Abstract
ConsensusPathDB consists of a comprehensive collection of human (as well as mouse and yeast) molecular interaction data integrated from 32 different public repositories and a web interface featuring a set of computational methods and visualization tools to explore these data. This protocol describes the use of ConsensusPathDB (http://consensuspathdb.org) with respect to the functional and network-based characterization of biomolecules (genes, proteins and metabolites) that are submitted to the system either as a priority list or together with associated experimental data such as RNA-seq. The tool reports interaction network modules, biochemical pathways and functional information that are significantly enriched by the user's input, applying computational methods for statistical over-representation, enrichment and graph analysis. The results of this protocol can be observed within a few minutes, even with genome-wide data. The resulting network associations can be used to interpret high-throughput data mechanistically, to characterize and prioritize biomarkers, to integrate different omics levels, to design follow-up functional assay experiments and to generate topology for kinetic models at different scales.
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References
Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).
Yan, L. et al. Single-cell RNA-seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131–1139 (2013).
Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).
Khatri, P., Sirota, M. & Butte, A.J. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comp. Biol. 8, e1002375 (2012).
Maciejewski, H. Gene set analysis methods: statistical models and methodological differences. Brief Bioinf. 15, 504–518 (2014).
Taylor, I.W. et al. Dynamic modularity on protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27, 199–204 (2009).
Kamburov, A., Wierling, C., Lehrach, H. & Herwig, R. ConsensusPathDB–a database for integrating human functional interaction networks. Nucleic Acids Res. 37, D623–D628 (2009).
Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–D1056 (2015).
Vidal, M., Cusick, M.E. & Barabasi, A.L. Interactome networks and human disease. Cell 144, 986–998 (2011).
Stumpf, M.P.H. et al. Estimating the size of the human interactome. Proc. Natl. Acad. Sci. USA 105, 6959–6964 (2008).
Bader, G.D., Cary, M.P. & Sander, C. Pathguide: a pathway resource list. Nucleic Acids Res. 34, D504–D506 (2006).
Hoehe, M.R. et al. Multiple haplotype-resolved genomes reveal population patterns of gene and protein diplotypes. Nat. Commun. 5, 5569 (2014).
Grossmann, A. et al. Phospho-tyrosine dependent protein-protein interaction network. Mol. Syst. Biol. 11, 794 (2015).
Li, A.H. et al. Analysis of loss-of-function variants and 20 risk factor phenotypes in 8,554 individuals identifies loci influencing chronic disease. Nat. Genet. 47, 640–642 (2015).
Timme, S. et al. STAT3 expression, activity and functional consequences of STAT3 inhibition in esophageal squamous cell carcinomas and Barrett's adenocarcinomas. Oncogene 33, 3256–3266 (2014).
Sun, C. et al. High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat. Genet. 48, 323–330 (2016).
Seumois, G. et al. Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility. Nat. Immunol. 15, 777–788 (2014).
Kallio, M.A. et al. Chipster: user-friendly analysis software for microarray and other high-throughput data. BMC Genomics 12, 507 (2011).
Saito, R. et al. A travel guide to Cytoscape plugins. Nat. Methods 9, 1069–1076 (2012).
Pentchev, K., Ono, K., Herwig, R., Ideker, T. & Kamburov, A. Evidence mining and novelty assessment of protein-protein interactions with the ConsensusPathDB plugin for Cytoscape. Bioinformatics 26, 2796–2797 (2010).
Hofree, M., Shen, J.P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).
Yildirimman, Y. et al. Human embryonic stem cell derived hepatocyte-like cells as a tool for in vitro hazard assessment of chemical carcinogenicity. Tox Sci. 124, 278–290 (2011).
Huang, D.W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protocol. 4, 44–56 (2009).
Krämer, A., Green, J., Pollard, J. Jr. & Tugendreich, S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics 30, 523–530 (2014).
Chen, E.Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf. 14, 128 (2014).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).
Xia, J. & Wishart, D.S. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat. Protocol. 6, 743–760 (2011).
Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protocol. 2, 2366–2382 (2007).
