Principles and methods of integrative genomic analyses in cancer (original) (raw)
Hood, L., Heath, J. R., Phelps, M. E. & Lin, B. Systems biology and new technologies enable predictive and preventative medicine. Science306, 640–643 (2004). ArticleCASPubMed Google Scholar
Ideker, T., Galitski, T. & Hood, L. A new approach to decoding life: systems biology. Annu. Rev. Genomics Hum. Genet.2, 343–372 (2001). ArticleCASPubMed Google Scholar
Auffray, C. & Hood, L. Editorial: Systems biology and personalized medicine - the future is now. Biotechnol. J.7, 938–939 (2012). This paper outlines the definitions and state of the art methodology in systems biology. ArticleCASPubMed Google Scholar
Tian, Q., Price, N. D. & Hood, L. Systems cancer medicine: towards realization of predictive, preventive, personalized and participatory (P4) medicine. J. Intern. Med.271, 111–121 (2012). ArticleCASPubMedPubMed Central Google Scholar
Schadt, E. Eric Schadt. Interview by H. Craig Mak. Nature Biotech.30, 769–770 (2012). ArticleCAS Google Scholar
Joyce, A. R. & Palsson, B. Ø. The model organism as a system: integrating 'omics' data sets. Nat. Rev. Mol. Cell. Biol.7, 198–210 (2006). ArticleCASPubMed Google Scholar
Martin, M. Semantic Web may be cancer information's next step forward. J. Natl. Cancer Inst.103, 1215–1218 (2011). ArticlePubMed Google Scholar
Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res.39, D945–D950 (2011). ArticleCASPubMed Google Scholar
Cheung, H. W. et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl Acad. Sci. USA108, 12372–12377 (2011). ArticlePubMedPubMed Central Google Scholar
Martin, M. Rewriting the mathematics of tumor growth. J. Natl Cancer Inst.103, 1564–1565 (2011). ArticlePubMed Google Scholar
Forbes, S. A. et al. The Catalogue of Somatic Mutations in Cancer (COSMIC). Curr. Protoc. Hum. Genet. Chapter 10, Unit 10.11 (2008).
International Cancer Genome Consortium. International network of cancer genome projects. Nature464, 993–998 (2010). This is a description and the first results of the ICGC, a worldwide endeavour to characterize a wide range of tumours by next-generation sequencing.
The Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genet.45, 1113–1120 (2013).
ENCODE Project Consortium. A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol.9, e1001046 (2011). This is a genome-wide encyclopaedia of structural and regulatory elements in the genome.
Quigley, D. A. et al. The 5p12 breast cancer susceptibility locus affects MRPS30 expression in estrogen-receptor positive tumors. Mol. Oncol.8, 273–284 (2013). ArticleCASPubMedPubMed Central Google Scholar
Fletcher, M. N. C. et al. Master regulators of FGFR2 signalling and breast cancer risk. Nature Commun.4, 2464 (2013). ArticleCAS Google Scholar
Chin, L., Andersen, J. N. & Futreal, P. A. Cancer genomics: from discovery science to personalized medicine. Nature Med.17, 297–303 (2011). ArticleCASPubMed Google Scholar
Yuan, Y. et al. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med.4, 157ra143–157ra143 (2012). ArticlePubMed Google Scholar
Kilpinen, S. et al. Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome Biol.9, R139 (2008). ArticleCASPubMedPubMed Central Google Scholar
Wong, A. K. et al. IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Res.40, W484–W490 (2012). ArticleCASPubMedPubMed Central Google Scholar
Engreitz, J. M., Daigle, B. J., Marshall, J. J. & Altman, R. B. Independent component analysis: mining microarray data for fundamental human gene expression modules. J. Biomed. Inform.43, 932–944 (2010). ArticleCASPubMedPubMed Central Google Scholar
Engreitz, J. M. et al. ProfileChaser: searching microarray repositories based on genome-wide patterns of differential expression. Bioinformatics27, 3317–3318 (2011). ArticleCASPubMedPubMed Central Google Scholar
Madhavan, S. et al. Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol. Cancer Res.7, 157–167 (2009). This paper describes integrated genomic analyses in medicine. ArticleCASPubMedPubMed Central Google Scholar
Gentleman, R. C. et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol.5, R80 (2004). ArticlePubMedPubMed Central Google Scholar
