The resulting meta-gene list was compared with the original DE gene list in each of the original studies. To identify significant pathways from the meta-gene list, over-representation analysis (ORA) was performed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and p-values were adjusted by Benjamini-Hochberg’s False Discovery Rate (FDR). A protein-protein interaction (PPI) network was constructed based on STRING database (Szklarczyk et al., 2019 (link)) and then visualized by a web-based tool1 (Xia et al., 2014 (link), 2015 (link); Zhou et al., 2019 (link)). Hub nodes were identified by high degrees and high centrality from the PPI network. The results were then compared with the network constructed by WEB-based GEne SeT AnaLysis Toolkit (Zhang et al., 2005 (link); Wang et al., 2013 (link), 2017 (link); Liao et al., 2019 (link)).
Gene set analysis toolkit
Manufactured by Gene Tech
The GEne SeT AnaLysis Toolkit is a comprehensive suite of laboratory equipment designed for the analysis of genetic sequences. The core function of this toolkit is to facilitate the extraction, amplification, and sequencing of DNA and RNA samples, enabling researchers to study genetic patterns and variations.
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Lab products found in correlation
13 protocols using gene set analysis toolkit
1
Pathway analysis of meta-gene list
2
Transcriptome Analysis of CELSR2 in HCC
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The LinkedOmics database (http://www.linkedomics.org/ login.php) is an open access online biometrics platform whose resource comes from 11,158 patients from the TCGA [17 (link)]. In this study, we analyzed genes differentially expressed in correlation with CELSR2 in the TCGA HCC cohort (n = 371), committed to finding and assessing the correlation between genes by Pearson’s correlation coefficient. Similarly, the Web-based GEne SeT AnaLysis Toolkit (WebGestalt) [18 (link)] was then used to perform GO (CC, BP and MF), KEGG pathway, kinase-target enrichment, miRNA-target enrichment and transcription factor-target enrichment analyses of these related genes.
3
Quantitative Phosphoproteomics and Statistical Analysis
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Statistical tests of immunoblot densitometry were performed in excel using a one-sided two-sample Student’s t-test. For phosphoproteomic analysis, phosphopeptides present in all replicates were determined to have significant changes (Fig. 4a ) in abundance for a given condition, X, if
and p <0.05, where p was calculated with a two-tail heteroscedastic Student’s t-Test. KEGG pathway enrichment of the phosphoproteomic timecourse experiment was assessed with the WEB-based GEne SeT AnaLysis Toolkit using a hypergeometric test with Benjamini-Hochberg multiple-test correction59 (link),60 . The 485 proteins quantified in all three biological replicates served as the reference set.
and p <0.05, where p was calculated with a two-tail heteroscedastic Student’s t-Test. KEGG pathway enrichment of the phosphoproteomic timecourse experiment was assessed with the WEB-based GEne SeT AnaLysis Toolkit using a hypergeometric test with Benjamini-Hochberg multiple-test correction59 (link),60 . The 485 proteins quantified in all three biological replicates served as the reference set.
4
Comprehensive Proteomic Data Analysis
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All the
scaled protein abundances
were submitted to Peruses software for analysis. Z-score was used
for protein abundance normalization and the subsequent heatmap and
volcano plot analysis. The WEB-based GEne SeT AnaLysis Toolkit was
used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
All the changed proteins were analyzed using gene ontology (GO) web-based
searching. STRING database version 10.0 (http://string-db.org ) was used for
protein–protein interaction (PPI) analysis with a confidence
threshold of 0.7. The interaction network was mapped using Cytoscape
(3.8.2).
scaled protein abundances
were submitted to Peruses software for analysis. Z-score was used
for protein abundance normalization and the subsequent heatmap and
volcano plot analysis. The WEB-based GEne SeT AnaLysis Toolkit was
used for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
All the changed proteins were analyzed using gene ontology (GO) web-based
searching. STRING database version 10.0 (
protein–protein interaction (PPI) analysis with a confidence
threshold of 0.7. The interaction network was mapped using Cytoscape
(3.8.2).
5
Differential Expression and Pathway Analysis
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OS-related microarray expression data and gene probe annotation files were downloaded from the GEO DataSets database, and background correction and normalization processing were subsequently conducted using the Affy package in R language.28 (link) Next, linear model-empirical Bayesian statistics combined with traditional t tests were employed to specifically screen out differentially expressed genes.29 (link) MEM was employed to predict differential lncRNAs.30 (link) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted using the WEB-based GEne SeT AnaLysis Toolkit31 (link) in an attempt to elucidate the major biochemical metabolic pathways and signaling pathways.32 (link) Next, miRNAs that had binding sites for both lncRNAs and mRNAs were obtained from RNA22,33 (link) starBase,34 (link) and miRDB.35 (link)
6
Transcriptome Analysis of Thymic Lymphomas
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Data normalization was performed using GeneSpring GX12.5 (Agilent Technologies). Signal intensities for each probe were normalized to the 75th percentile without baseline transformation. Genes that were differentially expressed in thymic lymphomas between vehicle and rapamycin treated Fbxw7+/− p53+/− mice and p53+/− mice were identified by the unpaired Student's t-test with a p-value cut-off of less than 0.05 and a fold change criteria of more than 1.3. Correlation analysis was performed in R using Spearman rank order correlation. Transcript correlation networks were generated using Cytoscape using the Expression Correlation Network plugin with a correlation coefficient cut-off of 0.8. Gene enrichment analysis was performed using the Gene Ontology feature of the WEB-based GEne SeT AnaLysis Toolkit [24 (link),25 (link)].
7
Comprehensive Protein-Protein Interaction Analysis
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STRING (42 (link)) analysis was performed using the multi protein search function using default options. The ‘confidence’ option was chosen to indicate strength of data support for each interaction. In addition, GO terms indicated in the figure legends were highlighted. GO term analysis was performed using the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) (43 (link)) using default settings with the following options: organism of interest: Homo sapiens; method of interest: over-representation analysis (ORA); functional database: geneontology, biological process noRedundant. For GO term analysis of genes identified by mass spectrometry, the Reference Set ‘genome protein-coding’ was used. For GO term analysis of genes identified by RNAseq, the Reference Set ‘genome’ was used.
8
Pathway Analysis of Differentially Expressed Genes in Ovarian Cancer
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The Gene Ontology (GO)17 and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway18 are two powerful databases to explore the underlying activated or inhabited pathways in cancers. KEGG and GO pathway analyses were performed on DEGs in immunoreactive HGS‐OvCa with a false discovery rate (FDR) of less than 0.1 using the WEB‐based GEne SeT AnaLysis Toolkit (http://www.webgestalt.org/option.php ).19
9
Fractionation Experiment: GSEA and ORA
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For the fractionation experiment, a GSEA was performed ranking the identified proteins by the logarithmic transformation (base 2) of the iBAQ value and an ORA was done to compare the proteins identified by fractionated vs non-fractionated. Both analyses were done in the WEB-based GEne SeT AnaLysis Toolkit platform (WebGestalt; www.webgestalt.org) [46] . For identification of ECM proteins, the Human Matrisome Project [47] , [48] was searched. Venn Diagrams were generated with Biovenn [49] (link).
10
Quantitative Phosphoproteomics and Statistical Analysis
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