Berger, S.I., Posner, J.M. & Ma'ayan, A. Genes2Networks: connecting lists of gene symbols using mammalian protein interaction databases. BMC Bioinf. 8, 372 (2007).
Liberzon, A. et al. Molecular signature database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).
Cerami, E.G. et al. Pathway commons, a web resource for biological pathway data. Nucleic Acids Res. 39, D685–D690 (2011).
Oda, K., Matsuoka, Y., Funahashi, A. & Kitano, H. A comprehensive pathway map of epidermal growth factor receptor signalling. Mol. Syst. Biol. 1, 2005.0010 (2005).
Vanholder, R. et al. Review on uremic toxins: classification, concentration, and interindividual variability. Kidney Int. 63, 1934–1943 (2003).
Kanehisa, M. et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014).
Fabregat, A. et al. The Reactome pathway knowledgebase. Nucleic Acids Res. 44, D481–D487 (2016).
Kutmon, M. et al. Wikipathways: capturing the full diversity of pathway knowledge. Nucleic Acids Res. 44, D488–D494 (2016).
Kamburov, A., Grossmann, A., Herwig, R. & Stelzl, U. Cluster-based assessment of protein-protein interaction confidence. BMC Bioinf. 13, 262 (2012).
Goldberg, D.S. & Roth, F.P. Assessing experimentally derived interactions in a small world. Proc. Natl. Acad. Sci. USA 100, 4372–4376 (2003).
Kuchaiev, O., Rasajski, M., Higham, D.J. & Przulj, N. Geometric de-noising of protein-protein interaction networks. PLoS Comp. Biol. 5, e1000454 (2009).
Kamburov, A., Stelzl, U., Lehrach, H. & Herwig, R. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Res. 41, D793–D800 (2013).
Yu, G. et al. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26, 976–978 (2010).
Kamburov, A., Stelzl, U. & Herwig, R. IntScore: a web tool for confidence scoring of biological interactions. Nucleic Acids Res. 40, W140–W146 (2012).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289–300 (1995).
Kamburov, A. et al. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res. 39, D712–D717 (2011).
Lehmann, E. Nonparametrics: Statistical Methods Based on Ranks (San Francisco, California: Holden-Day, 1975).
Adjaye, J. et al. Primary differentiation in the human blastocyst: comparative molecular portraits of inner cell mass and trophectoderm cells. Stem Cells 23, 1514–1525 (2005).
Zheng, Y. et al. Temporal regulation of EGF signaling networks by the scaffold protein Shc1. Nature 499, 166–171 (2012).
Li, S. et al. Structural basis for inhibition of the epidermal growth factor receptor by cetuximab. Cancer Cell 7, 301–311 (2005).
Hanahan, D. & Weinberg, R.A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Reyes, M. & Benet, L.Z. Effects of uremic toxins and metabolism of different biopharmaceutics drug disposition classification system xenobiotics. J. Pharm. Sci. 100, 3831–3842 (2011).
Watanabe, H. et al. p-Cresyl sulfate, a uremic toxin, causes vascular endothelial and smooth muscle cell damages by inducing oxidative stress. Pharmacol. Res. Perspect. 3, e00092 (2015).
Niakan, K.K., Han, J., Pedersen, R.A., Simon, C. & Pera, R.A.R. Human pre-implantation embryo development. Development 139, 829–841 (2012).
Acknowledgements
We are grateful to all scientists who provided annotation of the original molecular interaction data and are allowing automated access to their databases. Integration of interaction data could be achieved only because the original data were provided in an excellently documented way.
This work was financed in part by the European Commission under its 7th Framework Programme (HeCaToS 602156 to R.H.) and the Max Planck Society.
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R.H. and A.K. conceived ConsensusPathDB, designed the protocol and wrote the manuscript; A.K. developed ConsensusPathDB; C.H. and M.L. conducted the procedure, performed data analysis and contributed to the manuscript.
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Herwig, R., Hardt, C., Lienhard, M. et al. Analyzing and interpreting genome data at the network level with ConsensusPathDB. Nat Protoc 11, 1889–1907 (2016). https://doi.org/10.1038/nprot.2016.117
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DOI: https://doi.org/10.1038/nprot.2016.117
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