kinase/substrate interactions. BMC Bioinformatics, 11, 349.CrossRefGoogle Scholar
  51. 51.
    Blankenburg, H., Finn, R. D., Prlic, A., Jenkinson, A. M., Ramirez, F., Emig, D., et al. (2009). DASMI: Exchanging, annotating and assessing molecular interaction data. Bioinformatics, 25, 1321–1328.CrossRefGoogle Scholar
  52. 52.
    Blankenburg, H., Ramirez, F., Buch, J., & Albrecht, M. (2009). DASMIweb: Online integration, analysis and assessment of distributed protein interaction data. Nucleic Acids Research, 37, W122–W128.CrossRefGoogle Scholar
  53. 53.
    Sun, C. H., Hwang, T., Oh, K. & Yi, G. S. (2010). DynaMod: Dynamic functional modularity analysis. Nucleic Acids Research, 38 Suppl, W103–W108.Google Scholar
  54. 54.
    Dogrusoz, U., Cetintas, A., Demir, E., & Babur, O. (2009). Algorithms for effective querying of compound graph-based pathway databases. BMC Bioinformatics, 10, 376.CrossRefGoogle Scholar
  55. 55.
    Lee, S. A., Chan, C. H., Chen, T. C., Yang, C. Y., Huang, K. C., Tsai, C. H., et al. (2009). POINeT: Protein interactome with sub-network analysis and hub prioritization. BMC Bioinformatics, 10, 114.CrossRefGoogle Scholar
  56. 56.
    Klammer, M., Godl, K., Tebbe, A., & Schaab, C. (2010). Identifying differentially regulated subnetworks from phosphoproteomic data. BMC Bioinformatics, 11, 351.CrossRefGoogle Scholar
  57. 57.
    Banky, D., Ordog, R., & Grolmusz, V. (2009). NASCENT: An automatic protein interaction network generation tool for non-model organisms. Bioinformation, 3, 361–363.Google Scholar
  58. 58.
    Kamburov, A., Wierling, C., Lehrach, H., & Herwig, R. (2008). ConsensusPathDB—A database for integrating human functional interaction networks. Nucleic Acids Research, 37, D623–D628.CrossRefGoogle Scholar
  59. 59.
    Hu, Z., Snitkin, E. S., & DeLisi, C. (2008). VisANT: An integrative framework for networks in systems biology. Briefings in Bioinformatics, 9, 317–325.CrossRefGoogle Scholar
  60. 60.
    Berger, S. I., Posner, J. M., & Ma’ayan, A. (2007). Genes2Networks: Connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics, 8, 372.CrossRefGoogle Scholar
  61. 61.
    Barsky, A., Gardy, J. L., Hancock, R. E., & Munzner, T. (2007). Cerebral: A cytoscape plugin for layout of and interaction with biological networks using subcellular localization annotation. Bioinformatics, 23, 1040–1042.CrossRefGoogle Scholar
  62. 62.
    Avila-Campillo, I., Drew, K., Lin, J., Reiss, D. J., & Bonneau, R. (2007). BioNetBuilder: Automatic integration of biological networks. Bioinformatics, 23, 392–393.CrossRefGoogle Scholar
  63. 63.
    Obayashi, T., Hayashi, S., Shibaoka, M., Saeki, M., Ohta, H., & Kinoshita, K. (2008). COXPRESdb: A database of coexpressed gene networks in mammals. Nucleic Acids Research, 36, D77–D82.CrossRefGoogle Scholar
  64. 64.
    Jensen, L. J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., et al. (2008). STRING 8—A global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Research, 37, D412–D416.CrossRefGoogle Scholar
  65. 65.
    Chaurasia, G., Iqbal, Y., Hanig, C., Herzel, H., Wanker, E. E., & Futschik, M. E. (2007). UniHI: An entry gate to the human protein interactome. Nucleic Acids Research, 35, D590–D594.CrossRefGoogle Scholar
  66. 66.
    Zaidel-Bar, R., Itzkovitz, S., Ma’ayan, A., Iyengar, R., & Geiger, B. (2007). Functional atlas of the integrin adhesome. Nature Cell Biology, 9, 858–867.CrossRefGoogle Scholar
  67. 67.
    Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences USA, 102, 15545–15550.CrossRefGoogle Scholar
  68. 68.
    Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T. et al. (2010). The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research, 38 Suppl, W214–W220.Google Scholar
  69. 69.
    Newman, A. M., & Cooper, J. B. (2010). AutoSOME: A clustering method for identifying gene expression modules without prior knowledge of cluster number. BMC Bioinformatics, 11, 117.CrossRefGoogle Scholar
  70. 70.
    Gould, C. M., Diella, F., Via, A., Puntervoll, P., Gemund, C., Chabanis-Davidson, S., et al. (2010). ELM: The status of the 2010 eukaryotic linear motif resource. Nucleic Acids Research, 38, D167–D180.CrossRefGoogle Scholar
  71. 71.
    Edwards, R. J., Davey, N. E., & Shields, D. C. (2008). CompariMotif: Quick and easy comparisons of sequence motifs. Bioinformatics, 24, 1307–1309.CrossRefGoogle Scholar
  72. 72.
    Edwards, R. J., Davey, N. E., & Shields, D. C. (2007). SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. PLoS ONE, 2, e967.CrossRefGoogle Scholar
  73. 73.
    Yang, C. Y., Chang, C. H., Yu, Y. L., Lin, T. C., Lee, S. A., Yen, C. C., et al. (2008). PhosphoPOINT: A comprehensive human kinase interactome and phospho-protein database. Bioinformatics, 24, i14–i20.CrossRefGoogle Scholar
  74. 74.
    Gong, W., Zhou, D., Ren, Y., Wang, Y., Zuo, Z., Shen, Y., et al. (2008). PepCyber:P PEP: A database of human protein–protein interactions mediated by phosphoprotein-binding domains. Nucleic Acids Research, 36, D679–D683.CrossRefGoogle Scholar
  75. 75.
    Xue, Y., Ren, J., Gao, X., Jin, C., Wen, L., & Yao, X. (2008). GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Molecular and Cellular Proteomics, 7, 1598–1608.CrossRefGoogle Scholar
  76. 76.
    Keerthikumar, S., Raju, R., Kandasamy, K., Hijikata, A., Ramabadran, S., Balakrishnan, L., Ahmed, M., Rani, S., Selvan, L. D. N., Somanathan, D. S., et al. (2008). RAPID: Resource of Asian primary immunodeficiency diseases. Nucleic Acids Research, 37, D863–D867.CrossRefGoogle Scholar
  77. 77.
    Syed, A. S., D’Antonio, M., & Ciccarelli, F. D. (2010). Network of cancer genes: A web resource to analyze duplicability, orthology and network properties of cancer genes. Nucleic Acids Research, 38, D670–D675.CrossRefGoogle Scholar
  78. 78.
    Wang, L., Xiong, Y., Sun, Y., Fang, Z., Li, L., Ji, H., et al. (2010). HLungDB: An integrated database of human lung cancer research. Nucleic Acids Research, 38, D665–D669.CrossRefGoogle Scholar
  79. 79.
    Gong, X., Wu, R., Zhang, Y., Zhao, W., Cheng, L., Gu, Y., et al. (2010). Extracting consistent knowledge from highly inconsistent cancer gene data sources. BMC Bioinformatics, 11, 76.CrossRefGoogle Scholar
  80. 80.
    Chautard, E., Ballut, L., Thierry-Mieg, N., & Ricard-Blum, S. (2009). MatrixDB, a database focused on extracellular protein–protein and protein–carbohydrate interactions. Bioinformatics, 25, 690–691.CrossRefGoogle Scholar
  81. 81.
    Yang, J. O., Kim, W. Y., Jeong, S. Y., Oh, J. H., Jho, S., Bhak, J., et al. (2009). PDbase: A database of Parkinson’s disease-related genes and genetic variation using substantia nigra ESTs. BMC Genomics, 10(Suppl 3), S32.CrossRefGoogle Scholar
  82. 82.
