Cited by 13,288Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 7 of the 8 references it cites were found in a registry or an index.

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

Open access

Edgar, R.·2002·Nucleic Acids Research

DOI 10.1093/nar/30.1.207PMC PMC99122

Open this paper in CoChat — free account Open PDF

Abstract

The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Integrity signals

Registry

Crossref · 10.1093/nar/30.1.207

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray
Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995) · Science · doi:10.1126/science.270.5235.467 · Cited by 9,609
Serial Analysis of Gene Expression
Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995) · Science · doi:10.1126/science.270.5235.484 · Cited by 3,713
Gene expression informatics —it's all in your mine
Bassett, D. E., Eisen, M. B., & Boguski, M. S. (1999) · Nature Genetics · doi:10.1038/4478 · Cited by 400
Using oligonucleotide probe arrays to access genetic diversity
Lipshutz, R. J., Morris, D., Chee, M. S., Hubbell, E. A., Kozal, M. J., Shah, N. M., Shen, N., Yang, R., & Fodor, S. P. A. (1999) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-8817-2_9 · Cited by 339
One-stop shop for microarray data
Brāzma, A., Robinson, A. J., Cameron, G. N., & Ashburner, M. (2000) · Nature · doi:10.1038/35001676 · Cited by 157
Microarray gene expression database: progress towards an international repository of gene expression data.
Kellam, P. (2001) · Genome biology · doi:10.1186/gb-2001-2-5-reports4011 · Cited by 20
Unlimited Access—Limitless SuccessPreprint
Goodman, L. (2001) · Genome Research · doi:10.1101/gr.11.5.637 · Cited by 7

Flagged first, then by citations

Show all 8 references

1 of the 8 references couldn’t be matched to a known paper.

