GOsummaries: an R Package for Visual Functional Annotation of Experimental Data

Kolde, Raivo; Vilo, Jaak

doi:10.12688/f1000research.6925.1

Cited by 1,224 publications

(1,289 citation statements)

References 27 publications

(24 reference statements)

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“…The threshold were log2 (Fold Change)>1 and P value <0.05. Subsequently, the differences were visualized in volcano plot, Venn diagram, and Heat map by using ggplot2 [Wickham, 2016], VennDiagram [Chen and Boutros, 2011], and pheatmap [Kolde, 2015] in R language.…”

Section: Screening Of Differentially Expressed Genes (Degs)mentioning

confidence: 99%

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction

Lin

et al. 2017

J of Cellular Biochemistry

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Acute myocardial infarction (AMI) is a common disease with serious consequences in mortality and cost. Here we aim to screen the differentially expressed genes (DEGs) as biomarkers for early diagnosis of AMI. The microarray data of AMI was downloaded from Gene Expression Omnibus (GEO), including two independent types of research GSE66360 and GSE62646. The DEGs between control and processed samples were screened out by using limma package. Meanwhile, we performed functional analysis of GO terms and/or KEGG pathways to observe the enriched pathways of the DEGs. Finally, regression analysis of raw data was performed by using packet affyPLM in R language. Dataset GSE62646 contained 53 DEGs (FC log2>1 and P value <0.05) between first-day samples from 28 STEMI patients and control samples from 14 patients without myocardial infarction history. There were 12 up-regulated and 41 down-regulated genes, 35 DEGs annotated. In GSE66360, a total of 3034 DEGs between 32 AMI patients and 38 healthy persons were obtained, including 1861 up-regulated and 1173 downregulated DEGs. The comparison of the DEGs in two datasets revealed four common up-regulated genes (EGR1, STAB1, SOCS3, TMEM176A). In enrichment analysis, STAB1, SOCS3, EGR1 involved in metabolic regulation and signaling pathways related to coronary artery disease with a characteristic change (P < 0.05). The DEGs, especially the four up-regulated common genes, could serve as biomarkers for early diagnosis of AMI. Additionally, the relative biological pathways these DEGs enriched in might provide a good reference to explore the molecular expression mechanism of AMI. J. Cell. Biochem. 119: 650-658, 2018. © 2017 Wiley Periodicals, Inc. KEY WORDS: ACUTE MYOCARDIAL INFARCTION; DIFFERENTIALLY EXPRESSED GENES; FUNCTIONAL ANNOTATION; BIOMARKERSA cute myocardial infarction (AMI) can cause heart failure, an irregular heartbeat, cardiogenic shock, or cardiac arrest [Risk et al., 2017]. It is the major cause of morbidity and mortality in the general population. This well-known heart attack is generally classified into ST-elevation MI (STEMI) and non-ST elevation MI (NSTEMI) [Moe and Wong, 2010]. According to the World Health Organization (WHO), annually, more than 3 million people are estimated with acute STEMI and that and more than 4 million suffers from NSTEMI [Organization, 2005;Reed et al., 2017]. In the modern society, the aging of population and comorbidities (e.g., obesity and metabolic syndrome) become more prevalent. All theses public health burden are the high risks of AMI. Besides, other health problems caused by ischemic heart disease are likely to increase even further [Yasuda and Shimokawa, 2009].The main treatments for STEMI include thrombolysis and percutaneous coronary intervention [Bates and Menees, 2012]. Hereinto, the percutaneous coronary intervention (PCI) should be performed within 90-120 min, if not, thrombolysis, preferably within 30 min of arrival to the hospital, is recommended [Bassand et al., 2005]. All these treatments need is extremely a time ...

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Section: Screening Of Differentially Expressed Genes (Degs)mentioning

confidence: 99%

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction

Lin

et al. 2017

J of Cellular Biochemistry

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“…Heat maps of microarray data were generated using the pheatmap package in R (56). Relative expression of each transcript is reported as log-transformed expression value for each sample, normalized to the median expression value of the transcript across all six samples.…”

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confidence: 99%

Histone Deacetylase 3 Coordinates Deacetylase-independent Epigenetic Silencing of Transforming Growth Factor-β1 (TGF-β1) to Orchestrate Second Heart Field Development

Lewandowski

Janardhan

Trivedi

2015

Journal of Biological Chemistry

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Background: Histone-modifying genes play critical roles in the pathogenesis of human congenital heart disease. Results: HDAC3 recruits PRC2 complex to mediate epigenetic silencing of TGF-␤1 in a deacetylase-independent manner within second heart field progenitor cells. Conclusion: HDAC3-mediated epigenetic silencing of TGF-␤1 is required for normal heart development. Significance: This is the first report of HDAC-mediated epigenetic regulation of second heart field development.

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“…The threshold was log2 (fold change) >1 and P<0.05. Subsequently, the differences were visualized in a volcano plot, Venn diagram and Heat map by using ggplot2 (19), Venn diagram (20) and pheatmap (21) in R language.…”

Section: Screening Of Degsmentioning

confidence: 99%

Gene expression analysis for pneumonia caused by Gram-positive bacterial infection

et al. 2018

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Abstract. Gram-positive bacteria are an important pathogenic factor for bacterial pneumonia. The aim of the present study was to identify the differentially expressed genes (DEGs) and to explore their associated pathways or expression patterns. Expression profiling of gene arrays from two independent datasets, GSE6269 and GSE35716, were downloaded from the Gene Expression Omnibus. The DEGs between peripheral blood samples from healthy controls and patients with bacterial pneumonia were identified. The Functional Annotation Tool in the Database for Annotation, Visualization and Integrated Discovery was used to annotate and analyze the DEGs in Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Multiple proteins were used to generate a protein-protein interaction (PPI) network. A total of 624 (621 annotated) were identified in the GSE6269 dataset and 398 (295 annotated) DEGs were identified in the GSE35716 dataset between pneumonia and healthy samples. A total of 40 common DEGs were identified between the 2 datasets, including 4 downregulated and 32 upregulated DEGs. In the GO category cellular component, melanosome was highly enriched among 11 genes; in the category biological process, the three most enriched items were regulation of ruffle assembly, negative regulation of calcium ion transport and necroptotic process. In the KEGG terms, only the nuclear factor-κB signaling pathway (Homo sapiens 04064) was significantly enriched. In the PPI network, five genes (CCL4, TIMP metallopeptidase inhibitor 1, intercellular adhesion molecule 1, plasminogen activator, urokinase receptor and cathepsin B) were identified to have a high degree of interaction with other DEGs. In conclusion, these five genes may represent key genes associated with pneumonia caused by Gram-positive bacteria. All of these results provide primary information and basic knowledge to understand the mechanisms of the pathogenesis.

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GOsummaries: an R Package for Visual Functional Annotation of Experimental Data

Cited by 1,224 publications

References 27 publications

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction

Identification of Biomarkers for Early Diagnosis of Acute Myocardial Infarction

Histone Deacetylase 3 Coordinates Deacetylase-independent Epigenetic Silencing of Transforming Growth Factor-β1 (TGF-β1) to Orchestrate Second Heart Field Development

Gene expression analysis for pneumonia caused by Gram-positive bacterial infection

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