Atherosclerosis
Volume 205, Issue 1 , Pages 9-14 , July 2009

Improvements to cardiovascular Gene Ontology

  • Ruth C. Lovering

      Affiliations

    • Department of Medicine, University College London, Rayne Institute, 5 University Street, London WC1E 6JF, UK
    • Corresponding Author InformationCorresponding author. Tel.: +44 20 7679 6968; fax: +44 20 7679 6212.
  • ,
  • Emily C. Dimmer

      Affiliations

    • GOA Project, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
  • ,
  • Philippa J. Talmud

      Affiliations

    • Department of Medicine, University College London, Rayne Institute, 5 University Street, London WC1E 6JF, UK

Received 30 June 2008 ,Revised 29 September 2008 ,Accepted 14 October 2008.

References 

  1. Consortium TGO. Creating the gene ontology resource: design and implementation. Genome Res. 2001;11:1425–1433
  2. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29
  3. Lomax J. Get ready to GO! A biologist's guide to the Gene Ontology. Brief Bioinform. 2005;6:298–304
  4. Dimmer EC, Huntley RP, Barrell DG, Binns D, Draghici S, Camon EB, Hubank M, Talmud PJ, Apweiler R, Lovering RC. The Gene Ontology; providing a functional role in proteomic studies. Proteomics 2008; July [Epub ahead of print].
  5. Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res. 2003;13:662–672
  6. Ashley EA, Ferrara R, King JY, et al. Network analysis of human in-stent restenosis. Circulation. 2006;114:2644–2654
  7. Pan Y, Kislinger T, Gramolini AO, et al. Identification of biochemical adaptations in hyper- or hypocontractile hearts from phospholamban mutant mice by expression proteomics. Proc Natl Acad Sci USA. 2004;101:2241–2246
  8. Boraldi F, Annovi G, Carraro F, et al. Hypoxia influences the cellular cross-talk of human dermal fibroblasts. A proteomic approach. Biochim Biophys Acta. 2007;1774:1402–1413
  9. Staab CA, Ceder R, Jagerbrink T, et al. Bioinformatics processing of protein and transcript profiles of normal and transformed cell lines indicates functional impairment of transcriptional regulators in buccal carcinoma. J Proteome Res. 2007;6:3705–3717
  10. Shi M, Jin J, Wang Y, et al. Mortalin: a protein associated with progression of Parkinson disease?. J Neuropathol Exp Neurol. 2008;67:117–124
  11. Perco P, Wilflingseder J, Bernthaler A, et al. Biomarker candidates for cardiovascular disease and bone metabolism disorders in chronic kidney disease: a systems biology perspective. J Cell Mol Med. 2008;12:1177–1187
  12. Dyer MD, Murali TM, Sobral BW. The landscape of human proteins interacting with viruses and other pathogens. PLoS Pathog. 2008;4:e32
  13. Ho E, Webber R, Wilkins MR. Interactive three-dimensional visualization and contextual analysis of protein interaction networks. J Proteome Res. 2008;7:104–112
  14. Cline MS, Smoot M, Cerami E, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2:2366–2382
  15. Kislinger T, Rahman K, Radulovic D, Cox B, Rossant J, Emili A. PRISM, a generic large scale proteomic investigation strategy for mammals. Mol Cell Proteomics. 2003;2:96–106
  16. Cao R, He Q, Zhou J, et al. High-throughput analysis of rat liver plasma membrane proteome by a nonelectrophoretic in-gel tryptic digestion coupled with mass spectrometry identification. J Proteome Res. 2008;7:535–545
  17. Stevens SM, Duncan RS, Koulen P, Prokai L. Proteomic analysis of mouse brain microsomes: identification and bioinformatic characterization of endoplasmic reticulum proteins in the mammalian central nervous system. J Proteome Res. 2008;7:1046–1054
  18. Barbe L, Lundberg E, Oksvold P, et al. Toward a confocal subcellular atlas of the human proteome. Mol Cell Proteomics. 2008;7:499–508
  19. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006;34:D535–539
  20. Alfarano C, Andrade CE, Anthony K, et al. The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 2005;33:D418–424
  21. Mishra GR, Suresh M, Kumaran K, et al. Human protein reference database—2006 update. Nucleic Acids Res. 2006;34:D411–414
  22. Kerrien S, Alam-Faruque Y, Aranda B, et al. IntAct—open source resource for molecular interaction data. Nucleic Acids Res. 2007;35:D561–565
  23. Khatri P, Draghici S. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics. 2005;21:3587–3595
  24. Lovering RC, Dimmer E, Khodiyar VK, et al. Cardiovascular GO annotation initiative year 1 report: why cardiovascular GO?. Proteomics. 2008;8:1950–1953
  25. Aerts S, Haeussler M, van Vooren S, et al. Text-mining assisted regulatory annotation. Genome Biol. 2008;9:R31
  26. Cohen KB, Hunter L. Getting started in text mining. PLoS Comput Biol. 2008;4:e20
  27. Camon EB, Barrell DG, Dimmer EC, et al. An evaluation of GO annotation retrieval for BioCreAtIvE and GOA. BMC Bioinform. 2005;6(Suppl 1):S17
  28. Ashburner M, Bergman CM. Drosophila melanogaster: a case study of a model genomic sequence and its consequences. Genome Res. 2005;15:1661–1667
  29. Bieri T, Blasiar D, Ozersky P, et al. WormBase: new content and better access. Nucleic Acids Res. 2007;35:D506–510

PII: S0021-9150(08)00749-1

doi: 10.1016/j.atherosclerosis.2008.10.014

Atherosclerosis
Volume 205, Issue 1 , Pages 9-14 , July 2009