RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

doi:10.1201/b16589-9

Cited by 19 publications

(23 citation statements)

References 29 publications

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“…DCGL 2.0 (24) is an R package for identifying differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) from gene expression microarray data. It examines the expression correlation based on the exact co-expression changes of gene pairs between two conditions, and thus is able to differentiate significant co-expression changes from relatively trivial ones (25). It has four functional modules: Gene filtration, link filtration, differential co-expression analysis (DCEA) and differential regulation analysis.…”

Section: Construction Of Differential Co-expression Network By Dcglmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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Abstract. The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysisrevealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

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Section: Construction Of Differential Co-expression Network By Dcglmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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“…Gene co-expression analysis was developed to explore gene interconnections at the expression level from a system perspective, and differential co-expression analysis, which examines the change in gene expression correlation between two conditions, was accordingly designed as a complementary technique to traditional differential expression analysis (Liu et al, 2010;Yu et al, 2011). The third part, the core of the study, includes five methods for identifying DCGs and DCGLs, which mainly differ in the measure of differential co-expression of a gene.…”

Section: Gene Expression Profiling Analysismentioning

confidence: 99%

Biochip analysis of prostate cancer

Fan¹,

Wang²,

Feng³

et al. 2014

Genet. Mol. Res.

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ABSTRACT. Microarray expression analysis was used to forecast the roles of differentially co-expressed genes (DCG) and DCG and links in the pathogenesis of prostate cancer. In addition, we demonstrate that the relationship between transcriptional factors (TFs) and their targets can be considered a key factor in determining the difference between primary and metastatic prostate cancer. Regulatory impact factors were adopted to calculate the impact of TF. We identified 5 TFs and 29 target genes important in the transition between normal prostate and primary prostate cancer and 2 TFs and 7 target genes important in the transition between primary and metastatic prostate cancer. These results suggest that it may be possible to predict the clinical behavior of prostate cancer based on gene expression analysis.

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“…Then, the samples were divided into two groups (RA vs. osteoarthritis) and the t-test method in the LIMMA package (a set of tools for background correction and scaling) of R software (20) was used to calculate the differentially expressed genes (DEGs), with threshold of |logFC|>1.0 and a P-value <0.05. Finally, the DCGs and DCLs were calculated with the cutoff criterion of q<0.25 using the functions of DCe, DCp and DCsum in the differential coexpression analysis and differential regulation analysis of gene expression microarray data (DCGL) package (21,22). The DCGL package included four modules: Gene screening, relations screening, analysis of differential co-expression and differential regulation analysis.…”

Section: Analysis Of Differential Co-expressionmentioning

confidence: 99%

Differential co-expression analysis of rheumatoid arthritis with microarray data

Wang

Zhao

et al. 2014

Molecular Medicine Reports

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Abstract. The aim of the present study was to investigate the underlying molecular mechanisms of rheumatoid arthritis (RA) using microarray expression profiles from osteoarthritis and RA patients, to improve diagnosis and treatment strategies for the condition. The gene expression profile of GSE27390 was downloaded from Gene Expression Omnibus, including 19 samples from patients with RA (n=9) or osteoarthritis (n=10). Firstly, the differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.05, using the t-test method in LIMMA package. Then, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) were screened with q<0.25 by the differential coexpression analysis and differential regulation analysis of gene expression microarray data package. Secondly, pathway enrichment analysis for DCGs was performed by the Database for Annotation, Visualization and Integrated Discovery and the DCLs associated with RA were selected by comparing the obtained DCLs with known transcription factor (TF)-targets in the TRANSFAC database. Finally, the obtained TFs were mapped to the known TF-targets to construct the network using cytoscape software. A total of 1755 DEGs, 457 DCGs and 101988 DCLs were achieved and there were 20 TFs in the obtained six TF-target relations (STAT3-TNF, PBX1-PLAU, SOCS3-STAT3, GATA1-ETS2, ETS1-ICAM4 and CEBPE-GATA1) and 457 DCGs. A number of TF-target relations in the constructed network were not within DCLs when the TF and target gene were DCGs. The identified TFs may have an important role in the pathogenesis of RA and have the potential to be used as biomarkers for the development of novel diagnostic and therapeutic strategies for RA.

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RSEM: Accurate Transcript Quantification from RNA-Seq Data With or Without a Reference Genome

Cited by 19 publications

References 29 publications

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

Biochip analysis of prostate cancer

Differential co-expression analysis of rheumatoid arthritis with microarray data

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