Sorted by citations

Items 201–300 of 1,115

Ontology-Based Meta-Analysis of Global Collections of High-Throughput Public Data
Kupershmidt, I., Su, Q. J., Grewal, A., Sundaresh, S., Halperin, I., Flynn, J. F., Shekar, M., Wang, H. H., Park, J. R., Cui, W., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013066 · Cited by 365
From cohorts to molecules: adverse impacts of endocrine disrupting mixtures
Caporale, N., Leemans, M., Birgersson, L., Germain, P.-L., Cheroni, C., Borbély, G., Engdahl, E., Lindh, C., Bressan, R. B., Cavallo, F., et al. (2022) · Science · doi:10.1126/science.abe8244 · Cited by 368
Brown and white adipose tissues: intrinsic differences in gene expression and response to cold exposure in mice
Rosell, M., Kaforou, M., Frontini, A., Okolo, A., Chan, Y., Nikolopoulou, E., Millership, S. J., Fenech, M. E., MacIntyre, D. A., Turner, J. O., et al. (2014) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00473.2013 · Cited by 376
A Genome-Wide Characterization of MicroRNA Genes in Maize
Zhang, L., Chia, J.-M., Kumari, S., Stein, J. C., Liu, Z., Narechania, A., Maher, C. A., Guill, K. E., McMullen, M. D., & Ware, D. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000716 · Cited by 365
Oligodendrocyte Progenitor Cells Become Regionally Diverse and Heterogeneous with Age
Spitzer, S. O., Sitnikov, S., Kamen, Y., Evans, K. A., Kronenberg-Versteeg, D., Dietmann, S., de Faria, O., Agathou, S., & Káradóttir, R. T. (2019) · Neuron · doi:10.1016/j.neuron.2018.12.020 · Cited by 364
Mitochondrial Reprogramming Underlies Resistance to BCL-2 Inhibition in Lymphoid Malignancies
Guièze, R., Liu, V. M., Rosebrock, D., Jourdain, A. A., Hernández-Sánchez, M., Martinez Zurita, A., Sun, J., Ten Hacken, E., Baranowski, K., Thompson, P. A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.08.005 · Cited by 361
Toward the blood-borne miRNome of human diseases
Keller, A., Leidinger, P., Bauer, A., ElSharawy, A., Haas, J., Backes, C., Wendschlag, A., Giese, N., Tjaden, C., Ott, K., et al. (2011) · Nature Methods · doi:10.1038/nmeth.1682 · Cited by 355
Nutrigenomics and nutrigenetics: the emerging faces of nutrition
Mutch, D. M., Wahli, W., & Williamson, G. (2005) · The FASEB Journal · doi:10.1096/fj.05-3911rev · Cited by 359
Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata
Crispin, M. K., Ko, J., Craiglow, B. G., Li, S., Shankar, G., Urban, J., Chen, J. C., Cerise, J. E., Jabbari, A., Winge, M. C. G., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.89776 · Cited by 359
IGF-I induced genes in stromal fibroblasts predict the clinical outcome of breast and lung cancer patients
Rajski, M., Zanetti-Dällenbach, R., Vogel, B., Herrmann, R., Rochlitz, C., & Buess, M. (2010) · BMC Medicine · doi:10.1186/1741-7015-8-1 · Cited by 355
miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets
Lu, T., Lee, C., Tsai, M., Chiu, Y., Hsiao, C. K., Lai, L., & Chuang, E. Y.-Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0042390 · Cited by 362
IDEG6: a web tool for detection of differentially expressed genes in multiple tag sampling experiments
Romualdi, C., Bortoluzzi, S., d’Alessi, F., & Danieli, G. A. (2003) · Physiological Genomics · doi:10.1152/physiolgenomics.00096.2002 · Cited by 352
Transcriptional regulation of nitrogen-associated metabolism and growth
Gaudinier, A., Rodríguez-Medina, J., Zhang, L., Olson, A. J., Liseron-Monfils, C., Bågman, A.-M., Foret, J., Abbitt, S. E., Tang, M., Li, B., et al. (2018) · Nature · doi:10.1038/s41586-018-0656-3 · Cited by 356
Lipopolysaccharide Activates Toll-like Receptor 4 (TLR4)-mediated NF-κB Signaling Pathway and Proinflammatory Response in Human Pericytes
Guijarro-Muñoz, I., Compte, M., Álvarez-Cienfuegos, A., Álvarez-Vallina, L., & Sanz, L. (2014) · Journal of Biological Chemistry · doi:10.1074/jbc.m113.521161 · Cited by 342
Self-Organizing 3D Human Trunk Neuromuscular Organoids
Martins, J.-M. F., Fischer, C., Urzi, A., Vidal, R. O., Kunz, S., Ruffault, P.-L., Kabuss, L., Hube, I., Gazzerro, E., Birchmeier, C., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2019.12.007 · Cited by 339
