In total, 19,532 differentially expressed genes (DEGs) were identified. These genes were annotated into 144 Gene Ontology (GO) terms and 18 clusters of orthologous groups (COG) involved in 129 Kyoto Encyclopedia of Genes and Genomes (KEGG) defined pathways After validation with the TCGA database, a total of 155 differentially expressed genes (DEGs) were consistent with those identified by GSE76275. Based on the STRING database, we constructed a PPI network using the DEGs obtained from GSE76275 datasets In recent years, the high-throughput sequencing has been used to identify the differentially expressed genes (DEGs) in peripheral blood of CAD patients. For example, Liu et al. [ 15 ] found that 12 genes associated with Toll-like receptor signaling pathways are closely related to the occurrence and severity of CAD by transcriptome sequencing of. When the expression levels are significantly different, the corresponding genes are called Differentially Expressed Genes (DEGs). These DEGs are assumed to be the molecular driving force and/or the molecular biomarkers of different phenotypes Using an integrated bioinformatics method, we explored the functions of 113 differentially expressed genes (DEGs) between them
An overview of differentially expressed genes (DEGs) involved in normal and abortive pistils of Japanese apricot. (A) Heatmap showing the differential expression pattern of the DEGs. The color scale shows the gene expression values (log2fc). (B) Up- and down-regulation of differentially To investigate the differentially expressed genes (DEGs) and molecular regulatory mechanisms related to adipogenic differentiation between IMF- and AbF-derived preadipocytes, we analysed the mRNA expression profiles in preadipocytes (0d, Pre-) and adipocytes (10d, Ad-) from IMF and AbF of Gushi chickens Then, we obtained the differentially expressed genes (DEGs) through the R 4.0.1 software. STRING was used to construct protein-protein interaction (PPI) networks; then, the Cytoscape 3.7.2 software was used to visualize the PPI and screen hub genes. GraphPad Prism 8.0 software was used to verify the expression of the hub gene Differentially expressed genes (DEGs) were identified using GEO2R. A protein-protein interaction network was constructed. Functional annotation for DEGs was performed via DAVID, Metascape, and BiNGO DEGS means Differentially Expressed Genes. DEGS is an abbreviation for Differentially Expressed Genes. Share this. Have you found the page useful? Please use the following to spread the word: About | Contact Us Link to Us iOS app | Android Popular Abbreviations Popular Categories
. Their annotation and analyses were conducted using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) to predict their possible functions and pathways involved Functional analysis showed that most of the differentially expressed genes (DEGs) were involved in protein processing in the endoplasmic reticulum, plant hormone signal transduction, and..
Differently expressed genes screening and clustering. For soybeans in Al tolerance group and Al intolerance group, the differentially expressed genes (DEGs) for different samples during different treatments at different times were screened by limma Version 3.34.9 in R package. The threshold was FDR (adjust p-value) < 0.05 and |log2FC|> 1. For. Differentially expressed genes (DEGs) were identified using the following filter criteria: and. Fold change indicates the ratio of expression quantity between two samples (groups). The false discovery rate (FDR) was obtained by correcting the significant difference value. 2.4 (A) The miRNA-mRNA network consists of 12 differentially expressed genes (DEGs) and 257 miRNAs. (B) The lncRNA-miRNA network consists of 10 differentially expressed lncRNAs (DElncRNAs) and 79 miRNAs. The green and red colors note the downregulation and upregulation, respectively
Figure 7 Kyoto Encyclopedia of Genes and Genomes pathway map illustrating differentially expressed genes (DEGs), as identified by protein-protein interaction network analysis using STRING. The network map was generated in the Database for Annotation, Visualization and Integrated Discovery 6.8. Each node is a differentially expressed gene Identification of differentially expressed genes between lines AKCMS11 and AKPR303. We identified 3167 differentially expressed genes (DEGs) between the two lines containing 1432 up-regulated and 1390 down-regulated in the AKCMS11 sterile buds as compared to AKPR303 fertile buds (Additional file 2: Table S6).Additionally, 683 DEGs were uniquely expressed in the sterile genotype and 157 DEGs. The identification of differentially expressed genes (DEGs) is an important task in many biological studies. The currently widely used methods often calculate a score for each gene by estimating the significance level in terms of the differential expression. However, biological experiments often have only three duplications, plus plenty of noises contain in gene expression datasets, which. These genes are included because they are differentially expressed in other comparisons, specified in another column. Raw expression data is also attached, which the users can use to analyze further. For read counts data, three methods are available in iDEP for identifying DEGs, namely DESeq2 (Love et al., 2014), limma-voom (Law et al., 2014. Transcriptome sequencing and analysis revealed differentially expressed genes (DEGs) between the mutant and normal capitula, some of which may encode important regulators controlling the differential development. Many transcription factors and genes related to the photoperiod and gibberellin (GA) pathways, floral organ identity, and the.