Cline, M. S. et al. Integration of biological networks and gene expression data using Cytoscape. Nature Protocol.2, 2366–2382 (2007). This paper describes a widely used space for genomic analysis and visualization. ArticleCAS Google Scholar
Gundem, G. et al. IntOGen: integration and data mining of multidimensional oncogenomic data. Nature Methods7, 92–93 (2010). ArticleCASPubMed Google Scholar
Margolin, A. A. et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci. Transl. Med.5, 181re1–181re1 (2013). ArticlePubMedPubMed Central Google Scholar
Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L. & Nolan, G. P. Computational solutions to large-scale data management and analysis. Nature Rev. Genet.11, 647–657 (2010). ArticleCASPubMed Google Scholar
Quigley, D. & Balmain, A. Systems genetics analysis of cancer susceptibility: from mouse models to humans. Nature Rev. Genet.10, 651–657 (2009). ArticleCASPubMed Google Scholar
Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature501, 506–511 (2013). This paper describes an integration of next-generation sequencing data from DNA and RNA levels that reveals the structure of many regulatory elements. ArticleCASPubMedPubMed Central Google Scholar
Chin, K. et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell10, 529–541 (2006). ArticleCASPubMed Google Scholar
Lando, M. et al. Gene dosage, expression, and ontology analysis identifies driver genes in the carcinogenesis and chemoradioresistance of cervical cancer. PLoS Genet.5, e1000719 (2009). ArticleCASPubMedPubMed Central Google Scholar
Sun, Z. et al. Integrated analysis of gene expression, CpG island methylation, and gene copy number in breast cancer cells by deep sequencing. PLoS ONE6, e17490 (2011). ArticleCASPubMedPubMed Central Google Scholar
Ovaska, K. et al. Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med.2, 65 (2010). ArticleCASPubMedPubMed Central Google Scholar
Aure, M. R. et al. Identifying in-trans process associated genes in breast cancer by integrated analysis of copy number and expression data. PLoS ONE8, e53014 (2013). ArticleCASPubMedPubMed Central Google Scholar
Chibon, F. et al. Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity. Nature Med.16, 781–787 (2010). ArticleCASPubMed Google Scholar
Chari, R., Coe, B. P., Vucic, E. A., Lockwood, W. W. & Lam, W. L. An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer. BMC Syst. Biol.4, 67 (2010). ArticleCASPubMedPubMed Central Google Scholar
Louhimo, R. & Hautaniemi, S. CNAmet: an R package for integrating copy number, methylation and expression data. Bioinformatics27, 887–888 (2011). ArticleCASPubMed Google Scholar
R Core Team (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
Shen, Y., Sun, W. & Li, K.-C. Dynamically weighted clustering with noise set. Bioinformatics26, 341–347 (2010). ArticleCASPubMed Google Scholar
Curtis, C. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature486, 346–352 (2012). ArticleCASPubMedPubMed Central Google Scholar
Yuan, Y., Savage, R. S. & Markowetz, F. Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput. Biol.7, e1002227 (2011). ArticleCASPubMedPubMed Central Google Scholar
Bøvelstad, H. M. et al. Predicting survival from microarray data—a comparative study. Bioinformatics23, 2080–2087 (2007). ArticleCASPubMed Google Scholar
Tibshirani, R. Regression shrinkage and selection via the Lasso. J. R. Statist. Soc. Series B.58, 267–288 (1996). Google Scholar
Nowak, G., Hastie, T., Pollack, J. R. & Tibshirani, R. A fused lasso latent feature model for analyzing multi-sample aCGH data. Biostatistics12, 776–791 (2011). ArticlePubMedPubMed Central Google Scholar
Mankoo, P. K., Shen, R., Schultz, N., Levine, D. A. & Sander, C. Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS ONE6, e24709 (2011). ArticleCASPubMedPubMed Central Google Scholar
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Statist. Soc.: Series B (Statist. Methodol.)67, 301–320 (2005). Article Google Scholar
