Dataset Information

The Gene Expression Omnibus Database.

ABSTRACT: The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports community-derived reporting standards that specify provision of several critical study elements including raw data, processed data, and descriptive metadata. The database not only provides access to data for tens of thousands of studies, but also offers various Web-based tools and strategies that enable users to locate data relevant to their specific interests, as well as to visualize and analyze the data. This chapter includes detailed descriptions of methods to query and download GEO data and use the analysis and visualization tools. The GEO homepage is at http://www.ncbi.nlm.nih.gov/geo/.

SUBMITTER: Clough E

PROVIDER: S-EPMC4944384 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Gene Expression Omnibus Database.

Clough Emily E Barrett Tanya T

Methods in molecular biology (Clifton, N.J.) 20160101

The Gene Expression Omnibus (GEO) database is an international public repository that archives and freely distributes high-throughput gene expression and other functional genomics data sets. Created in 2000 as a worldwide resource for gene expression studies, GEO has evolved with rapidly changing technologies and now accepts high-throughput data for many other data applications, including those that examine genome methylation, chromatin structure, and genome-protein interactions. GEO supports co ...[more]

PMID: 27008011

Similar Datasets

Project description:Sepsis is a life-threatening condition in which an uncontrolled inflammatory host response is triggered. The exact pathogenesis of sepsis remains unclear. The aim of the present study was to identify key genes that may aid in the diagnosis and treatment of sepsis. mRNA expression data from blood samples taken from patients with sepsis and healthy individuals was downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) between the two groups were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein‑protein interaction (PPI) network construction, was performed to investigate the function of the identified DEGs. Furthermore, for validation of these results, the expression levels of several DEGs were analyzed by reverse transcription quantitative‑polymerase chain reaction (RT‑qPCR) in three patients with sepsis and three healthy blood samples to support the results obtained from the bioinformatics analysis. Receiver operating characteristic analyses were also used to analyze the diagnostic ability of the identified DEGs for sepsis. The results demonstrated that a total of 4,402 DEGs, including 1,960 upregulated and 2,442 downregulated genes, were identified between patients with sepsis and healthy individuals. KEGG pathway analysis revealed that 39 DEGs were significantly enriched in toll‑like receptor signaling pathways. The top 20 upregulated and downregulated DEGs were used to construct the PPI network. Hub genes with high degrees, including interleukin 1 receptor‑associated kinase 3 (IRAK3), S100 calcium‑binding protein (S100)A8, angiotensin II receptor‑associated protein (AGTRAP) and S100A9, were demonstrated to be associated sepsis. Furthermore, RT‑qPCR results demonstrated that IRAK3, adrenomedullin (ADM), arachidonate 5‑lipoxygenase (ALOX5), matrix metallopeptidase 9 (MMP9) and S100A8 were significantly upregulated, while ectonucleoside triphosphate diphosphohydrolase 1 (ENTPD1) was upregulated but not significantly, in blood samples from patients with sepsis compared with healthy individuals, which was consistent with bioinformatics analysis results. Therefore, AGTRAP, IRAK3, ADM, ALOX5, MMP9, S100A8 and ENTPD1 were identified to have potential diagnostic value in sepsis. In conclusion, dysregulated levels of the AGTRAP, IRAK3, ADM, ALOX5, MMP9, S100A8 and ENTPD1 genes may be involved in sepsis pathophysiology and may be utilized as potential diagnostic biomarkers or therapeutic targets.

Project description:Background: Alcohol dependence is an overall health-related challenge; however, the specific mechanisms underlying alcohol dependence remain unclear. Serine proteinase inhibitor A3 (SERPINA3) plays crucial roles in multiple human diseases; however, its role in alcohol dependence clinical practice has not been confirmed. Methods: We screened Gene Expression Omnibus (GEO) expression profiles, and identified differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks were generated using STRING and Cytoscape, and the key clustering module was identified using the MCODE plugin. SERPINA3-based target microRNA prediction was performed using online databases. Functional enrichment analysis was performed. Fifty-eight patients with alcohol dependence and 20 healthy controls were recruited. Clinical variables were collected and follow-up was conducted for 8 months for relapse. Results: SERPINA3 was identified as a DEG. ELANE and miR-137 were identified after PPI analysis. The enriched functions and pathways included acute inflammatory response, response to stress, immune response, and terpenoid backbone biosynthesis. SERPINA3 concentrations were significantly elevated in the alcohol dependence group than in healthy controls (P < 0.001). According to the median value of SERPINA3 expression level in alcohol dependence group, patients were divided into high SERPINA3 (≥2677.33 pg/ml, n = 29) and low SERPINA3 groups (<2677.33 pg/ml, n = 29). Binary logistic analysis indicated that IL-6 was statistically significant (P = 0.015) Kaplan-Meier survival analysis did not indicate any difference in event-free survival between patients with low and high SERPINA3 levels (P = 0.489) after 8 months of follow-up. Receiver characteristic curve analysis revealed that SERPINA3 had an area under the curve of 0.921 (P < 0.0001), with a sensitivity and specificity of 93.1 and 80.0%, respectively. Cox regression analysis revealed that aspartate transaminase level was a negative predictor of relapse (β = 0.003; hazard ratio = 1.003; P = 0.03). Conclusions: SERPINA3 level was remarkably elevated in patients with alcohol dependence than healthy controls, indicating that SERPINA3 is correlated with alcohol dependence. However, SERPINA3 may not be a potential predictive marker of relapse with patients in alcohol dependence.

Dataset Information

The Gene Expression Omnibus Database.

Publications

The Gene Expression Omnibus Database.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets