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Optimized Protein–Protein Interaction Network Usage with Context Filtering

  • Natalia Pietrosemoli
  • Maria Pamela Dobay
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1819)

Abstract

Protein–protein interaction networks (PPIs) collect information on physical—and in some cases–functional interactions between proteins. Most PPIs are annotated with confidence scores, which reflect the probability that a reported interaction is a true interaction. These scores, however, do not allow users to isolate interactions relevant in a particular biological context. Here, we describe solutions for performing context filtering on PPIs to allow biological data interpretation and functional inference in two publicly available PPIs resources (HIPPIE and STRING) and in the proprietary pathway analysis tool and knowledge base Ingenuity Pathway Analysis.

Key words

Protein–protein interaction networks Context filtering Orthogonal text mining resources 

Notes

Acknowledgments

This work was supported by the Swiss Initiative in Systems Biology, SystemsX, through a fellowship (2013/137) provided to M.P.D.

References

  1. 1.
    Rual JF, 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 437:1173–1178CrossRefGoogle Scholar
  2. 2.
    Schaefer MH, Fontaine JF, Vinayagam A, Porras P, Wanker EE, Andrade-Navarro MA (2012) HIPPIE: integrating protein interaction networks with experiment based quality scores. PLoS One 7:e31826CrossRefGoogle Scholar
  3. 3.
    von Mering C, Jensen LJ, Snel B, Hooper SD, Krupp M, Foglierini M et al (2005) STRING: known and predicted protein-protein associations, integrated and transferred across organisms. Nucleic Acids Res 33:D433–D437CrossRefGoogle Scholar
  4. 4.
    Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41:D808–D815CrossRefGoogle Scholar
  5. 5.
    Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, Surendranath V et al (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13:2363–2371CrossRefGoogle Scholar
  6. 6.
    Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G (2002) MINT: a molecular INTeraction database. FEBS Lett 513:135–140CrossRefGoogle Scholar
  7. 7.
    Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, Orchard S et al (2004) IntAct: an open source molecular interaction database. Nucleic Acids Res 32:D452–D455CrossRefGoogle Scholar
  8. 8.
    Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539CrossRefGoogle Scholar
  9. 9.
    Dobay MP, Stertz S, Delorenzi M (2017) Context-based retrieval of functional modules in protein-protein interaction networks. Brief BioinformGoogle Scholar
  10. 10.
    Schaefer MH, Lopes TJ, Mah N, Shoemaker JE, Matsuoka Y, Fontaine JF et al (2013) Adding protein context to the human protein-protein interaction network to reveal meaningful interactions. PLoS Comput Biol 9:e1002860CrossRefGoogle Scholar
  11. 11.
    Gray KA, Yates B, Seal RL, Wright MW, Bruford EA (2015) Genenames.org: the HGNC resources in 2015. Nucleic Acids Res 43:D1079–D1085CrossRefGoogle Scholar
  12. 12.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al (2000) Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 25:25–29CrossRefGoogle Scholar
  13. 13.
    Beyerly E (1962) New medical subject heading lists: a comparative review of American and soviet works. Bull Med Libr Assoc 50:196–202PubMedPubMedCentralGoogle Scholar
  14. 14.
    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504CrossRefGoogle Scholar
  15. 15.
    Mi H, Muruganujan A, Casagrande JT, Thomas PD (2013) Large-scale gene function analysis with the PANTHER classification system. Nat Protoc 8:1551–1566CrossRefGoogle Scholar
  16. 16.
    R Development Core Team (2014) R: a language and environment for statistical computing. the R Foundation for Statistical Computing, ViennaGoogle Scholar
  17. 17.
    McKusick VA (2007) Mendelian inheritance in man and its online version, OMIM. Am J Hum Genet 80:588–604CrossRefGoogle Scholar
  18. 18.
    Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M et al (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368CrossRefGoogle Scholar
  19. 19.
    Pletscher-Frankild S, Palleja A, Tsafou K, Binder JX, Jensen LJ (2015) DISEASES: text mining and data integration of disease-gene associations. Methods 74:83–89CrossRefGoogle Scholar
  20. 20.
    Gaur P, Munjhal A, Lal SK (2011) Influenza virus and cell signaling pathways. Med Sci Monit 17:RA148–RA154CrossRefGoogle Scholar
  21. 21.
    Ludwig S, Planz O, Pleschka S, Wolff T (2003) Influenza-virus-induced signaling cascades: targets for antiviral therapy? Trends Mol Med 9:46–52CrossRefGoogle Scholar
  22. 22.
    Eierhoff T, Hrincius ER, Rescher U, Ludwig S, Ehrhardt C (2010) The epidermal growth factor receptor (EGFR) promotes uptake of influenza a viruses (IAV) into host cells. PLoS Pathog 6:e1001099CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institut Pasteur, Bioinformatics and Biostatistics Hub, C3BI, USR 3756 CNRSParisFrance
  2. 2.SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment GénopodeLausanneSwitzerland
  3. 3.IQVIABaselSwitzerland
  4. 4.Yocto Group LimitedZurichSwitzerland

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