Segal, E., Friedman, N., Koller, D. & Regev, A. A module map showing conditional activity of expression modules in cancer. Nature Genet.36 1090–1098 (2004). This landmark publication establishes the principles of identification of regulatory modules. ArticlePubMed Google Scholar
Kelder, T. et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res.40, D1301–D1307 (2012). ArticleCASPubMed Google Scholar
Rhee, S. Y., Wood, V., Dolinski, K. & Draghici, S. Use and misuse of the gene ontology annotations. Nature Rev. Genet.9, 509–515 (2008). ArticleCASPubMed Google Scholar
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA102, 15545–15550 (2005). ArticleCASPubMedPubMed Central Google Scholar
Dittrich, M. T., Klau, G. W., Rosenwald, A., Dandekar, T. & Müller, T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics24, i223–i231 (2008). ArticleCASPubMedPubMed Central Google Scholar
Qiu, Y.-Q., Zhang, S., Zhang, X.-S. & Chen, L. Detecting disease associated modules and prioritizing active genes based on high throughput data. BMC Bioinformatics11, 26 (2010). ArticleCASPubMedPubMed Central Google Scholar
Guo, Z. et al. Edge-based scoring and searching method for identifying condition-responsive protein-protein interaction sub-network. Bioinformatics23, 2121–2128 (2007). ArticleCASPubMed Google Scholar
Chuang, H.-Y. et al. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood120, 2639–2649 (2012). ArticleCASPubMedPubMed Central Google Scholar
Doniger, S. W. et al. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol.4, R7 (2003). ArticlePubMedPubMed Central Google Scholar
Tarca, A. L. et al. A novel signaling pathway impact analysis. Bioinformatics25, 75–82 (2009). ArticleCASPubMed Google Scholar
Efroni, S., Schaefer, C. F. & Buetow, K. H. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS ONE2, e425 (2007). ArticleCASPubMedPubMed Central Google Scholar
Drier, Y., Sheffer, M. & Domany, E. Pathway-based personalized analysis of cancer. Proc. Natl Acad. Sci. USA110, 6388–6393 (2013). ArticlePubMedPubMed Central Google Scholar
Mayer, C.-D., Lorent, J. & Horgan, G. W. Exploratory analysis of multiple omics datasets using the adjusted RV coefficient. Stat. Appl. Genet. Mol. Biol.10, Article 14 (2011).
Lê Cao, K.-A., González, I. & Déjean, S. integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics25, 2855–2856 (2009). ArticleCASPubMedPubMed Central Google Scholar
Margolin, A. A., Wang, K., Califano, A. & Nemenman, I. Multivariate dependence and genetic networks inference. IET Syst. Biol.4, 428–440 (2010). ArticleCASPubMed Google Scholar
Margolin, A. A. & Califano, A. Theory and limitations of genetic network inference from microarray data. Ann. NY Acad. Sci.1115, 51–72 (2007). ArticleCASPubMed Google Scholar
Koller, D. & Friedman, N. Probabilistic graphical models: principles and techniques. (Massachusetts Institute of Technology, 2009). This study describes one of the basic approaches for studying gene–gene dependencies. Google Scholar
Califano, A., Butte, A. J., Friend, S., Ideker, T. & Schadt, E. Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nature Genet.44, 841–847 (2012)). This paper describes a fundamental attempt to identify genotype–phenotype interactions. ArticleCASPubMed Google Scholar
Ideker, T., Ozier, O., Schwikowski, B. & Siegel, A. F. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics18 (Suppl. 1), S233–240 (2002). ArticlePubMed Google Scholar
Breitling, R., Amtmann, A. & Herzyk, P. Graph-based iterative Group Analysis enhances microarray interpretation. BMC Bioinformatics5, 100 (2004). ArticleCASPubMedPubMed Central Google Scholar
Stingo, F. C. & Vannucci, M. Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data. Bioinformatics27, 495–501 (2011). ArticleCASPubMed Google Scholar
Bauer, S., Gagneur, J. & Robinson, P. N. GOing Bayesian: model-based gene set analysis of genome-scale data. Nucleic Acids Res.38, 3523–3532 (2010). ArticleCASPubMedPubMed Central Google Scholar
Newton, M. A., He, Q. & Kendziorski, C. A model-based analysis to infer the functional content of a gene list. Stat. Appl. Genet. Mol. Biol.11, http://dx.doi.org/10.2202/1544-6115.1716 (2012).
Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genet.34, 166–176 (2003). ArticlePubMed Google Scholar
Segal, E., Friedman, N., Kaminski, N., Regev, A. & Koller, D. From signatures to models: understanding cancer using microarrays. Nature Genet.37 S38–S45 (2005). ArticleCASPubMed Google Scholar
Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics26, i237–i245 (2010). This paper describes an application of approaches from the probabilistic graphical models in the identification of pathways or dependencies deviating from a given norm. ArticleCASPubMedPubMed Central Google Scholar
Kristensen, V. N. et al. Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling. Proc. Natl Acad. Sci. USA109, 2802–2807 (2012). ArticlePubMed Google Scholar
Imoto, S. et al. Combining microarrays and biological knowledge for estimating gene networks via bayesian networks. J. Bioinform. Comput. Biol.2, 77–98 (2004). ArticleCASPubMed Google Scholar
Birtwistle, M. R. et al. Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol. Syst. Biol.3, 144 (2007). ArticleCASPubMedPubMed Central Google Scholar
Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature486, 395–399 (2012). ArticleCASPubMed Google Scholar
Cancer, Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature455, 1061–1068 (2008).
Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nature Genet.45, 1127–1133 (2013). ArticleCASPubMed Google Scholar
Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nature Genet.45, 1134–1140 (2013). ArticleCASPubMed Google Scholar
Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genet.45, 1113–1120 (2013).
Newman, M. E. J. Fast algorithm for detecting community structure in networks. Phys. Rev. E Stat. Nonlin Soft Matter Phys.69, 066133 (2004). ArticleCASPubMed Google Scholar
Louhimo, R., Lepikhova, T., Monni, O. & Hautaniemi, S. Comparative analysis of algorithms for integration of copy number and expression data. Nature Methods9, 351–355 (2012). ArticleCASPubMed Google Scholar
Solvang, H. K., Lingjærde, O. C., Frigessi, A., Børresen-Dale, A.-L. & Kristensen, V. N. Linear and non-linear dependencies between copy number aberrations and mRNA expression reveal distinct molecular pathways in breast cancer. BMC Bioinformatics12, 197 (2011). ArticlePubMedPubMed Central Google Scholar
Heiser, L. M. et al. Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc. Natl Acad. Sci. USA109, 2724–2729 (2012). ArticlePubMed Google Scholar
Hoshino, D. et al. Network analysis of the focal adhesion to invadopodia transition identifies a PI3K-PKCα invasive signaling axis. Sci. Signal.5, ra66 (2012). ArticleCASPubMedPubMed Central Google Scholar
Stronach, E. A. et al. DNA-PK mediates AKT activation and apoptosis inhibition in clinically acquired platinum resistance. Neoplasia13, 1069–1080 (2011). ArticleCASPubMedPubMed Central Google Scholar
Mok, T. S. et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med.361, 947–957 (2009). ArticleCASPubMed Google Scholar
Shepherd, F. A. et al. Erlotinib in previously treated non-small-cell lung cancer. N. Engl. J. Med.353, 123–132 (2005). ArticleCASPubMed Google Scholar
Piccart-Gebhart, M. J. et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N. Engl. J. Med.353, 1659–1672 (2005). ArticleCASPubMed Google Scholar
Romond, E. H. et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N. Engl. J. Med.353, 1673–1684 (2005). ArticleCASPubMed Google Scholar
Jonker, D. J. et al. Cetuximab for the treatment of colorectal cancer. N. Engl. J. Med.357, 2040–2048 (2007). ArticleCASPubMed Google Scholar