Penetration of the Stigma and Style Elicits a Novel Transcriptome in Pollen Tubes, Pointing to Genes Critical for Growth in a Pistil
Qin, Y., Leydon, A. R., Manziello, A., Pandey, R., Mount, D. W., Denic, S. Z., Vasic, B. V., Johnson, M. A., & Palanivelu, R. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000621 · Cited by 336
Gene Expression Value Prediction Based on XGBoost Algorithm
Li, W., Yin, Y., Quan, X., & Zhang, H. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01077 · Cited by 353
Genome-wide RNA-mediated interference screen identifies miR-19 targets in Notch-induced T-cell acute lymphoblastic leukaemia
Mavrakis, K. J., Wolfe, A. L., Oricchio, E., Palomero, T., Keersmaecker, K. D., McJunkin, K., Zuber, J. A., James, T., Khan, A. A., Leslie, C. S., et al. (2010) · Nature Cell Biology · doi:10.1038/ncb2037 · Cited by 335
Making sense of cancer genomic dataPreprint
Chin, L., Hahn, W. C., Getz, G. A., & Meyerson, M. M. (2011) · Genes & Development · doi:10.1101/gad.2017311 · Cited by 346
Conservation and innovation in the DUX4-family gene network
Whiddon, J. L., Langford, A. T., Wong, C.-J., Zhong, J. W., & Tapscott, S. J. (2017) · Nature Genetics · doi:10.1038/ng.3846 · Cited by 333
Batch effect removal methods for microarray gene expression data integration: a survey
Lazar, C., Meganck, S., Taminau, J., Steenhoff, D., Coletta, A., Molter, C., Weiss-Solis, D. Y., Duque, R., Bersini, H., & Nowe, A. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs037 · Cited by 333
Human pluripotent stem cell-derived neural constructs for predicting neural toxicity
Schwartz, M. P., Hou, Z., Propson, N. E., Zhang, J., Engstrom, C. J., Costa, V. S., Jiang, P., Nguyen, B. K., Bolin, J. M., Daly, W., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1516645112 · Cited by 325
Uterine Selection of Human Embryos at Implantation
Brosens, J. J., Salker, M. S., Teklenburg, G., Nautiyal, J., Salter, S., Lucas, E. S., Steel, J. H., Christian, M., Chan, Y., Boomsma, C. M., et al. (2014) · Scientific Reports · doi:10.1038/srep03894 · Cited by 326
Seminal Fluid Induces Leukocyte Recruitment and Cytokine and Chemokine mRNA Expression in the Human Cervix after Coitus
Sharkey, D. J., Tremellen, K. P., Jasper, M. J., Gemzell‐Danielsson, K., & Robertson, S. A. (2012) · The Journal of Immunology · doi:10.4049/jimmunol.1102736 · Cited by 329
Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323
Placental Pathology in Early-Onset and Late-Onset Fetal Growth Restriction
Mifsud, W., & Sebire, N. J. (2014) · Fetal Diagnosis and Therapy · doi:10.1159/000359969 · Cited by 328
In silico methods for drug repurposing and pharmacology
Hodos, R. A., Kidd, B. A., Shameer, K., Readhead, B., & Dudley, J. T. (2016) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1337 · Cited by 341
MDS and secondary AML display unique patterns and abundance of aberrant DNA methylation
Figueroa, M. E., Skrabanek, L. A., Li, Y., Jiemjit, A., Fandy, T. E., Paietta, E. M., Fernandez, H. F., Tallman, M. S., Greally, J. M., Carraway, H. E., et al. (2009) · Blood · doi:10.1182/blood-2009-01-200519 · Cited by 325
High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer
Chin, S., Teschendorff, A. E., Marioni, J. C., Wang, Y., Barbosa‐Morais, N. L., Thorne, N., Costa, J. L., Pinder, S. E., van de Wiel, M. A., Green, A. R., et al. (2007) · Genome biology · doi:10.1186/gb-2007-8-10-r215 · Cited by 323
Obg and Membrane Depolarization Are Part of a Microbial Bet-Hedging Strategy that Leads to Antibiotic Tolerance
Verstraeten, N., Knapen, W., Kint, C. I., Liebens, V., den Bergh, B. V., Dewachter, L., Michiels, J. E., Fu, Q., David, C. C., Fierro, A. C., et al. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.05.011 · Cited by 332
Increased local expression of coagulation factor X contributes to the fibrotic response in human and murine lung injury
