GEN3VA: Aggregation and analysis of gene expression signatures from related studies

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Gundersen, Gregory W ^a,b   Jagodnik, Kathleen M ^c,d   Woodland, Holly ^e   Fernandez, Nicholas F ^a,b   Sani, Kevin ^a,b   Dohlman, Anders B ^a,b   Ung, Peter Man Un ^a   Monteiro, Caroline D ^a,b   Schlessinger, Avner ^a   Ma'ayan, Avi ^a,b  

^a Department of Pharmacological Sciences ^*  (United States)

^b MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^c NASA GLENN RESEARCH CENTER   (United States)

^d BAYLOR COLLEGE OF MEDICINE   (United States)

^e the Fairway   (United Kingdom)

Author keywords

Data mining; Interactive reports; Microarrays; Systems Biology

Indexed keywords

AGGREGATES; DATA MINING; MAMMALS; MICROARRAYS; OPEN SYSTEMS; ORBITS; PRINCIPAL COMPONENT ANALYSIS; WEBSITES;

DATA REPRODUCIBILITY; DIFFERENTIALLY EXPRESSED GENE; GENE EXPRESSION PROFILING; GENE EXPRESSION SIGNATURES; GLUCOCORTICOID RECEPTOR; INTEGRATIVE ANALYSIS; INTERACTIVE REPORTS; SYSTEMS BIOLOGY;

GENE EXPRESSION;

DEXAMETHASONE; TRANSCRIPTOME;

AGING; ANIMAL; DRUG EFFECTS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; PROCEDURES; REPRODUCIBILITY; SOFTWARE;

AGING; ANIMALS; DEXAMETHASONE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; REPRODUCIBILITY OF RESULTS; SOFTWARE; TRANSCRIPTOME;

EID: 84994904735     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-1321-1     Document Type: Article

References (41)

