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Enrichment Map: A Network-Based Method for Gene-Set Enrichment Visualization and Interpretation
Open accessAbstract
BACKGROUND: Gene-set enrichment analysis is a useful technique to help functionally characterize large gene lists, such as the results of gene expression experiments. This technique finds functionally coherent gene-sets, such as pathways, that are statistically over-represented in a given gene list. Ideally, the number of resulting sets is smaller than the number of genes in the list, thus simplifying interpretation. However, the increasing number and redundancy of gene-sets used by many current enrichment analysis software works against this ideal. PRINCIPAL FINDINGS: To overcome gene-set redundancy and help in the interpretation of large gene lists, we developed "Enrichment Map", a network-based visualization method for gene-set enrichment results. Gene-sets are organized in a network, where each set is a node and edges represent gene overlap between sets. Automated network layout groups related gene-sets into network clusters, enabling the user to quickly identify the major enriched functional themes and more easily interpret the enrichment results. CONCLUSIONS: Enrichment Map is a significant advance in the interpretation of enrichment analysis. Any research project that generates a list of genes can take advantage of this visualization framework. Enrichment Map is implemented as a freely available and user friendly plug-in for the Cytoscape network visualization software (http://baderlab.org/Software/EnrichmentMap/).
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“Furthermore, gene set enrichment analysis (GSEA) revealed that the hypoxia pathway is impaired in miR-140G/G, but not in miR-140−/− chondrocytes”
From Gain-of-function mutation of microRNA-140 in human skeletal dysplasia (2019) · cites this paper as [26]
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
DAVID: Database for Annotation, Visualization, and Integrated Discovery
Dennis, G., Sherman, B. T., Hosack, D. A., Yang, J., Gao, W., Lane, H. C., & Lempicki, R. A. (2003) · Genome Biology · doi:10.1186/gb-2003-4-5-p3 · Cited by 9,327
Bindea, G., Mlecnik, B., Hackl, H., Charoentong, P., Tosolini, M., Kirilovsky, A., Fridman, W.-H., Pagès, F., Trajanoski, Z., & Galon, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp101 · Cited by 6,280
Maere, S., Heymans, K., & Kuiper, M. T. R. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti551 · Cited by 4,093
On the Interpretation of χ 2 from Contingency Tables, and the Calculation of P
Fisher, R. A. (1922) · Journal Of The Royal Statistical Society · doi:10.2307/2340521 · Cited by 2,975
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- S Draghici, 2003, Global functional profiling of gene expression., Genomics, 81, 98
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Metascape provides a biologist-oriented resource for the analysis of systems-level datasets
Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A. H., Tanaseichuk, O., Benner, C., & Chanda, S. K. (2019) · Nature Communications · doi:10.1038/s41467-019-09234-6 · Cited by 14,611
A pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964
Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., Wadi, L., Meyer, M., Wong, J., Xu, C., et al. (2019) · Nature Protocols · doi:10.1038/s41596-018-0103-9 · Cited by 1,970
Self-citationZhou, G., Soufan, O., Ewald, J., Hancock, R. E. W., Basu, N., & Xia, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz240 · Cited by 1,915
Bu, D., Luo, H., Huo, P., Wang, Z., Zhang, S., He, Z., Wu, Y., Zhao, L., Liu, J., Guo, J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab447 · Cited by 1,906
Dhariwal, A., Chong, J., Habib, S. M., King, I. L., Agellon, L. B., & Xia, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx295 · Cited by 1,946
Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations
MacParland, S. A., Liu, J. C., Ma, X.-Z., Innes, B. T., Bartczak, A. M., Gage, B. K., Manuel, J., Khuu, N., Echeverri, J., Linares, I., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06318-7 · Cited by 1,552
Self-citationA travel guide to Cytoscape plugins
Saito, R., Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P., Lotia, S., Pico, A. R., Bader, G. D., & Ideker, T. (2012) · Nature Methods · doi:10.1038/nmeth.2212 · Cited by 1,496
Self-citationUsing clusterProfiler to characterize multiomics data
Xu, S., Hu, E., Cai, Y., Xie, Z., Luo, X. M., Zhan, L., Tang, W., Wang, Q., Liu, B., Wang, R., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01020-z · Cited by 1,528