Karapetis, C. S. et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N. Engl. J. Med.359, 1757–1765 (2008). ArticleCASPubMed Google Scholar
Iadevaia, S., Lu, Y., Morales, F. C., Mills, G. B. & Ram, P. T. Identification of optimal drug combinations targeting cellular networks: integrating phospho-proteomics and computational network analysis. Cancer Res.70, 6704–6714 (2010). ArticleCASPubMedPubMed Central Google Scholar
van de Vijver, M. J. et al. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med.347, 1999–2009 (2002). ArticleCASPubMed Google Scholar
Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med.351, 2817–2826 (2004). ArticleCASPubMed Google Scholar
Cheng, W.-Y., Ou Yang, T.-H. & Anastassiou, D. Biomolecular events in cancer revealed by attractor metagenes. PLoS Comput. Biol.9, e1002920 (2013). ArticleCASPubMedPubMed Central Google Scholar
Cheng, W.-Y., Ou Yang, T.-H. & Anastassiou, D. Development of a prognostic model for breast cancer survival in an open challenge environment. Sci. Transl. Med.5, 181ra50–181ra50 (2013). ArticlePubMed Google Scholar
Sørlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA98, 10869–10874 (2001). ArticlePubMedPubMed Central Google Scholar
Sørlie, T. et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc. Natl Acad. Sci. USA100, 8418–8423 (2003). ArticleCASPubMedPubMed Central Google Scholar
Russnes, H. G. et al. Genomic architecture characterizes tumor progression paths and fate in breast cancer patients.Sci. Transl. Med.2, 38ra47–38ra47 (2010). ArticleCASPubMedPubMed Central Google Scholar
Chin, S.-F. et al. Using array-comparative genomic hybridization to define molecular portraits of primary breast cancers. Oncogene26, 1959–1970 (2007). ArticleCASPubMed Google Scholar
Cancer Genome Atlas Research Network. Comprehensive molecular portraits of human breast tumours. Nature490, 61–70 (2012).
Naume, B. et al. Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer. Mol. Oncol.1, 160–171 (2007). ArticlePubMedPubMed Central Google Scholar
Nordgard, S. H. et al. Genome-wide analysis identifies 16q deletion associated with survival, molecular subtypes, mRNA expression, and germline haplotypes in breast cancer patients. Genes Chromosomes Cancer47, 680–696 (2008). ArticleCASPubMed Google Scholar
Rønneberg, J. A. et al. Methylation profiling with a panel of cancer related genes: association with estrogen receptor, TP53 mutation status and expression subtypes in sporadic breast cancer. Mol. Oncol.5, 61–76 (2011). ArticleCASPubMed Google Scholar
Joshi, H., Bhanot, G., Børresen-Dale, A.-L. & Kristensen, V. N. Potential tumorigenic programs associated with TP53 mutation status reveal role of VEGF pathway. Br. J. Cancer107, 1722–1728 (2012). ArticleCASPubMedPubMed Central Google Scholar
Sun, Z. et al. Batch effect correction for genome-wide methylation data with Illumina Infinium platform. BMC Med. Genom.4, 84 (2011). ArticleCAS Google Scholar
Strehl, A. & Ghosh, J. Cluster ensembles — a knowledge reuse framework for combining partitionings. Journal of Machine Learning3, 583–617 (2002). Google Scholar
Monti, S., Tamayo, P., Mesirov, J. & Golub, T. Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Machine Learn.52, 91–118 (2003). Article Google Scholar
Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nature Med.17, 500–503 (2011). ArticleCASPubMed Google Scholar
Lee, M. & Kim, Y. CHESS (CgHExpreSS): a comprehensive analysis tool for the analysis of genomic alterations and their effects on the expression profile of the genome. BMC Bioinformatics10, 424 (2009). ArticleCASPubMedPubMed Central Google Scholar
