Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.
References
[1]
Whitfield ML, Sherlock G, Saldanha AJ, Murray JI, Ball CA, et al. (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13: 1977–2000. doi: 10.1091/mbc.02-02-0030.
[2]
Hegde P, Qi R, Gaspard R, Abernathy K, Dharap S, et al. (2001) Identification of tumor markers in models of human colorectal cancer using a 19,200-element complementary DNA microarray. Cancer Res 61: 7792–7797.
[3]
Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, et al. (2002) Gene-microarray analysis of multiple sclerosis lesions yields new targets validated in autoimmune encephalomyelitis. Nat Med 8: 500–508. doi: 10.1038/nm0502-500
[4]
Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447: 661–678.
[5]
Schymick JC, Scholz SW, Fung HC, Britton A, Arepalli S, et al. (2007) Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data. Lancet Neurol 6: 322–328. doi: 10.1016/s1474-4422(07)70037-6
[6]
Kittler R, Pelletier L, Heninger AK, Slabicki M, Theis M, et al. (2007) Genome-scale RNAi profiling of cell division in human tissue culture cells. Nat Cell Biol 9: 1401–1412. doi: 10.1038/ncb1659
[7]
Krishnan MN, Ng A, Sukumaran B, Gilfoy FD, Uchil PD, et al. (2008) RNA interference screen for human genes associated with West Nile virus infection. Nature 455: 242–245. doi: 10.1038/nature07207
[8]
Ozsolak F, Song JS, Liu XS, Fisher DE (2007) High-throughput mapping of the chromatin structure of human promoters. Nat Biotechnol 25: 244–248. doi: 10.1038/nbt1279
[9]
Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101: 6062–6067. doi: 10.1073/pnas.0400782101
[10]
Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, et al. (2000) Molecular portraits of human breast tumours. Nature 406: 747–752. doi: 10.1038/35021093
[11]
Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210. doi: 10.1093/nar/30.1.207
[12]
Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, et al. (2006) The PeptideAtlas project. Nucleic Acids Res 34: D655–D658. doi: 10.1093/nar/gkj040
[13]
Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28: 27–30. doi: 10.1093/nar/28.1.27
[14]
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium Nat Genet 25: 25–29.
[15]
Klein TE, Chang JT, Cho MK, Easton KL, Fergerson R, et al. (2001) Integrating genotype and phenotype information: an overview of the PharmGKB project. Pharmacogenetics Research Network and Knowledge Base Pharmacogenomics J 1: 167–170. doi: 10.1038/sj.tpj.6500035
[16]
Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, et al. (2002) DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 30: 303–305. doi: 10.1093/nar/30.1.303
[17]
Bader G, Betel D, Hogue C (2003) BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 31: 248–250. doi: 10.1093/nar/gkg056
[18]
Snel B, Lehmann G, Bork P, Huynen MA (2000) STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res 28: 3442–3444. doi: 10.1093/nar/28.18.3442
[19]
Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q (2008) GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 9 Suppl 1: S4. doi: 10.1186/gb-2008-9-s1-s4
[20]
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, et al. (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38: W214–W220. doi: 10.1093/nar/gkq537
[21]
Lee I, Ambaru B, Thakkar P, Marcotte EM, Rhee SY (2010) Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana. Nat Biotechnol 28: 149–156. doi: 10.1038/nbt.1603
[22]
Lee I, Date SV, Adai AT, Marcotte EM (2004) A probabilistic functional network of yeast genes. Science 306: 1555–1558. doi: 10.1126/science.1099511
[23]
Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM (2011) Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Res 21: 1109–1121. doi: 10.1101/gr.118992.110
[24]
Kim WK, Krumpelman C, Marcotte EM (2008) Inferring mouse gene functions from genomic-scale data using a combined functional network/classification strategy. Genome Biol 9 Suppl 1: S5. doi: 10.1186/gb-2008-9-s1-s5
[25]
Rhodes DR, Tomlins SA, Varambally S, Mahavisno V, Barrette T, et al. (2005) Probabilistic model of the human protein-protein interaction network. Nat Biotechnol 23: 951–959. doi: 10.1038/nbt1103
[26]
Segal E, Wang H, Koller D (2003) Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics 19: i264–i272. doi: 10.1093/bioinformatics/btg1037
[27]
Chen X, Lin MZ, Shen XL (2011) PAIR: the predicted Arabidopsis interactome resource. Nucleic Acids Res 39: D1134–D1140. doi: 10.1093/nar/gkq938
[28]
Myers C, Robson D, Wible A, Hibbs M, Chiriac C, et al. (2005) Discovery of biological networks from diverse functional genomic data. Genome Biol 6: R114–R114. doi: 10.1186/gb-2005-6-13-r114
[29]
Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc Natl Acad Sci U S A100: 8348–8353. doi: 10.1073/pnas.0832373100
Vastrik I, D'Eustachio P, Schmidt E, Gopinath G, Croft D, et al. (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol 8: R39. doi: 10.1186/gb-2007-8-3-r39
[32]
Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK, et al. (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13: 2363–2371. doi: 10.1101/gr.1680803
[33]
Huttenhower C, Haley EM, Hibbs MA, Dumeaux V, Barrett DR, et al. (2009) Exploring the human genome with functional maps. Genome Res 19: 1093–1106. doi: 10.1101/gr.082214.108
[34]
Sokal RR, Rohlf FJ (1995) Biometry : the principles and practice of statistics in biological research. New York: W.H. Freeman. xix, 887 p.
[35]
Hamosh A, Scott AF, Amberger J, Valle D, McKusick VA (2000) Online Mendelian Inheritance in Man (OMIM). Human Mutation 15: 57–61. doi: 10.1002/(sici)1098-1004(200001)15:1<57::aid-humu12>3.0.co;2-g
[36]
Greene CS, Troyanskaya OG (2012) Accurate evaluation and analysis of functional genomics data and methods. Ann N Y Acad Sci 1260: 95–100. doi: 10.1111/j.1749-6632.2011.06383.x
[37]
Hibbs MA, Myers CL, Huttenhower C, Hess DC, Li K, et al. (2009) Directing experimental biology: a case study in mitochondrial biogenesis. PLoS Comput Biol 5: e1000322 doi:10.1371/journal.pcbi.1000322.
[38]
Huttenhower C, Hibbs M, Myers C, Troyanskaya OG (2006) A scalable method for integration and functional analysis of multiple microarray datasets. Bioinformatics 22: 2890–2897. doi: 10.1093/bioinformatics/btl492
[39]
Hess DC, Myers CL, Huttenhower C, Hibbs MA, Hayes AP, et al. (2009) Computationally driven, quantitative experiments discover genes required for mitochondrial biogenesis. PLoS Genet 5: e1000407 doi:10.1371/journal.pgen.1000407.
[40]
Guan Y, Dunham M, Caudy A, Troyanskaya O (2010) Systematic planning of genome-scale experiments in poorly studied species. PLoS Comput Biol 6: e1000698 doi:10.1371/journal.pcbi.1000698.