Franz, M., Rodriguez, H., Lopes, C., Zuberi, K., Montojo, J., Bader, G. D., & Morris, Q. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky311 · Cited by 1,413
Self-citationAssociation analysis identifies 65 new breast cancer risk loci
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemaçon, A., Soucy, P., Glubb, D. M., Rostamianfar, A., et al. (2017) · Nature · doi:10.1038/nature24284 · Cited by 1,380
g:Profiler—a web server for functional interpretation of gene lists (2016 update)
Reimand, J., Arak, T., Adler, P., Kolberg, L., Reisberg, S., Peterson, H., & Vilo, J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw199 · Cited by 1,229
Intertumoral Heterogeneity within Medulloblastoma Subgroups
Cavalli, F. M. G., Remke, M., Rampasek, L., Peacock, J., Shih, D. J. H., Luu, B., Garzia, L., Torchia, J., Nor, C., Morrissy, A. S., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.05.005 · Cited by 1,219
Self-citationVan Hove, H., Martens, L., Scheyltjens, I., De Vlaminck, K., Pombo Antunes, A. R., De Prijck, S., Vandamme, N., De Schepper, S., Van Isterdael, G., Scott, C. L., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0393-4 · Cited by 1,074
Microglial brain region-dependent diversity and selective regional sensitivities to aging
Grabert, K., Michoel, T., Karavolos, M. H., Clohisey, S., Baillie, J. K., Stevens, M. P., Freeman, T. C., Summers, K. M., & McColl, B. W. (2016) · Nature Neuroscience · doi:10.1038/nn.4222 · Cited by 1,151
Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J., & Peterson, H. (2020) · F1000Research · doi:10.12688/f1000research.24956.2 · Cited by 1,098
Marques, F. Z., Nelson, E., Chu, P.-Y., Horlock, D., Fiedler, A., Ziemann, M., Tan, J. K., Kuruppu, S., Rajapakse, N. W., El-Osta, A., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.024545 · Cited by 1,065
Proteomics of SARS-CoV-2-infected host cells reveals therapy targets
Bojkova, D., Klann, K., Koch, B., Widera, M., Krause, D., Ciesek, S., Cinatl, J., & Münch, C. (2020) · Nature · doi:10.1038/s41586-020-2332-7 · Cited by 1,010
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs
Sturm, D., Orr, B. A., Toprak, U. H., Hovestadt, V., Jones, D., Capper, D., Sill, M., Buchhalter, I., Northcott, P. A., Irina, L., et al. (2016) · Cell · doi:10.1016/j.cell.2016.01.015 · Cited by 990
A copy number variation map of the human genome
Zarrei, M., MacDonald, J. R., Merico, D., & Scherer, S. W. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3871 · Cited by 921
Self-citationAssociation analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci
Study, T. P., Schumacher, F. R., (APCB), A. P. C. B., Study, T. I., Canary PASS Investigators, Breast and Prostate Cancer Cohort Consortium (BPC3), The PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium, (CAPS), C. O. T. P. I. S., Prostate Cancer Genome-wide Association Study of Uncommon Susceptibility Loci (PEGASUS), The Genetic Associations and Mechanisms in Oncology (GAME-ON)/Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium, et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0142-8 · Cited by 902
An essential role for the IL-2 receptor in Treg cell function
Chinen, T., Kannan, A., Levine, A. G., Fan, X., Klein, U., Zheng, Y., Gasteiger, G., Feng, Y., Fontenot, J. D., & Rudensky, A. Y. (2016) · Nature Immunology · doi:10.1038/ni.3540 · Cited by 934
Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder
C Yuen, R. K., Merico, D., Bookman, M., L Howe, J., Thiruvahindrapuram, B., Patel, R. V., Whitney, J., Deflaux, N., Bingham, J., Wang, Z., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4524 · Cited by 844
Self-citationCicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789
Hirst, J. (2013) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-070511-103700 · Cited by 779
Inflammasome is a central player in the induction of obesity and insulin resistance
Stienstra, R., van Diepen, J. A., Tack, C. J., Zaki, M. H., van de Veerdonk, F. L., Perera, D., Neale, G. A., Hooiveld, G. J., Hijmans, A., Vroegrijk, I., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1100255108 · Cited by 727
A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains
Irimia, M., Weatheritt, R. J., Ellis, J. D., Parikshak, N. N., Gonatopoulos-Pournatzis, T., Babor, M., Quesnel-Vallières, M., Tapial, J., Raj, B., O’Hanlon, D., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.035 · Cited by 693
“Gene Ontology analyses The Gene Ontology (GO) network for the human genes harboring neural-regulated AS events predicted to generate alternative protein isoforms (Figure 1C) was built using Enrichment Map (Merico et al., 2010) in Cytoscape 2.”