A multitude of genes tightly regulate ovarian follicular development and hormone secretion. These complex and coordinated biological processes are altered during pregnancy. In order to further understand the regulatory role of these genes during pregnancy, it is important to screen the differentially expressed genes (DEGs) in the ovaries of pregnant and nonpregnant mammals In this study, we profiled genes that are differentially expressed between cracked and normal jujube fruits through RNA sequencing (RNA-seq). We selectively confirmed differentially expressed genes (DEGs) using quantitative RT-PCR. Among 1036 DEGs, 785 genes were up-regulated and 251 genes were down-regulated in cracked jujube fruits . Abbreviation: DEGs, differentially expressed genes
A total of 1332 differentially expressed genes (DEGs) were identified, providing evidence that the development of hair follicles among the three distinct stages changed considerably. A total of 683 genes with significant differential expression were detected between anagen and catagen, 530 DEGs were identified between anagen and telogen, and. Identifying differentially expressed genes - what are Differentially Expressed Genes (DEGs) Differentially expressed genes (DEGs) were identified using GEO2R. A protein-protein interaction network was constructed. Functional annotation for DEGs was performed via DAVID, Metascape, and BiNGO. Significant modules and hub genes were identified using Cytoscape, and expression of hub genes and relationships between hub genes and MC were. The aim of this study was to identify the key differentially expressed genes (DEGs) and high-risk gene mutations in breast ductal carcinoma in situ (DCIS). Raw data (GSE36863) were downloaded from the database of Gene Expression Omnibus (GEO), including three DCIS samples (DCIS cell lines MCF10.DCIS, Sum102, and Sum225) and one normal control sample (normal mammary epithelial cell line MCF10A) DEGs in vivo human MAIT cell activation Differentially expressed genes (log 2 FC>1 and adjusted P<0.05) between MAIT cells isolated from the peripheral blood of human volunteers (n=14) 1 day prior to and 1 day following immunization with 5×10 10 vp of ChAdOx1
This study aimed to compare differentially expressed genes (DEGs) in 45,X and 47, XXY cells. Gene expression patterns were analyzed from microarray data obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Our results show that there are 16 common genes that are oppositely regulated in. Detecting differentially expressed genes (DEGs) between two phenotypes is a basic task in high-throughput biological studies. It is often hard to detect DEGs under the circumstances where the genes are differentially expressed only slightly between two phenotypes. Especially when the sample size is no
Differentially expressed genes (DEGs) were obtained by limma package. And Database for Annotation, Visualization and Integrated Discovery (DAVID) databases were further used to identify the potential gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Furthermore, a protein-protein interaction (PPI) network was. A total of 2146 differentially expressed genes (DEGs) were identified in the EC samples (A), and 1189 DEGs were identified in the HIP samples (B). Then 79 upregulated common DEGs (CDEGs) were found in EC and HIP ( C ), the heatmap ( D ) showed the expression level of these upregulated CDEGs
To detect differentially expressed genes (DEGs) under two conditions (e.g., disease vs. healthy status), some statistical methods have been used. For example, Marioni et al [ 3 ] have proposed to use likelihood ratio test (LRT) based on a generalized linear model (GLM) with dependent variable (count) having Poisson distribution to detect DEGs. February 5, 2016 Leave a comment 3,720 Views. Determining differentially expressed genes (DEGs) between biological samples is the key to understand how genotype gives rise to phenotype. RNA-seq and microarray are two main technologies for profiling gene expression levels. However, considerable discrepancy has been found between DEGs detected. In this study, Illumina RNA sequencing was performed to characterize differentially expressed genes (DEGs) between virgin and mated adults of different ages in a single female strain of C. sinica. In total, 713,563,726 clean reads were obtained and de novo assembled into 109,165 unigenes with an average length of 847 bp (N50 of 1,754 bp), among. The DEGs were analyzed with the LIMMA package, and the coregulated genes were chosen as the intersection of DEGs between the two datasets, called common differentially expressed genes (CDEGs). The enrichment study of CDEGs was subsequently performed with the DAVID and KOBAS web resources A study identified several differentially expressed genes (DEGs) that are linked to the development of diffuse large B-cell lymphoma (DLBCL). The results appeared in the World Journal of Clinical Cases. Researchers used the GSE60 dataset to identify the DEGs in DLBCL
Objective: The aim of this research is to identify hub genes that might serve as genetic markers to enhance the diagnosis, treatment, and prognosis of DN. Methods: The procedures of the study include access to public data, identification of differentially expressed genes (DEGs) by GEO2R, and functional annotation of DEGs using enrichment analysis The information on differentially expressed genes (DEGs), differentially methylated genes (DMGs), and pathways involved with the pathogenesis of childhood asthma is obtained primarily from studies employing microarrays in peripheral blood, bronchial and epithelial biopsy specimens, and nasal lavage samples A number of differentially expressed genes (DEGs) and differentially methylated genes (DMGs) have been identified in PCa though microarray analysis [11, 12]. Although some studies have focused on specific genes with aberrant DNA hypermethylation or hypomethylation in PCa, an integrated analysis of gene expression, methylation, and signalling. Abstract. Background. Coronary artery disease (CAD) is a chronic inflammatory disease caused by development of atherosclerosis (AS), which is the leading cause of mortality and disability. Our study aimed to identify the differentially expressed genes (DEGs) in CD14+ monocytes from CAD patients compared with those from non-CAD controls, which might pave the way to diagnosis and treatment for. the differentially expressed genes (DEGs). After func-tional annotation of the DEGs, motif finding was per-formed in the promoter region of DEGs. Additionally, single nucleotide variants (SNVs) in DCIS were identi-fied and annotated. These results might improve our un-derstanding of the molecular mechanism underlyin