Scotton, C. J., Krupiczojc, M. A., Königshoff, M., Mercer, P. F., Lee, G., Kaminski, N., Morser, J., Post, J. M., Maher, T. M., Nicholson, A. G., et al. (2009) · Journal of Clinical Investigation · doi:10.1172/jci33288 · Cited by 318
CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses
Russo, P. S. T., Ferreira, G. R., Cardozo, L. E., Bürger, M. C., Arias‐Carrasco, R., Maruyama, S. R., Hirata, T. D. C., de Lima, D. S., Passos, F. M., Fukutani, K. F., et al. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2053-1 · Cited by 313
Genome-wide association meta-analysis identifies new endometriosis risk loci
Nyholt, D. R., Low, S.-K., Anderson, C. A., Painter, J. N., Uno, S., Morris, A. P., MacGregor, S., Gordon, S. D., Henders, A. K., Martin, N. G., et al. (2012) · Nature Genetics · doi:10.1038/ng.2445 · Cited by 315
Increased Antibiotic Resistance ofEscherichia coliin Mature Biofilms
Ito, A., Taniuchi, A., May, T., Kawata, K., & Okabe, S. (2009) · Applied and Environmental Microbiology · doi:10.1128/aem.02949-08 · Cited by 312
Sensory lesioning induces microglial synapse elimination via ADAM10 and fractalkine signaling
Gunner, G., Cheadle, L., Johnson, K. M., Ayata, P., Badimon, A., Mondo, E., Nagy, M. A., Liu, L., Bemiller, S. M., Kim, K.-W., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0419-y · Cited by 318
The Plant Genome Integrative Explorer Resource: PlantGen IE .org
Sundell, D., Mannapperuma, C., Netotea, S., Delhomme, N., Lin, Y., Sjödin, A., de Peer, Y. V., Jansson, S., Hvidsten, T. R., & Street, N. R. (2015) · New Phytologist · doi:10.1111/nph.13557 · Cited by 316
Global analysis of X-chromosome dosage compensation
Gupta, V., Parisi, M. J., Sturgill, D. M., Nuttall, R., Doctolero, M. H., Dudko, O. K., Malley, J. D., Eastman, P. S., & Oliver, B. (2006) · Journal of Biology · doi:10.1186/jbiol30 · Cited by 315
BioJupies: Automated Generation of Interactive Notebooks for RNA-Seq Data Analysis in the Cloud
Torre, D., Lachmann, A., & Ma’ayan, A. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.007 · Cited by 301
Similarity of mouse perivascular and brown adipose tissues and their resistance to diet-induced inflammation
Fitzgibbons, T. P., Kogan, S., Aouadi, M., Hendricks, G. M., Straubhaar, J., & Czech, M. (2011) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00376.2011 · Cited by 313
Molecular subtypes of breast cancer are associated with characteristic DNA methylation patterns
Holm, K., Hegardt, C., Staaf, J., Vallon‐Christersson, J., Jonsson, G. G., Olsson, H. L., Borg, Å., & Ringnér, M. (2010) · Breast Cancer Research · doi:10.1186/bcr2590 · Cited by 312
Pathways to the analysis of microarray data
Curtis, R. K., Orešič, M., & Vidal‐Puig, A. (2005) · Trends in biotechnology · doi:10.1016/j.tibtech.2005.05.011 · Cited by 309
Induction of resident memory T cells enhances the efficacy of cancer vaccine
Nizard, M., Roussel, H., Diniz, M. O., Karaki, S., Tran, T., Voron, T., Dransart, E., Sandoval, F., Riquet, M., Rance, B., et al. (2017) · Nature Communications · doi:10.1038/ncomms15221 · Cited by 321
Adipocyte-derived exosomal miRNAs: a novel mechanism for obesity-related disease
Ferrante, S. C., Nadler, E. P., Pillai, D. K., Hubal, M. J., Wang, Z., Wang, J. M., Gordish‐Dressman, H., Koeck, E. S., Sevilla, S., Wiles, A. A., et al. (2014) · Pediatric Research · doi:10.1038/pr.2014.202 · Cited by 317
The functional potential of high Arctic permafrost revealed by metagenomic sequencing, qPCR and microarray analyses
Yergeau, É., Hogues, H., Whyte, L. G., & Greer, C. W. (2010) · The ISME Journal · doi:10.1038/ismej.2010.41 · Cited by 309
Learning from Co-expression Networks: Possibilities and Challenges
Serin, E. A. R., Nijveen, H., Hilhorst, H. W. M., & Ligterink, W. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.00444 · Cited by 311
Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes
Solomon, K. V., Haitjema, C. H., Henske, J. K., Gilmore, S. P., Borges-Rivera, D., Lipzen, A., Brewer, H. M., Purvine, S. O., Wright, A. T., Theodorou, M. K., et al. (2016) · Science · doi:10.1126/science.aad1431 · Cited by 302