MethodMultilevel proteomics reveals host perturbations by SARS-CoV-2 and SARS-CoV
Stukalov, A., Girault, V., Grass, V., Karayel, Ö., Bergant, V., Urban, C., Haas, D. A., Huang, Y., Oubraham, L., Wang, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03493-4 · Cited by 656
Kiryluk, K., Li, Y., Scolari, F., Sanna‐Cherchi, S., Choi, M., Verbitsky, M., Fasel, D. A., Lata, S., Prakash, S., Shapiro, S. L., et al. (2014) · Nature Genetics · doi:10.1038/ng.3118 · Cited by 640
Topological organization of multichromosomal regions by the long intergenic noncoding RNA Firre
Hacisuleyman, E., Goff, L. A., Trapnell, C., Williams, A., Henao‐Mejia, J., Sun, L., McClanahan, P. D., Hendrickson, D. G., Sauvageau, M., Kelley, D. R., et al. (2014) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2764 · Cited by 644
A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens
Gasperini, M., Hill, A. J., McFaline-Figueroa, J. L., Martin, B., Kim, S., Zhang, M. D., Jackson, D., Leith, A., Schreiber, J., Noble, W. S., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.029 · Cited by 613
Accessory subunits are integral for assembly and function of human mitochondrial complex I
Stroud, D. A., Surgenor, E. E., Formosa, L. E., Reljić, B., Frazier, A. E., Dibley, M. G., Osellame, L. D., Stait, T. L., Beilharz, T. H., Thorburn, D. R., et al. (2016) · Nature · doi:10.1038/nature19754 · Cited by 613
Inhibition of Amino Acid Metabolism Selectively Targets Human Leukemia Stem Cells
Jones, C. L., Stevens, B. M., D'Alessandro, A., Reisz, J. A., Culp-Hill, R., Nemkov, T., Pei, S., Khan, N., Adane, B., Ye, H., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.10.005 · Cited by 585
Krenkel, O., Puengel, T., Govaere, O., Abdallah, A. T., Mossanen, J. C., Kohlhepp, M., Liepelt, A., Lefebvre, E., Luedde, T., Hellerbrand, C., et al. (2018) · Hepatology · doi:10.1002/hep.29544 · Cited by 557
Whole-genome sequences of Malawi cichlids reveal multiple radiations interconnected by gene flow
Malinsky, M., Svardal, H., Tyers, A. M., Miska, E. A., Genner, M. J., Turner, G. F., & Durbin, R. M. (2018) · Nature Ecology & Evolution · doi:10.1038/s41559-018-0717-x · Cited by 568
Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma
Witt, H., Mack, S. C., Ryzhova, M. V., Bender, S., Sill, M., Isserlin, R., Benner, A., Hielscher, T., Milde, T., Remke, M., et al. (2011) · Cancer Cell · doi:10.1016/j.ccr.2011.07.007 · Cited by 554
Self-citationMembrane Cholesterol Efflux Drives Tumor-Associated Macrophage Reprogramming and Tumor Progression
Goossens, P., Rodriguez-Vita, J., Etzerodt, A., Masse, M., Rastoin, O., Gouirand, V., Ulas, T., Papantonopoulou, O., Van Eck, M., Auphan-Anezin, N., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2019.02.016 · Cited by 502
Sone, J., Mitsuhashi, S., Fujita, A., Mizuguchi, T., Hamanaka, K., Mori, K., Koike, H., Hashiguchi, A., Takashima, H., Sugiyama, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0459-y · Cited by 498
Tkach, J. M., Yimit, A., Lee, A. Y., Riffle, M. E., Costanzo, M., Jaschob, D., Hendry, J. A., Ou, J., Moffat, J., Boone, C. M., et al. (2012) · Nature Cell Biology · doi:10.1038/ncb2549 · Cited by 496
Spatially confined sub-tumor microenvironments in pancreatic cancer
Grünwald, B. T., Devisme, A., Andrieux, G., Vyas, F., Aliar, K., McCloskey, C. W., Macklin, A., Jang, G. H., Denroche, R., Romero, J. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.09.022 · Cited by 433
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Moiseeva, V., Cisneros, A., Sica, V., Deryagin, O., Lai, Y., Jung, S., Andrés, E., An, J., Segalés, J., Ortet, L., et al. (2023) · Nature · doi:10.1038/s41586-022-05535-x · Cited by 423
Dixit, D., Prager, B. C., Gimple, R. C., Poh, H. X., Wang, Y., Wu, Q., Qiu, Z., Kidwell, R. L., Kim, L. J. Y., Xie, Q., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0331 · Cited by 418
Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer
ABCTB Investigators, Milne, R. L., EMBRACE, Collaborators, G. S., HEBON, kConFab/AOCS Investigators, Collaborators, N., Kuchenbaecker, K. B., Michailidou, K., Beesley, J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3785 · Cited by 415
Multidimensional proteomics for cell biology
Larance, M., & Lamond, A. I. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3970 · Cited by 418
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Malinsky, M., CHALLIS, R. J., Tyers, A. M., Schiffels, S., Terai, Y., Ngatunga, B. P., Miska, E. A., Durbin, R. M., Genner, M. J., & Turner, G. F. (2015) · Science · doi:10.1126/science.aac9927 · Cited by 420
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Öst, A., Lempradl, A., Casas, E., Weigert, M., Tiko, T., Deniz, M., Pantano, L., Boenisch, U., Itskov, P. M., Stoeckius, M., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.005 · Cited by 406
da Silveira, W. A., Fazelinia, H., Rosenthal, S. B., Laiakis, E. C., Kim, M. S., Meydan, C., Kidane, Y., Rathi, K. S., Smith, S. M., Stear, B., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.002 · Cited by 402
“Pathway analysis was performed using GSEA and gene sets were visualized using the EnrichmentMap plugin (Merico et al., 2010) for Cytoscape (Shannon et al.”
Wang, P., Dong, Q., Zhang, C., Kuan, P.-F., Liu, Y., Jeck, W. R., Andersen, J. B., Jiang, W., Savich, G. L., Tan, T.-X., et al. (2013) · Oncogene · doi:10.1038/onc.2012.315 · Cited by 399
GeneMANIA Prediction Server 2013 Update
Zuberi, K., Franz, M., Rodriguez, H., Montojo, J., Lopes, C. T., Bader, G. D., & Morris, Q. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt533 · Cited by 396
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PCAWG Transcriptome Core Group, Calabrese, C., Davidson, N. R., Demircioğlu, D., Fonseca, N. A., He, Y., Kahles, A., Lehmann, K.-V., Liu, F., Shiraishi, Y., et al. (2020) · Nature · doi:10.1038/s41586-020-1970-0 · Cited by 411
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Gupta, P., Godec, J., Wolski, D., Adland, E., Yates, K. B., Pauken, K. E., Cosgrove, C., Ledderose, C., JUNGER, W. G., Robson, S. C., et al. (2015) · PLoS Pathogens · doi:10.1371/journal.ppat.1005177 · Cited by 384
Development of a Chimeric Model to Study and Manipulate Human Microglia In Vivo
Hasselmann, J., Coburn, M. A., England, W., Figueroa Velez, D. X., Kiani Shabestari, S., Tu, C. H., McQuade, A., Kolahdouzan, M., Echeverria, K., Claes, C., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.002 · Cited by 380
TREM2 Acts Downstream of CD33 in Modulating Microglial Pathology in Alzheimer's Disease
Griciuc, A., Patel, S., Federico, A. N., Choi, S. H., Innes, B. J., Oram, M. K., Cereghetti, G., McGinty, D., Anselmo, A., Sadreyev, R. I., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.010 · Cited by 378
Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity
Meyer, M., Reimand, J., Lan, X., Head, R., Zhu, X., Kushida, M. M., Bayani, J., Pressey, J. C., Lionel, A. C., Clarke, I. D., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320611111 · Cited by 394
Self-citationA proteogenomic portrait of lung squamous cell carcinoma
Satpathy, S., Krug, K., Beltran, P. M. J., Savage, S. R., Petralia, F., Kumar‐Sinha, C., Dou, Y., Reva, B. A., Kane, M. H., Avanessian, S. C., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.016 · Cited by 384
B cells sustain inflammation and predict response to immune checkpoint blockade in human melanoma
Griss, J., Bauer, W. M., Wagner, C., Simon, M., Chen, M., Grabmeier‐Pfistershammer, K., Maurer‐Granofszky, M., Roka, F., Penz, T., Bock, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12160-2 · Cited by 388
Galbo, P. M., Zang, X., & Zheng, D. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-4226 · Cited by 385