As a result, a total of 84 differentially expressed genes (DEGs) were identified (−0.5 < log 2 FC > 0.5, p < 0.05) (Additional file 1: Table S1), from which 48 protein-coding genes and 2 miRNA genes (MIR21 and MIR223) were upregulated, while 34 protein-coding genes were downregulated in MI patients The aim of this study was to characterise the molecular mechanisms of transcriptional regulation of Differentially Expressed Genes (DEGs) in rice coleoptiles under anoxia by identifying motifs that are common in the promoter region of co-regulate
The differentially expressed genes (DEGs) between ear and tassel primordia were enriched in pathways responding to oxygen-containing compound, hormone, and transcription factor activity by gene ontology (GO) analysis using agrigo (Additional file 1: Figure S3a, b) Run edgeR. Plot DEG results. Venn diagrams of DEG sets. The analysis of differentially expressed genes (DEGs) is performed with the glm method of the edgeR package (Robinson et al., 2010). The sample comparisons used by this analysis are defined in the header lines of the targets.txt file starting with <CMP> Some differentially expressed genes only express in SLE-iPSCs or control-iPSCs. The 4,254 differentially expressed genes were annotated in Gene Ontology (GO) and KEGG pathway analysis. We found that the DEGs involved in 9 cellular components, 9 molecular functions, 8 biological processes and 6 pathways with p-value ≤ 0.05 Differentially expressed genes (DEGs) with ㅣlog 2 FCㅣ > 1 and FDR ≤ 0.05 from comparisons of RKO cells with KRAS G12C/D/V and G13D to wildtype or BRAF V600E were identified in the overlapped regions of the Venn diagramme analysis (Supplementary Fig. 5 A-D and F, Additional File 1).The Trend of expressions were determined as all. Calculating Differentially Expressed Genes (DEGs) from RNA-sequencing requires replicates to estimate gene-wise variability, a requirement that is at times financially or physiologically infeasible in clinics. By imposing restrictive transcriptome-wide assumptions limiting inferential opportunities of conventional methods (edgeR, NOISeq-sim, DESeq, DEGseq), comparing two conditions without.
by interplay between genes and the environment and, thus, blood-based IS biomarkers are of significant clinical value.Therefore, this study aimed to find global differentially expressed genes (DEGs) in-silico, to identify key enriched genes via gene set enrichment analysis (GSEA) and to determine the clinical significance of these genes in IS Differentially expressed genes (DEGs) in stage III serous ovarian cancers. The patients included in this study ranged in age between 38 and 69 (mean age 53.3 ± 7.5 years). All 16 patients had a diagnosis of FIGO stage III papillary serous ovarian carcinoma. To identify genes that showed a predominant change of expression in patients with stage.
However, differentially expressed genes (DEGs) are rarely consistent across different studies (Nesic et al., 2002; Di Giovanni et al., 2003; Xiao et al., 2005; Byrnes et al., 2011; Lee-Liu et al., 2014; Duran et al., 2017), and many important genes and pathways have not been thoroughly investigated. As DEGs may provide therapeutic targets. To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify. A method which has a better ROC curve, in this sense, will produce top lists with more differentially expressed genes (DEGs), fewer non-DEGs, and, consequently, will leave out fewer DEGs. Furthermore, such a method will give higher average ranks to the DEGs, if the ranking is such that high rank means more evidence of differential expression
We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because it's not limited to available probes, has increased sensitivity. Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database A-Lister v1.1. A-Lister is a dual interface (CLI & GUI) tool that assists with analysis of differentially expressed entities (DEEs), such as differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and differentially methylated positions/regions (DMPs/DMRs), across multiple pairwise comparisons
FIG 1 Principal-component analysis (PCA) of the transcriptomes during development of differentially expressed genes (DEGs) of Puccinia striiformis f. sp. tritici in response to the high-temperature seedling plant resistance in wheat cultivar XY6. For the principal components 1 and 2, eigenvalues are 64.1% and 18.0%, respectively differentially expressed genes (DEGs), and bioinformatics methods were used for in -depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased To uncover the genes involved in the development of osteosarcoma (OS), we performed a meta-analysis of OS microarray data to identify differentially expressed genes (DEGs) and biological functions associated with gene expression changes between OS and normal control (NC) tissues. We used publicly available GEO datasets of OS to perform a meta-analysis Differentially expressed genes (DEGs) between breast cancer and normal breast tissue samples were identified by using the limma package followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to seek the potential functions of DEGs