Rapid evolution of male-biased gene expression in Drosophila
Meiklejohn, C. D., Parsch, J., Ranz, J. M., & Hartl, D. L. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1630690100 · Cited by 302
Temporal gene‐expression in Escherichia coli K‐12 biofilms
Domka, J., Lee, J., Bansal, T., & Wood, T. K. (2006) · Environmental Microbiology · doi:10.1111/j.1462-2920.2006.01143.x · Cited by 302
A survey of ovary-, testis-, and soma-biased gene expression in Drosophila melanogasteradults
Parisi, M. J., Nuttall, R., Edwards, P., Minor, J. M., Naiman, D. Q., Lü, J., Doctolero, M. H., Vainer, M., Cathy, C., Malley, J. D., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-6-r40 · Cited by 301
exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids
Murillo, O. D., Thistlethwaite, W., Rozowsky, J. S., Subramanian, S. L., Lucero, R., Shah, N., Jackson, A. R., Srinivasan, S., Chung, A. Y., Laurent, C. D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.018 · Cited by 308
PlaNet: Combined Sequence and Expression Comparisons across Plant Networks Derived from Seven Species
Mutwil, M., Klie, S., Tohge, T., Giorgi, F. M., Wilkins, O., Campbell, M. M., Fernie, A. R., Usadel, B., Nikoloski, Z., & Persson, S. (2011) · The Plant Cell · doi:10.1105/tpc.111.083667 · Cited by 299
Plasmodium falciparum Heterochromatin Protein 1 Marks Genomic Loci Linked to Phenotypic Variation of Exported Virulence Factors
Flueck, C., Bártfai, R., Volz, J. C., Niederwieser, I., Salcedo‐Amaya, A. M., Alako, B., Ehlgen, F., Ralph, S. A., Cowman, A. F., Bozdech, Z., et al. (2009) · PLoS Pathogens · doi:10.1371/journal.ppat.1000569 · Cited by 299
FBXO11 targets BCL6 for degradation and is inactivated in diffuse large B-cell lymphomas
Duan, S., C̆ermák, L., Pagan, J. K., Rossi, M., Martinengo, C., di Celle, P. F., Chapuy, B., Shipp, M. A., Chiarle, R., & Pagano, M. (2011) · Nature · doi:10.1038/nature10688 · Cited by 295
Structural basis of ribosomal frameshifting during translation of the SARS-CoV-2 RNA genome
Bhatt, P. R., Scaiola, A., Loughran, G., Leibundgut, M., Kratzel, A., Meurs, R., Dreos, R., O’Connor, K. M., McMillan, A. E., Bode, J. W., et al. (2021) · Science · doi:10.1126/science.abf3546 · Cited by 294
Vitamin D Receptor Signaling Mechanisms: Integrated Actions of a Well-Defined Transcription Factor
Carlberg, C., & Campbell, M. J. (2013) · Steroids · doi:10.1016/j.steroids.2012.10.019 · Cited by 293
Cellular differentiation hierarchies in normal and culture-adapted human embryonic stem cells
Enver, T., Soneji, S., Joshi, C., Brown, J., Iborra, F. J., Orntoft, T. F., Thykjær, T., Maltby, E. L., Smith, K., Dawud, R. A., et al. (2005) · Human Molecular Genetics · doi:10.1093/hmg/ddi345 · Cited by 292
Biased alternative polyadenylation in human tissues
Zhang, H., Lee, J. Y., & Tian, B. (2005) · Genome biology · doi:10.1186/gb-2005-6-12-r100 · Cited by 296
GDV1 induces sexual commitment of malaria parasites by antagonizing HP1-dependent gene silencing
Filarsky, M., Fraschka, S. A. K., Niederwieser, I., Brancucci, N. M. B., Carrington, E., Carrió, E., Moes, S., Jenoe, P., Bártfai, R., & Voss, T. S. (2018) · Science · doi:10.1126/science.aan6042 · Cited by 291
Deregulated gene expression pathways in myelodysplastic syndrome hematopoietic stem cells
Pellagatti, A., Cazzola, M., Giagounidis, A. A. N., Perry, J., Malcovati, L., Porta, M. G. D., Jädersten, M., Killick, S. B., Verma, A. K., Norbury, C. J., et al. (2010) · Leukemia · doi:10.1038/leu.2010.31 · Cited by 279
The glycan CA19-9 promotes pancreatitis and pancreatic cancer in mice
Engle, D. D., Tiriac, H., Rivera, K. D., Pommier, A., Whalen, S., Oni, T. E., Alagesan, B., Lee, E. J., Yao, M. A., Lucito, M. S., et al. (2019) · Science · doi:10.1126/science.aaw3145 · Cited by 288
Array CGH identifies reciprocal 16p13.1 duplications and deletions that predispose to autism and/or mental retardation