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Zimmer, S., Grebe, A., Bakke, S. S., Bode, N., Halvorsen, B. L., Ulas, T., Skjelland, M., Nardo, D. D., Labzin, L. I., Kerksiek, A., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad6100 · Cited by 366
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Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357
AutoAnnotate: A Cytoscape app for summarizing networks with semantic annotations
Kucera, M., Isserlin, R., Arkhangorodsky, A., & Bader, G. D. (2016) · F1000Research · doi:10.12688/f1000research.9090.1 · Cited by 351
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Bourdenx, M., Martín-Segura, A., Scrivo, A., Rodriguez-Navarro, J. A., Kaushik, S., Tasset, I., Diaz, A., Storm, N. J., Xin, Q., Juste, Y. R., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.048 · Cited by 337
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Creixell, P., Linding, R., Reimand, J., Bader, G. D., Haider, S., Wu, G., Ouellette, B. F. F., Boutros, P. C., Stein, L. D., Shibata, T., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3440 · Cited by 336
Self-citationMossanen, J. C., Krenkel, O., Ergen, C., Govaere, O., Liepelt, A., Puengel, T., Heymann, F., Kalthoff, S., Lefebvre, E., Eulberg, D., et al. (2016) · Hepatology · doi:10.1002/hep.28682 · Cited by 325
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Xie, Q., Wu, T., Gimple, R. C., Li, Z., Prager, B. C., Wu, Q., Yu, Y., Wang, P., Wang, Y., Gorkin, D. U., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.006 · Cited by 327
Compensatory metabolic networks in pancreatic cancers upon perturbation of glutamine metabolism
Biancur, D. E., Paulo, J. A., Małachowska, B., Quiles Del Rey, M., Sousa, C. M., Wang, X., Sohn, A. S. W., Chu, G. C., Gygi, S. P., Harper, J. W., et al. (2017) · Nature Communications · doi:10.1038/ncomms15965 · Cited by 315
Mitochondrial unfolded protein response controls matrix pre-RNA processing and translation
Münch, C., & Harper, J. W. (2016) · Nature · doi:10.1038/nature18302 · Cited by 320
Hurst, C. D., Alder, O., Platt, F. M., Droop, A., Stead, L. F., Burns, J. E., Burghel, G. J., Jain, S., Klimczak, L. J., Lindsay, H. A., et al. (2017) · Cancer Cell · doi:10.1016/j.ccell.2017.08.005 · Cited by 307
Pathway Commons 2019 Update: integration, analysis and exploration of pathway data
Rodchenkov, I. V., Babur, Ö., Luna, A., Aksoy, B. A., Wong, J. V., Fong, D., Franz, M., Siper, M. C., Cheung, M., Wrana, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz946 · Cited by 318
Self-citationBhasin, M. K., Dusek, J. A., Chang, B.-H., Joseph, M. G., Denninger, J. W., Fricchione, G. L., Benson, H., & Libermann, T. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062817 · Cited by 304
Gu, Z., & Hübschmann, D. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.04.008 · Cited by 306
DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer
Bashashati, A., Haffari, G., Ding, J., Ha, G., Lui, K., Rosner, J., Huntsman, D. D., Caldas, C., Aparicio, S. A. J. R., & Shah, S. P. (2012) · Genome biology · doi:10.1186/gb-2012-13-12-r124 · Cited by 301
Tang, M., Xie, Q., Gimple, R. C., Zhong, Z., Tam, T., Tian, J., Kidwell, R. L., Wu, Q., Prager, B. C., Qiu, Z., et al. (2020) · Cell Research · doi:10.1038/s41422-020-0338-1 · Cited by 305
p21 produces a bioactive secretome that places stressed cells under immunosurveillance
Sturmlechner, I., Zhang, C., Sine, C. C., van Deursen, E.-J., Jeganathan, K. B., Hamada, N., Grasic, J., Friedman, D., Stutchman, J. T., Can, I., et al. (2021) · Science · doi:10.1126/science.abb3420 · Cited by 295
“Overrepresentation analysis for transcription regulatory targets of individual TFs was performed using the Fisher’s exact test method for selected gene lists against the mouse gene sets from ENCODE and MSigDB collections (57, 59).”
