Abstract
Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rual J-F, Venkatesan K, Hao T et al (2005) Towards a proteome-scale map of the human protein–protein interaction network. Nature 437:1173–1178
Ewing RM, Chu P, Elisma F et al (2007) Large-scale mapping of human protein–protein interactions by mass spectrometry. Mol Syst Biol 3:89
Fabregat A, Sidiropoulos K, Garapati P et al (2016) The Reactome pathway knowledgebase. Nucleic Acids Res 44:D481–D487
Gerstein MB, Kundaje A, Hariharan M et al (2012) Architecture of the human regulatory network derived from ENCODE data. Nature 489:91–100
Jiang C, Xuan Z, Zhao F et al (2007) TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35:D137–D140
Wu G, Dawson E, Duong A et al (2014) ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res 3:146
Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504
Wu G, Feng X, Stein L (2010) A human functional protein interaction network and its application to cancer data analysis. Genome Biol 11:R53
UniProt Consortium (2015) UniProt: a hub for protein information. Nucleic Acids Res 43:D204–D212
McGarvey PB, Huang H, Barker WC et al (2000) PIR: a new resource for bioinformatics. Bioinformatics 16:290–291
Kanehisa M, Sato Y, Kawashima M et al (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44:D457–D462
Schaefer CF, Anthony K, Krupa S et al (2009) PID: the pathway interaction database. Nucleic Acids Res 37:D674–D679
Mi H, Poudel S, Muruganujan A et al (2016) PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44:D336–D342
Razick S, Magklaras G, Donaldson IM (2008) iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinf 9:405
Lee HK, Hsu AK, Sajdak J et al (2004) Coexpression analysis of human genes across many microarray data sets. Genome Res 14:1085–1094
Prieto C, Risueno A, Fontanillo C et al (2008) Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One 3:e3911
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25:25–29
Flicek P, Aken BL, Ballester B et al (2010) Ensembl’s 10th year. Nucleic Acids Res 38(Database):D557–D562
Finn RD, Coggill P, Eberhardt RY et al (2016) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44:D279–D285
Cancer Genome Atlas Research Network (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061–1068
Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103:8577–8582
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Wu, G., Haw, R. (2017). Functional Interaction Network Construction and Analysis for Disease Discovery. In: Wu, C., Arighi, C., Ross, K. (eds) Protein Bioinformatics. Methods in Molecular Biology, vol 1558. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6783-4_11
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6783-4_11
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6781-0
Online ISBN: 978-1-4939-6783-4
eBook Packages: Springer Protocols