Ullmann, R., TURNER, G. M., Kirchhoff, M., Chen, W., Tonge, B. J., Rosenberg, C., Field, M., Vianna‐Morgante, A. M., Christie, L., Krepischi, A. C. V., et al. (2007) · Human Mutation · doi:10.1002/humu.20546 · Cited by 292
FoxJ1-dependent gene expression is required for differentiation of radial glia into ependymal cells and a subset of astrocytes in the postnatal brain
Jacquet, B. V., Salinas-Mondragon, R., Liang, H.-X., Therit, B., Buie, J. D., Dykstra, M., Campbell, K., Ostrowski, L., Brody, S., & Ghashghaei, H. T. (2009) · Development · doi:10.1242/dev.041129 · Cited by 277
The metabolite BH4 controls T cell proliferation in autoimmunity and cancer
Cronin, S. J. F., Seehus, C., Weidinger, A., Talbot, S., Reissig, S., Seifert, M., Pierson, Y., McNeill, E., Longhi, M. S., Turnes, B. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0701-2 · Cited by 285
Mutant U2AF1 Expression Alters Hematopoiesis and Pre-mRNA Splicing In Vivo
Shirai, C. L., Ley, J. N., White, B. S., Kim, S., Tibbitts, J., Shao, J., Ndonwi, M., Wadugu, B., Duncavage, E. J., Okeyo-Owuor, T., et al. (2015) · Cancer Cell · doi:10.1016/j.ccell.2015.04.008 · Cited by 287
Identification of a Novel Biomarker, SEMA5A , for Non–Small Cell Lung Carcinoma in Nonsmoking Women
Lu, T., Tsai, M., Lee, J., Hsu, C., Chen, P., Lin, C., Shih, J., Yang, P., Hsiao, C. K., Lai, L., et al. (2010) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-10-0332 · Cited by 288
NeuroD1 reprograms chromatin and transcription factor landscapes to induce the neuronal program
Pataskar, A., Jung, J., Smialowski, P., Noack, F., Calegari, F., Straub, T., & Tiwari, V. K. (2015) · The EMBO Journal · doi:10.15252/embj.201591206 · Cited by 285
Silicon enhances suberization and lignification in roots of rice (Oryza sativa)
Fleck, A. T., Nye, T., Repenning, C., Stahl, F., Zahn, M., & Schenk, M. (2010) · Journal of Experimental Botany · doi:10.1093/jxb/erq392 · Cited by 283
Transcriptome analysis of Vibrio parahaemolyticus in type III secretion system 1 inducing conditions
Nydam, S. D., Shah, D. H., & Call, D. R. (2014) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2014.00001 · Cited by 286
Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues
Kilpinen, S. K., Autio, R., Ojala, K., Iljin, K., Bucher, E., Sara, H., Pisto, T., Saarela, M., Skotheim, R. I., Björkman, M. L., et al. (2008) · Genome biology · doi:10.1186/gb-2008-9-9-r139 · Cited by 289
oPOSSUM-3: Advanced Analysis of Regulatory Motif Over-Representation Across Genes or ChIP-Seq Datasets
Kwon, A. T.-J., Arenillas, D. J., Worsley-Hunt, R., & Wasserman, W. W. (2012) · G3 Genes Genomes Genetics · doi:10.1534/g3.112.003202 · Cited by 283
LXRs link metabolism to inflammation through Abca1-dependent regulation of membrane composition and TLR signaling
Ito, A., Hong, C., Rong, X., Zhu, X., Tarling, E. J., Hedde, P. N., Gratton, E., Parks, J., & Tontonoz, P. (2015) · eLife · doi:10.7554/elife.08009 · Cited by 281
“The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus (Edgar et al., 2002) and are accessible through GEO Series accession number GSE70444 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70444) and GSE70463 (http://www.ncbi.…”
Method
Iron-binding haemerythrin RING ubiquitin ligases regulate plant iron responses and accumulation
Kobayashi, T., Nagasaka, S., Senoura, T., Itai, R. N., Nakanishi, H., & Nishizawa, N. K. (2013) · Nature Communications · doi:10.1038/ncomms3792 · Cited by 280
High-Resolution Transcriptome Maps Reveal Strain-Specific Regulatory Features of Multiple Campylobacter jejuni Isolates
Dugar, G., Herbig, A., Förstner, K. U., Heidrich, N., Reinhardt, R., Nieselt, K., & Sharma, C. M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003495 · Cited by 286
Selection of GalNAc-conjugated siRNAs with limited off-target-driven rat hepatotoxicity
Janas, M. M., Schlegel, M. K., Harbison, C. E., Yilmaz, V. O., Jiang, Y., Parmar, R., Zlatev, I., Castoreno, A., Xu, H., Shulga-Morskaya, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-02989-4 · Cited by 279