MethodTranscriptional Convergence of Oligodendrocyte Lineage Progenitors during Development
Marques, S., van Bruggen, D., Vanichkina, D. P., Floriddia, E. M., Munguba, H., Väremo, L., Giacomello, S., Falcão, A. M., Meijer, M., Björklund, Å. K., et al. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.07.005 · Cited by 290
Choi, Y.-M., Kim, H., Shim, W., Anwar, M. A., Kwon, J.-W., Kwon, H., Kim, H. J., Jeong, H., Kim, H. M., Hwang, D., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135083 · Cited by 283
Pisu, D., Huang, L., Grenier, J. K., & Russell, D. G. (2020) · Cell Reports · doi:10.1016/j.celrep.2019.12.033 · Cited by 289
Kleppe, M., Koche, R., Zou, L., van Galen, P., Hill, C. E., Dong, L., De Groote, S., Papalexi, E., Hanasoge Somasundara, A. V., Cordner, K., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2017.11.009 · Cited by 279
RNA demethylation increases the yield and biomass of rice and potato plants in field trials
Yu, Q., Liu, S., Yu, L., Xiao, Y., Zhang, S., Wang, X., Xu, Y., Yu, H. J., Li, Y., Yang, J., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00982-9 · Cited by 277
Ha, G., Roth, A. J. L., Lai, D., Bashashati, A., Ding, J., Goya, R., Giuliany, R., Rosner, J., Oloumi, A., Shumansky, K., et al. (2012) · Genome Research · doi:10.1101/gr.137570.112 · Cited by 269
Ibebunjo, C., Chick, J. M., Kendall, T., Eash, J. K., Li, C., Zhang, Y., Vickers, C., Wu, Z., Clarke, B. A., Shi, J., et al. (2013) · Molecular and Cellular Biology · doi:10.1128/mcb.01036-12 · Cited by 267
The single-cell epigenomic and transcriptional landscape of immunity to influenza vaccination
Wimmers, F., Donato, M., Kuo, A. J., Ashuach, T., Gupta, S., Li, C., Dvorak, M., Foecke, M. H., Chang, S. E., Hagan, T., et al. (2021) · Cell · doi:10.1016/j.cell.2021.05.039 · Cited by 275
A Guide to Conquer the Biological Network Era Using Graph Theory
Koutrouli, M., Karatzas, E., Páez-Espino, D., & Pavlopoulos, G. A. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00034 · Cited by 279
Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer’s disease
Otero-Garcia, M., Mahajani, S. U., Wakhloo, D., Tang, W., Xue, Y.-Q., Morabito, S., Pan, J., Oberhauser, J., Madira, A. E., Shakouri, T., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.021 · Cited by 262
Proteome-wide analysis of chaperone-mediated autophagy targeting motifs
Kirchner, P., Bourdenx, M., Madrigal-Matute, J., Tiano, S., Diaz, A., Bartholdy, B. A., Will, B., & Cuervo, A. M. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000301 · Cited by 257
Lim, R. G., Quan, C., Reyes-Ortiz, A. M., Lutz, S. E., Kedaigle, A. J., Gipson, T. A., Wu, J., Vatine, G. D., Stocksdale, J., Casale, M. S., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.04.021 · Cited by 253
Pathway analysis of genomic data: concepts, methods, and prospects for future development
Ramanan, V. K., Shen, L., Moore, J. H., & Saykin, A. J. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.03.004 · Cited by 268
Topoisomerase II beta interacts with cohesin and CTCF at topological domain borders
Uusküla-Reimand, L., Hou, H., Samavarchi‐Tehrani, P., Rudan, M. V., Liang, M., Medina-Rivera, A., Mohammed, H., Schmidt, D., Schwalie, P. C., Young, E. J., et al. (2016) · Genome biology · doi:10.1186/s13059-016-1043-8 · Cited by 261
RCy3: Network biology using Cytoscape from within R
Gustavsen, J., Pai, S., Isserlin, R., Demchak, B., & Pico, A. R. (2019) · F1000Research · doi:10.12688/f1000research.20887.3 · Cited by 251
Self-citationAssociation analyses identify 31 new risk loci for colorectal cancer susceptibility
Law, P., Timofeeva, M. N., Fernández–Rozadilla, C., Broderick, P., Studd, J. B., Fernandez-Tajes, J. J., Farrington, S. M., Svinti, V., Palles, C., Orlando, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09775-w · Cited by 249
Mapping the Cellular Response to Small Molecules Using Chemogenomic Fitness Signatures