    Nogales-Cadenas, R., Abascal, F., Diez-Perez, J., Carazo, J. M., & Pascual-Montano, A. (2008). CentrosomeDB: A human centrosomal proteins database. Nucleic Acids Research, 37, D175–D180.CrossRefGoogle Scholar
  83. 83.
    Richardson, C. J., Gao, Q., Mitsopoulous, C., Zvelebil, M., Pearl, L. H., & Pearl, F. M. G. (2008). MoKCa database—Mutations of kinases in cancer. Nucleic Acids Research, 37, D824–D831.CrossRefGoogle Scholar
  84. 84.
    Igarashi, Y., Eroshkin, A., Gramatikova, S., Gramatikoff, K., Zhang, Y., Smith, J. W., et al. (2007). CutDB: A proteolytic event database. Nucleic Acids Research, 35, D546–D549.CrossRefGoogle Scholar
  85. 85.
    Shtatland, T., Guettler, D., Kossodo, M., Pivovarov, M., & Weissleder, R. (2007). PepBank—A database of peptides based on sequence text mining and public peptide data sources. BMC Bioinformatics, 8, 280.CrossRefGoogle Scholar
  86. 86.
    Li, C.-Y., Liu, Q.-R., Zhang, P.-W., Li, X.-M., Wei, L., & Uhl, G. R. (2008). OKCAM: An ontology-based, human-centered knowledgebase for cell adhesion molecules. Nucleic Acids Research, 37, D251–D260.CrossRefGoogle Scholar
  87. 87.
    Hulbert, E. M., Smink, L. J., Adlem, E. C., Allen, J. E., Burdick, D. B., Burren, O. S., et al. (2007). T1DBase: Integration and presentation of complex data for type 1 diabetes research. Nucleic Acids Research, 35, D742–D746.CrossRefGoogle Scholar
  88. 88.
    Hijikata, A., Raju, R., Keerthikumar, S., Ramabadran, S., Balakrishnan, L., Ramadoss, S. K., et al. (2010). Mutation@A Glance: An integrative web application for analysing mutations from human genetic diseases. DNA Research, 17, 197–208.CrossRefGoogle Scholar
  89. 89.
    Kandasamy, K., Mohan, S. S., Raju, R., Keerthikumar, S., Kumar, G. S., Venugopal, A. K., et al. (2010). NetPath: A public resource of curated signal transduction pathways. Genome Biology, 11, R3.CrossRefGoogle Scholar
  90. 90.
    Amanchy, R., Periaswamy, B., Mathivanan, S., Reddy, R., Tattikota, S. G., & Pandey, A. (2007). A curated compendium of phosphorylation motifs. Nature Biotechnology, 25, 285–286.CrossRefGoogle Scholar
  91. 91.
    Mathivanan, S., Ahmed, M., Ahn, N. G., Alexandre, H., Amanchy, R., Andrews, P. C., et al. (2008). Human Proteinpedia enables sharing of human protein data. Nature Biotechnology, 26, 164–167.CrossRefGoogle Scholar
  92. 92.
    Kandasamy, K., Keerthikumar, S., Goel, R., Mathivanan, S., Patankar, N., Shafreen, B., et al. (2009). Human Proteinpedia: A unified discovery resource for proteomics research. Nucleic Acids Research, 37, D773–D781.CrossRefGoogle Scholar
  93. 93.
    Prasad, T. S., Kandasamy, K., & Pandey, A. (2009). Human protein reference database and human proteinpedia as discovery tools for systems biology. Methods in Molecular Biology, 577, 67–79.CrossRefGoogle Scholar
  94. 94.
    Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., et al. (2009). Circos: An information aesthetic for comparative genomics. Genome Research, 19, 1639–1645.CrossRefGoogle Scholar
  95. 95.
    Kuster, B., Schirle, M., Mallick, P., & Aebersold, R. (2005). Scoring proteomes with proteotypic peptide probes. Nature Reviews Molecular Cell Biology, 6, 577.CrossRefGoogle Scholar
  96. 96.