The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells
McEwen, L. M., O’Donnell, K. J., McGill, M. G., Edgar, R. D., Jones, M. J., MacIsaac, J. L., Lin, D. T. S., Ramadori, K. E., Morin, A. M., Gladish, N., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820843116 · Cited by 280
Self-citation
Genes and pathways underlying regional and cell type changes in Alzheimer’s disease
Miller, J. A., Woltjer, R. L., Goodenbour, J. M., Horvath, S., & Geschwind, D. H. (2013) · Genome Medicine · doi:10.1186/gm452 · Cited by 275
An interactive environment for agile analysis and visualization of ChIP-sequencing data
Lerdrup, M., Johansen, J. V., Singh, S. A., & Hansen, K. H. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3180 · Cited by 277
Identification of a choroid plexus vascular barrier closing during intestinal inflammation
Carloni, S., Bertocchi, A., Mancinelli, S., Bellini, M., Erreni, M., Borreca, A., Braga, D., Giugliano, S., Mozzarelli, A. M., Manganaro, D., et al. (2021) · Science · doi:10.1126/science.abc6108 · Cited by 276
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273
Extracellular Vesicles in Luminal Fluid of the Ovine Uterus
Burns, G. W., Brooks, K. E., Wildung, M. R., Navakanitworakul, R., Christenson, L. K., & Spencer, T. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090913 · Cited by 272
Gene-expression analysis and network discovery using Genevestigator
Philip Zimmermann, B., Hennig, L., & Gruissem, W. (2005) · Trends in Plant Science · doi:10.1016/j.tplants.2005.07.003 · Cited by 271
Osteosarcoma originates from mesenchymal stem cells in consequence of aneuploidization and genomic loss of Cdkn2
Mohseny, A. B., Szuhai, K., Romeo, S. O., Buddingh, E. P., Bruijn, I. H. B., de Jong, D., van Pel, M., Cleton-Jansen, A., & Hogendoorn, P. C. W. (2009) · The Journal of Pathology · doi:10.1002/path.2603 · Cited by 270
Cancer transcriptome profiling at the juncture of clinical translation
Cieslik, M. P., & Chinnaiyan, A. M. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.96 · Cited by 266
Cannabidiol exerts sebostatic and antiinflammatory effects on human sebocytes
Oláh, A., Tóth, B. I., Borbíró, I., Sugawara, K., Szöllõsi, A. G., Czifra, G., Pál, B., Ambrus, L., Kloepper, J., Camera, E., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci64628 · Cited by 268
CD8+ T-cells infiltrate Alzheimer’s disease brains and regulate neuronal- and synapse-related gene expression in APP-PS1 transgenic mice
Unger, M. S., Li, E., Scharnagl, L., Poupardin, R., Altendorfer, B., Mrowetz, H., Hutter-Paier, B., Weiger, T. M., Heneka, M. T., Attems, J., et al. (2020) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2020.05.070 · Cited by 248
Linking the Transcriptional Profiles and the Physiological States of Mycobacterium tuberculosis during an Extended Intracellular Infection
Rohde, K. H., Veiga, D. F. T., Caldwell, S., Balázsi, G., & Russell, D. G. (2012) · PLoS Pathogens · doi:10.1371/journal.ppat.1002769 · Cited by 270
Human CD 8 + EMRA T cells display a senescence‐associated secretory phenotype regulated by p38 MAPK
Callender, L. A., Carroll, E. C., Beal, R. W. J., Chambers, E. S., Nourshargh, S., Akbar, A. N., & Henson, S. M. (2017) · Aging Cell · doi:10.1111/acel.12675 · Cited by 278
Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data
Zhu, Q., Shah, S., Dries, R., Cai, L., & Yuan, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4260 · Cited by 250
Combinatorial Single-Cell Analyses of Granulocyte-Monocyte Progenitor Heterogeneity Reveals an Early Uni-potent Neutrophil Progenitor
Kwok, I., Becht, É., Xia, Y., Ng, M. S. F., Teh, Y. C., Tan, L., Evrard, M., Li, J. L. Y., Tran, H. T., Tan, Y., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.06.005 · Cited by 270
Recurrent CNVs Disrupt Three Candidate Genes in Schizophrenia Patients
Vrijenhoek, T., Buizer‐Voskamp, J. E., van der Stelt, I., Strengman, E., Sabatti, C., van Kessel, A. G., Brunner, H. G., Ophoff, R. A., & Veltman, J. A. (2008) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2008.09.011 · Cited by 275