1. Barrett T, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res. 2013;41(D1):D991-5.
2. Brazma A, et al. ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2003;31(1):68-71.
3. Ochsner SA, et al. GEMS (Gene Expression MetaSignatures), a Web resource for querying meta-analysis of expression microarray datasets: 17β-estradiol in MCF-7 cells. Cancer Res. 2009;69(1):23-6.
4. Kerkentzes K, et al. Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue. Front Oncol. 2014;4:251.
5. Zhu Y, et al. GEOmetadb: powerful alternative search engine for the Gene Expression Omnibus. Bioinformatics. 2008;24(23):2798-800.
6. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 2007;23(14):1846-7.
7. Gundersen GW, et al. GEO2Enrichr: browser extension and server app to extract gene sets from GEO and analyze them for biological functions. Bioinformatics. 2015;31(18):3060-2.
8. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma'ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016;44(W1):W90-W97.
9. Clark NR, et al. Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool. In: Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE; 2015. http://ieeexplore.ieee.org/document/7359689/?reload=true.
10. Duan Q, et al. L1000CDS2: LINCS L1000 Characteristic Direction Signatures Search Engine. npj Systems Biology and Applications. 2016. In press.
11. Clark NR, et al. The characteristic direction: a geometrical approach to identify differentially expressed genes. BMC Bioinformatics. 2014;15:79.
12. Pedregosa, Fabian, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res. 2011;12:2825-30.
13. Clustergrammer. Available from: http://amp.pharm.mssm.edu/clustergrammer/. Accessed 1 June 2016.
14. Chen EY, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.
15. Bledsoe RK, et al. Crystal structure of the glucocorticoid receptor ligand binding domain reveals a novel mode of receptor dimerization and coactivator recognition. Cell. 2002;110(1):93-105.
16. Berman HM, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235-42.
17. DeLano WL. The PyMOL molecular graphics system. 2002.
18. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207-10.
19. Network Analysis in Systems Biology. Available from: https://www.coursera.org/learn/network-biology. Accessed 1 Apr 2015.
20. Big Data Science with the BD2K-LINCS Data Coordination and Integration Center. Available from: https://www.coursera.org/learn/bd2k-lincs. Accessed 1 Oct 2015.
21. Ashburner M, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25-9.
22. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27-30.
23. Smith CL, Goldsmith C-AW, Eppig JT. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol. 2005;6(1):R7.
24. Consortium EP. The ENCODE (ENCyclopedia of DNA elements) project. Science. 2004;306(5696):636-40.
25. Bernstein BE, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010;28(10):1045-8.
26. Lachmann A, et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics. 2010;26(19):2438-44.
27. Berger SI, Posner JM, Ma'ayan A. Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics. 2007;8:372.
28. Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, Feldmann AS, Hu KS, McDermott MG, Duan Q, Clark NR, Jones MR, Kou Y, Goff T, Woodland H, Amaral FMR, Szeto GL, Fuchs O, Schüssler-Fiorenza Rose SM, Sharma S, Schwartz U, Bausela XB, Szymkiewicz M, Maroulis V, Salykin A, Barra CM, Kruth CD, Bongio NJ, Mathur V, Radmila DT, Rubin UD, Malatras A, Fulp CT, Galindo JA, Motiejunaite R, Jüschke C, Dishuck PC, Lahl K, Jafari M, Aibar S, Zaravinos A, Steenhuizen LH, Allison LR, Gamallo P, de Andres Segura F, Devlin TD, Pérez-García V, Ma'ayan A. Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd. Nature Communications 7, 12846. 2016. doi: 10.1038/ncomms12846.
29. Yang X, et al. Impact of PICALM and CLU on hippocampal degeneration. Hum Brain Mapp. 2016;37(7):2419-30.
30. Smith LK, et al. beta2-microglobulin is a systemic pro-aging factor that impairs cognitive function and neurogenesis. Nat Med. 2015;21(8):932-7.
31. Panwar P, et al. Changes in Structural-Mechanical Properties and Degradability of Collagen during Aging-associated Modifications. J Biol Chem. 2015;290(38):23291-306.
32. Eppig JT, et al. The Mouse Genome Database (MGD): facilitating mouse as a model for human biology and disease. Nucleic Acids Res. 2015;43(Database issue):D726-36.
33. Wang X, Stocco DM. The decline in testosterone biosynthesis during male aging: a consequence of multiple alterations. Mol Cell Endocrinol. 2005;238(1-2):1-7.
34. Rottenberg H. Longevity and the evolution of the mitochondrial DNA-coded proteins in mammals. Mech Ageing Dev. 2006;127(9):748-60.
35. Zhao J, et al. NF-kappaB activation with aging: characterization and therapeutic inhibition. Methods Mol Biol. 2015;1280:543-57.
36. Kawahara TL, et al. SIRT6 links histone H3 lysine 9 deacetylation to NF-kappaB-dependent gene expression and organismal life span. Cell. 2009;136(1):62-74.
37. O'Brown ZK, et al. The Inflammatory Transcription Factors NFkappaB, STAT1 and STAT3 Drive Age-Associated Transcriptional Changes in the Human Kidney. PLoS Genet. 2015;11(12):e1005734.
38. Paimela T, et al. Celastrol regulates innate immunity response via NF-kappaB and Hsp70 in human retinal pigment epithelial cells. Pharmacol Res. 2011;64(5):501-8.
39. Ni H, et al. Celastrol inhibits lipopolysaccharide-induced angiogenesis by suppressing TLR4-triggered nuclear factor-kappa B activation. Acta Haematol. 2014;131(2):102-11.
40. Chalapareddy S, et al. Radicicol Confers Mid-Schizont Arrest by Inhibiting Mitochondrial Replication in Plasmodium falciparum. Antimicrob Agents Chemother. 2014;58(8):4341-52.
41. Altonsy MO, et al. Context-dependent cooperation between nuclear factor kappaB (NF-kappaB) and the glucocorticoid receptor at a TNFAIP3 intronic enhancer: a mechanism to maintain negative feedback control of inflammation. J Biol Chem. 2014;289(12):8231-9.