    Craig, R., Cortens, J. P., & Beavis, R. C. (2005). The use of proteotypic peptide libraries for protein identification. Rapid Communications in Mass Spectrometry, 19, 1844–1850.CrossRefGoogle Scholar
  97. 97.
    Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D. A., & White, F. M. (2007). Multiple reaction monitoring for robust quantitative proteomic analysis of cellular signaling networks. Proceedings of the National Academy of Sciences USA, 104, 5860–5865.CrossRefGoogle Scholar
  98. 98.
    Anderson, L., & Hunter, C. L. (2006). Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Molecular and Cellular Proteomics, 5, 573–588.CrossRefGoogle Scholar
  99. 99.
    Koc, H., & Swenberg, J. A. (2002). Applications of mass spectrometry for quantitation of DNA adducts. Journal of Chromatography B, 778, 323.CrossRefGoogle Scholar
  100. 100.
    Thevis, M., Opfermann, G., & Schanzer, W. (2001). High speed determination of beta-receptor blocking agents in human urine by liquid chromatography/tandem mass spectrometry. Biomedical Chromatography, 15, 393–402.CrossRefGoogle Scholar
  101. 101.
    Ho, E. N. M., Leung, D. K. K., Wan, T. S. M., & Yu, N. H. (2006). Comprehensive screening of anabolic steroids, corticosteroids, and acidic drugs in horse urine by solid-phase extraction and liquid chromatography-mass spectrometry. Journal of Chromatography A, 1120, 38.CrossRefGoogle Scholar
  102. 102.
    Herrin, G., McCurdy, H. H. H., & Wall, W. H. (2005). Investigation of an LCMSMS (QTrap) method for the rapid screening and identification of drugs in postmortem toxicology whole blood samples. Journal of Analytical Toxicology, 29, 599.Google Scholar
  103. 103.
    Guan, F., Uboh, C. E., Soma, L. R., Luo, Y., Rudy, J., & Tobin, T. (2005). Detection, quantification and confirmation of anabolic steroids in equine plasma by liquid chromatography and tandem mass spectrometry. Journal of Chromatography B, 829, 56.CrossRefGoogle Scholar
  104. 104.
    Hua, L., Jiang, W., Eric, K., Wendy, C., Betty, C., Michael, D. J., et al. (2004). Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients with rheumatoid arthritis. Arthritis and Rheumatism, 50, 3792–3803.CrossRefGoogle Scholar
  105. 105.
    Gupta, M. K., Jung, J. W., Uhm, S. J., Lee, H., Lee, H. T., & Kim, K. P. (2009). Combining selected reaction monitoring with discovery proteomics in limited biological samples. Proteomics, 9, 4834–4836.CrossRefGoogle Scholar
  106. 106.
    Kuzyk, M. A., Smith, D., Yang, J., Cross, T. J., Jackson, A. M., Hardie, D. B., et al. (2009). Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Molecular and Cellular Proteomics, 8, 1860–1877.CrossRefGoogle Scholar
  107. 107.
    Picotti, P., Rinner, O., Stallmach, R., Dautel, F., Farrah, T., Domon, B., et al. (2010). High-throughput generation of selected reaction-monitoring assays for proteins and proteomes. Nature Methods, 7, 43–46.CrossRefGoogle Scholar
  108. 108.
    Editorial. (2007). Democratizing proteomics data. Nature Biotechnology, 25, 262.Google Scholar
  109. 109.
    Editorial. (2008). Thou shalt share your data. Nature Methods, 5, 209.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Institute of BioinformaticsBangaloreIndia
  2. 2.Department of BiotechnologyKuvempu UniversityShankaraghattaIndia
  3. 3.Bioinformatics Centre, School of Life SciencesPondicherry UniversityPondicherryIndia
  4. 4.McKusick-Nathans Institute of Genetic Medicine and Departments of Biological Chemistry, Pathology and OncologyJohns Hopkins UniversityBaltimoreUSA

Personalised recommendations