Actin remodeling confers BRAF inhibitor resistance to melanoma cells through YAP/TAZ activation
Kim, M. H., Kim, J., Hong, H., Lee, J. J.-K., Lee, J., Jung, E., & Kim, J. (2015) · The EMBO Journal · doi:10.15252/embj.201592081 · Cited by 270
Cellulose-Derived Oligomers Act as Damage-Associated Molecular Patterns and Trigger Defense-Like Responses
Souza, C. D. A., Li, S., Lin, A. Z., Boutrot, F., Großmann, G., Zipfel, C., & Somerville, S. C. (2017) · PLANT PHYSIOLOGY · doi:10.1104/pp.16.01680 · Cited by 268
Enhanced virome sequencing using targeted sequence capturePreprint
Wylie, T., Wylie, K. M., Herter, B. N., & Storch, G. A. (2015) · Genome Research · doi:10.1101/gr.191049.115 · Cited by 273
Role of the Aryl Hydrocarbon Receptor in Carcinogenesis and Potential as a Drug Target
Safe, S. H., Lee, S., & Jin, U.-H. (2013) · Toxicological Sciences · doi:10.1093/toxsci/kft128 · Cited by 281
Induction of microRNAs, mir-155, mir-222, mir-424 and mir-503, promotes monocytic differentiation through combinatorial regulation
Forrest, A. R. R., Kanamori-Katayama, M., Tomaru, Y., Lassmann, T., Ninomiya, N., Takahashi, Y., de Hoon, M. J. L., Kubosaki, A., Kaiho, A., Suzuki, M., et al. (2010) · Leukemia · doi:10.1038/leu.2009.246 · Cited by 223
Isolation and Characterization of Progenitor-Like Cells from Human Renal Proximal Tubules
Lindgren, D., Boström, A.-K., Nilsson, K., Hansson, J., Sjölund, J., Möller, C., Jirström, K., Nilsson, E., Landberg, G., Axelson, H., et al. (2011) · American Journal Of Pathology · doi:10.1016/j.ajpath.2010.10.026 · Cited by 265
The Primary Transcriptome of Barley Chloroplasts: Numerous Noncoding RNAs and the Dominating Role of the Plastid-Encoded RNA Polymerase
Zhelyazkova, P., Sharma, C. M., Förstner, K. U., Liere, K., Vogel, J., & Börner, T. (2012) · The Plant Cell · doi:10.1105/tpc.111.089441 · Cited by 263
Comparison of RNA-Seq and Microarray Gene Expression Platforms for the Toxicogenomic Evaluation of Liver From Short-Term Rat Toxicity Studies
Rao, M. S., Vleet, T. R. V., Ciurlionis, R., Buck, W. R., Mittelstadt, S. W., Blomme, E. A. G., & Liguori, M. J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2018.00636 · Cited by 291
Gene Expression Commons: An Open Platform for Absolute Gene Expression Profiling
Seita, J., Sahoo, D., Rossi, D. J., Bhattacharya, D., Serwold, T. F., Inlay, M. A., Ehrlich, L. I. R., Fathman, J. W., Dill, D. L., & Weissman, I. L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040321 · Cited by 258
A metagenomic study of diet-dependent interaction between gut microbiota and host in infants reveals differences in immune response
Schwartz, S., Friedberg, I., Ivanov, I. V., Davidson, L. A., Goldsby, J. S., Dahl, D. B., Herman, D., Wang, M., Donovan, S. M., & Chapkin, R. S. (2012) · Genome biology · doi:10.1186/gb-2012-13-4-r32 · Cited by 261

Authors: Edgar, R.
Affiliations: National Institutes of Health (US)
Corresponding author: Edgar, R.
ORCID: Edgar, R.
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2002-01-01
Volume: 30
Issue: 1
Pages: 207-210
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/30.1.207
PMC: PMC99122
OpenAlex: W2020541351
Cited by: 13,288
References: 8
Citations by year: 2026: 616 · 2025: 746 · 2024: 960 · 2023: 1010 · 2022: 991 · 2021: 1102 · 2020: 991 · 2019: 820 · 2018: 689 · 2017: 668 · 2016: 658 · 2015: 649 · 2014: 638 · 2013: 570 · 2012: 510
FWCI: 27.23
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Nucleic Acids Research (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: Gene Expression Omnibus, gene expression data, genomic hybridization, microarray data, public data repositories, data submission, data storage and retrieval, biological specimen
MeSH terms: Animals, Communication, Database Management Systems, Forecasting, Humans, National Library of Medicine (U.S.), United States, Information Storage and Retrieval, Genome, Internet, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks