Between-group correlation was analyzed using the OmicShare cloud platform (https://www.omicshare.com/ ). DEmiRNAs and DElncRNAs which are significantly negatively and positively associated with the DEmRNAs (p < 0.05) were screened out, respectively. miRNAs having binding sites on the 3’UTR of the core mRNAs were predicted using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/ ) [34 (link), 35 (link)], and then intersected with the DEmiRNAs of significantly negative correlation. Similarly, miRNAs having binding sites on the DElncRNAs of significantly positive correlation with the key mRNAs were predicted using the miRDB database (http://mirdb.org/custom.html ) [36 (link)], and then intersected with the overlapped miRNAs. The final mRNAs, miRNAs and lncRNAs screened out were projected on the OmicShare cloud platform to establish a ceRNA regulatory network.
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13 protocols using cloud platform
1
Constructing ceRNA Regulatory Network
2
Metabolomics of Microbial Samples
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All the experiments were repeated three times and data were analyzed by ANOVA with SPSS Statistics 20.0 and expressed as mean ± standard deviation (n = 3), (P < 0.05). Linear retention indices (RI) and mass spectra of GC × GC-ToF-MS and GC-MS data were identified by comparison with the NIST database. The Omicshare Cloud platform (https://www.omicshare.com/tools/ ), the Microbiology Letter platform (http://www.bioinformatics.com.cn/ ), and the Metware Cloud platform (https://cloud.metware.cn ) were used for differential substance related data analysis.
3
Elucidating FN's Anti-CRC Mechanisms
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The Database for Annotation, Visualization and Integrated Discovery (DAVID) database was used to obtain biological functions and molecular pathways of core targets in FN against CRC before being visualized through the Omicshare cloud platform. Advanced bubble diagrams of signaling pathways and biological processes were plotted according to P values. The Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses of all identifiable targets were performed to further reveal detailed pharmacological mechanisms of FN in the treatment of CRC.
4
Analyzing DEGs in Rat Cerebral Ischemia
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In order to understand the biological functions and pathways of DEGs in rat cerebral ischemia after PN intervention, we used an annotation and visualization integrated OmicShare cloud platform to analyze and visualize the biological functions and pathways of differential genes.
5
KEGG Pathway Enrichment Analysis Using DAVID
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The Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources (DAVID, https://david.ncifcrf.gov/ , version 6.8) [46 ], a web-based online bioinformatics resource for the high-throughput functional annotation bioinformatics functional annotation and enrichment analysis, was conducted to perform for Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp/ ) pathway enrichment, with p value < 0.05 was considered statistically significant [47 (link)]. The visualization of enrichment analysis was illuminated through Omicshare cloud platform (http://www.omicshare.com/ ).
6
Bioinformatic Analysis of Biological Targets
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The Database for Annotation, Visualization, and Integrated Discovery (DAVID; https://david.ncifcrf.gov/ ) was employed to perform GO function enrichment analysis and KEGG pathway enrichment analysis of core targets. (Huang da et al., 2009 (link)). Results were visualized using the Omicshare cloud platform (http://www.omicshare.com/ ).
7
Predicting miRNA-148a-5p Target Genes
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The microRNA Target Prediction and Functional Study Database (miRDB; http://www.mirdb.org/miRDB/index.html ) and the target gene prediction databases TargetScan7.1 (http://www.targetscan.org/ ) were used to predict target genes of the seven differentially expressed miRNAs screened in our previous experiment (Li Z. et al., 2017 (link)). The target genes were then enriched by defining the intersection through the DAVID biological information network for NF-κB signal pathway enrichment and through the Omicshare cloud platform (https://www.omicshare.com/ ) and GO analysis of the intersection target genes. Then, using miRbase (http://www.mirbase.org/ ) and TargetScan to query the complementary binding sites of gga-miR-148a-5p and target genes, it was predicted that PDPK1 was the target gene of gga-miR-148a-5p.
8
Transcription Factor Network Prediction
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Transcription factor (TFs) network prediction was performed online at the threshold parameter p-value ≤ 1e-5 on the Plant Transcriptional Regulatory Map (PTRM) website (http://plantregmap.gao-lab.org/regulation_prediction.php ), using all the CcGASA sequences as an input. The Cytoscape 3.8 software was used to visualize the transcription factor regulatory network [27 (link)]. The predicted TFs were subjected to GO analyses on the Omicshare cloud platform (https://www.omicshare.com/tools/ ). The functional interacting network models of CcGASA proteins were predicted using the web program STRING 11.0 (http://string-db.org ). The confidence parameter was set at a threshold of 0.40, and for other parameters the default values were used.
9
Quantitative Proteomics Pipeline
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The MS raw data for each sample were searched using the MASCOT engine (Matrix Science, London, UK; Version 2.2) embedded into Proteome Discoverer 1.4 software for identification and quantitation analysis. The volcano plot and UpSet graph were drawn with the help of the Omicstudio cloud platform (https://www.omicstudio.cn/tool/43 ). The GO was plotted by the OmicShare cloud platform (https://www.omicshare.com/ ). KEGG and PPI analyses were performed using OmicsBean (http://www.omicsbean.cn/dashboard/ ).
10
Screening Differential Gene Expression
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Gene screening was carried out on the GSE126848 and GSE147507 datasets according to adj.P ≤ .05 and ∣ LogFC∣≥1. Using the screening conditions, 2 groups of effective DEGs were identified, and the online Omicshare cloud platform was used for data visualization and the construction of volcano and cluster analysis plots. Valid DEGs from the 2 datasets were stored in the online platform, jvenn, and the intersection of the genes was used to obtain common DEGs, defined as GS1.
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