Shen, R., Olshen, A. B. & Ladanyi, M. Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics25, 2906–2912 (2009). ArticleCASPubMedPubMed Central Google Scholar
Leday, G. G. R. & van de Wiel, M. A. PLRS: a flexible tool for the joint analysis of DNA copy number and mRNA expression data. Bioinformatics29, 1081–1082 (2013). ArticleCASPubMed Google Scholar
Yuan, Y., Curtis, C., Caldas, C. & Markowetz, F. A. Sparse regulatory network of copy-number driven gene expression reveals putative breast cancer oncogenes. IEEE/ACM Trans. Comput. Biol. Bioinform.9, 947–954 (2012). ArticleCASPubMed Google Scholar
Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature463, 318–325 (2010). ArticleCASPubMed Google Scholar
Saadi, A. et al. Stromal genes discriminate preinvasive from invasive disease, predict outcome, and highlight inflammatory pathways in digestive cancers. Proc. Natl Acad. Sci. USA107, 2177–2182 (2010). ArticlePubMedPubMed Central Google Scholar
Hamatani, T. et al. Global gene expression analysis identifies molecular pathways distinguishing blastocyst dormancy and activation. Proc. Natl Acad. Sci. USA101, 10326–10331 (2004). ArticleCASPubMedPubMed Central Google Scholar
Engström, P. G. et al. Digital transcriptome profiling of normal and glioblastoma-derived neural stem cells identifies genes associated with patient survival. Genome Med.4, 76 (2012). ArticleCASPubMedPubMed Central Google Scholar
Wu, J., Mao, X., Cai, T., Luo, J. & Wei, L. KOBAS server: a web-based platform for automated annotation and pathway identification. Nucleic Acids Res.34, W720–W724 (2006). ArticleCASPubMedPubMed Central Google Scholar
Xie, C. et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res.39, W316–W322 (2011). ArticleCASPubMedPubMed Central Google Scholar
Chang, H.-T. et al. Comprehensive analysis of microRNAs in breast cancer. BMCGenomics13, S18 (2012). Google Scholar
Tamborero, D., Lopez-Bigas, N. & Gonzalez-Perez, A. Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression. PLoS ONE8, e55489 (2013). ArticleCASPubMedPubMed Central Google Scholar
Warsow, G. et al. ExprEssence—revealing the essence of differential experimental data in the context of an interaction/regulation net-work. BMC Syst. Biol.4, 164 (2010). ArticlePubMedPubMed Central Google Scholar
Deshpande, R., Sharma, S., Verfaillie, C. M., Hu, W.-S. & Myers, C. L. A scalable approach for discovering conserved active subnetworks across species. PLoS Comput. Biol.6, e1001028 (2010). ArticleCASPubMedPubMed Central Google Scholar
Goffard, N., Frickey, T. & Weiller, G. PathExpress update: the enzyme neighbourhood method of associating gene-expression data with metabolic pathways. Nucleic Acids Res.37, W335–W339 (2009). ArticleCASPubMedPubMed Central Google Scholar
Bryant, W. A., Sternberg, M. J. E. & Pinney, J. W. AMBIENT: Active Modules for Bipartite Networks—using high-throughput transcriptomic data to dissect metabolic response. BMC Syst. Biol.7, 26 (2013). ArticlePubMedPubMed Central Google Scholar
Kirk, P., Griffin, J. E., Savage, R. S., Ghahramani, Z. & Wild, D. L. Bayesian correlated clustering to integrate multiple datasets. Bioinformatics28, 3290–3297 (2012). ArticleCASPubMedPubMed Central Google Scholar
Brodtkorb, M. et al. Whole-genome integrative analysis reveals expression signatures predicting transformation in follicular lymphoma. Blood, 123,1051–1054 (2014). ArticleCASPubMed Google Scholar