Lee, A. Y., St.Onge, R. P., Proctor, M. J., Wallace, I. M., Nile, A. H., Spagnuolo, P. A., Jitkova, Y., Gronda, M., Wu, Y., Kim, M. K., et al. (2014) · Science · doi:10.1126/science.1250217 · Cited by 241
Self-citationThe Proteogenomic Landscape of Curable Prostate Cancer
Sinha, A., Huang, V., Livingstone, J. M., Wang, J., Fox, N. S., Kurganovs, N. J., Ignatchenko, V., Fritsch, K., Donmez, N., Heisler, L. E., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.02.005 · Cited by 245
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Lanver, D., Müller, A. N., Happel, P., Schweizer, G., Haas, F. B., Franitza, M., Pellegrin, C., Reißmann, S., Altmüller, J., Rensing, S. A., et al. (2018) · The Plant Cell · doi:10.1105/tpc.17.00764 · Cited by 249
Zhou, M., Wang, X., Shi, H., Cheng, L., Wang, Z., Zhao, H., Yang, L., & Sun, J. (2016) · Oncotarget · doi:10.18632/oncotarget.7181 · Cited by 233
Gardiner, E. J., Beveridge, N. J., Wu, J. Q., Carr, V. J., Scott, R. J., Tooney, P. A., & Cairns, M. J. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.78 · Cited by 235
Therapeutic strategies for diffuse midline glioma from high-throughput combination drug screening
Lin, G. L., Wilson, K. M., Ceribelli, M., Stanton, B. Z., Woo, P. J., Kreimer, S., Qin, E. Y., Zhang, X., Lennon, J., Nagaraja, S., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw0064 · Cited by 230
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Mayneris‐Perxachs, J., Castells‐Nobau, A., Arnoriaga‐Rodríguez, M., Martín, M., de la Vega-Correa, L., Zapata, C., Burokas, A., Blasco, G., Coll, C., Escrichs, A., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.04.001 · Cited by 231
The blood-brain barrier is dysregulated in COVID-19 and serves as a CNS entry route for SARS-CoV-2
Krasemann, S., Haferkamp, U., Pfefferle, S., Woo, M. S., Heinrich, F., Schweizer, M., Appelt‐Menzel, A., Cubukova, A., Barenberg, J., Leu, J., et al. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.12.011 · Cited by 232
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Yin, L., Thambisetty, M., Troncoso, J. C., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0282-4 · Cited by 225
Integrative pathway enrichment analysis of multivariate omics data
Paczkowska, M., Barenboim, J., Sintupisut, N., Fox, N. S., Zhu, H., Abd-Rabbo, D., Mee, M. W., Boutros, P. C., PCAWG Drivers and Functional Interpretation Working Group, Abascal, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13983-9 · Cited by 236
Self-citation
Metadata
- Authors
- Merico, D., Isserlin, R., Stueker, O., Emili, A., & Bader, G. D.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Merico, Daniele
- Bader, Gary D.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-11-15
- Volume
- 5
- Issue
- 11
- Pages
- e13984
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC2981572
- OpenAlex
- W2097948046
- Cited by
- 2,235
- References
- 58
- Citations by year
- 2026: 89 · 2025: 145 · 2024: 176 · 2023: 216 · 2022: 238 · 2021: 233 · 2020: 237 · 2019: 164 · 2018: 158 · 2017: 145 · 2016: 127 · 2015: 101 · 2014: 79 · 2013: 69 · 2012: 43
- FWCI
- 16.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Heart and Stroke Foundation of Canada
- Genome Canada
- Ontario Genomics
- Ontario Genomics Institute
- Hospital for Sick Children
- National Institutes of Health
- National Human Genome Research Institute (P41 P41HG04118, P41HG04118, P41HG004118)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- gene set enrichment analysis, network visualization, gene set overlap, network clustering, pathway over-representation, gene expression data interpretation, Cytoscape plugin
- MeSH terms
- Algorithms, Breast Neoplasms, Colonic Neoplasms, Estrogens, Female, Humans, Software, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Cluster Analysis, Computational Biology, Internet, Gene Expression Profiling, Gene Regulatory Networks
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