A detailed tutorial for circMine v2

Description: The Differential expression analysis is aimed to discover and identify all of significant circRNAs between different physiological and pathological conditions by using the method of limma. The differential analysis results are displayed in a table (Figure 7). The results of differential expression results can link to predict the circRNA-miRNA interaction, circRNA IRES, and ribo-circRNA location. A menu at the table header can be used to set columns and rows to show interesting information (Figure 7). The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table (Figure 7). Moreover, the circBase ID links to circBase for the detail information of the circRNA.

R package(s): The limma, dplyr, and jsonlite packages are used for the differential expression analysis, involving in the data processes and differential expression analysis.

• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;

Figure 7. The web interface of the Differential expression analysis.

Description: The ViolinPlot analysis function aims to draw a violin plot to show the differential expression of one or multiple circRNAs among different physiological and pathological conditions. The result displays as a violin plot (Figure 8), which can be freely downloaded and saved as a PDF file with high resolution. (na: p> 0.05; *: p<=0.05; **: p<=0.01; ***: p<=0.001; ****: p<=0.0001)

R package(s):The dplyr, EnvStats, reshape2, stringr and ggpubr packages are used for the ViolinPlot, involving in the data processes, differential expression analysis, and violin plot.

• circRNA: circBase ID(s);
• Method: a method for the differential expression analysis, including t.test, wilcox.test, anova, and kruskal.test; P-value format: a format of p-value for displaying.

Figure 8. The web interface of the ViolinPlot.

Description: The BoxplotUnfacet analysis function aims to draw a box plot without facet to show the differential expression of one or multiple circRNAs among different pathological or physiological conditions. The result displays as a box plot (Figure 9), which can be freely downloaded and saved as a PDF file with high resolution. (na: p>0.05; *: p<=0.05; **: p<=0.01; ***: p<=0.001; ****: p<=0.0001)

R package(s):The dplyr, reshape2, ggplot2, gridExtra and ggpubr packages are used for the unfacet boxplot, involving in the data processes, differential expression analysis, and boxplot.

• circRNA: circBase ID(s);
• Method: a method for the differential expression analysis, including t.test, wilcox.test, anova, and kruskal.test; P-value format: a format of p-value for displaying.

Figure 9. The web interface of the BoxplotUnfacet.

Description: The BoxplotFacet analysis function aims to draw a box plot with facet to show the differential expression of one or multiple circRNAs among different pathological or physiological conditions. The result displays as a box plot (Figure 10), which can be freely downloaded and saved as a PDF file with high resolution. (na: p>0.05; *: p<=0.05; **: p<=0.01; ***: p<=0.001; ****: p<=0.0001)

R package(s): The dplyr, reshape2, ggplot2, gridExtra and ggpubr packages are used for the facet boxplot, involving in the data processes, differential expression analysis, and boxplot.

• circRNA:circBase ID(s);
• Method: a method for the differential expression analysis, including t.test, wilcox.test, anova, and kruskal.test; P-value format: a format of p-value for displaying.

Figure 9. The web interface of the BoxplotFacet.

Description: The VolcanoPlot is a helpful analysis to depict the significant difference of one or more circRNAs among different physiological and pathological conditions. The result displays as a volcano plot (Figure 11), which can be freely downloaded and saved as a PDF file with high resolution.

R package(s): The dplyr, limma, and ggplot2 packages are used for the volcano plot, involving in the data processes, differential expression analysis, and volcano plot.

• circRNA: circBase ID(s);
• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA.

Figure 11. The web interface of the VolcanoPlot.

Description: The Heatmap aims to draw a heatmap plot for the interesting circRNAs. Multiple methods are provided to draw the heatmap plot. The result displays as a heatmap plot, which can be freely downloaded and saved as a PDF file with high resolution (Figure 12).

R package(s): The dplyr, jsonlite, pheatmap, and ggplot2 packages are used for the data processes and heatmap plot, respectively.

• circRNA(s): at least two circBase IDs;
• Method: a method for the heatmap plot, including ward.D, ward.D2, single, complete, average, mcquitty, media, and centroid.

Figure 12. The web interface of the Heatmap plot.

Description: The GO enrichment in the differential expression module aims to perform the Gene Ontology (GO) enrichment analysis on the significant differential circRNAs based on the annotations of their host genes in the GO resource. The results are displayed as a table and various figures. The plots can be freely download and saved as a PDF file with high resolution (Figure 13). A menu at the table header can be used to set columns and rows to show interesting information (Figure 13). The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table (Figure 13). Moreover, the GO ID links to the GO resource for the detail information of the GO term.

R package(s): The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, topGO, and Rgraphviz packages are used for the GO enrichment analysis, involving in the data processes, differential expression analysis, dot plot, and bar plot.

• Log2FC cutoff: a threshold value of log2FC (fold changes) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;
• Dysregulation type: including All (all dysregulation circRNA), Up (up-regulation circRNA), and Down (down-regulation circRNA);
• GO-p-value: a threshold p-value for the enriched GO term;
• GO-q-value: a threshold q-value for the enriched GO term;
• GO-type: including All (all GO terms), BC (biological processes GO terms), CC (cell components GO terms), and MF (molecular functions GO terms).

Figure 13. The web interface of the GO enrichment.

Description: The GO GSEA analysis function aims to build a Gene Set Enrichment Analysis (GSEA) to determine whether a set of significant circRNAs show statistically significant and concordant differences between different physiological and pathological condition, based on their host genes annotated in the Gene Ontology (GO) resource (Figure 14). The results are displayed as a table and various figures. The figures can be freely download and saved as a PDF file with high resolution. A menu at the table header can be used to set columns and rows to show the interesting information. The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table. Moreover, the GO ID links to the GO resource for the detail information of the GO term.

R package(s): The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, orgDbs, topGO, and Rgraphviz packages are used for the GO GSEA enrichment analysis, involving in the data processes, differential expression analysis and visualization.

• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;
• Dysregulation type:including All (all dysregulation gene feature ), Up (up-regulation gene feature), and Down (down-regulation gene feature);
• GO-p-value: a threshold p-value for the enriched GO term;
• GO-q-value: a threshold q-value for the enriched GO term;
• Number of terms: a number of the GO terms to show.

Figure 14. The web interface of the GO GSEA.

Description: The KEGG enrichment in the differential expression module is a helpful application to conduct the pathway analysis on the significant differential circRNAs based on the annotations of their host genes in the KEGG resource. The results are displayed as a table and various figures. The plots can be freely download and saved as a PDF file with high resolution (Figure 15). A menu at the table header can be used to set columns and rows to show interesting information (Figure 15). The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table (Figure 15). Moreover, the KEGG ID links to the KEGG resource for the detail information of the KEGG term.

R package(s): The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, Rgraphviz, and pathviewpackages were installed in the R project and are used for the KEGG enrichment analysis, involving in the data processes, differential expression analysis, dot plot, and bar plot.

• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;
• Dysregulation type:including All (all dysregulation circRNA), Up (up-regulation circRNA), and Down (down-regulation circRNA);
• KEGG-p-value: a threshold p-value for the enriched KEGG term;
• KEGG-q-value: a threshold q-value for the enriched KEGG term.

Figure 15. The web interface of the KEGG enrichment.

Description: The KEGG GSEA analysis function aims to build a Gene Set Enrichment Analysis (GSEA) to determine whether a set of significant circRNAs show statistically significant and concordant differences between different physiological and pathological condition, based on their host genes annotated in the KEGG resource (Figure 16). The results are displayed as a table and various figures. The figures can be freely download and saved as a PDF file with high resolution. A menu at the table header can be used to set columns and rows to show the interesting information. The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table. Moreover, the KEGG ID links to the KEGG resource for the detail information of the KEGG term.

R package(s): The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, orgDbs, topGO, and Rgraphviz packages are used for the KEGG GSEA enrichment analysis, involving in the data processes, differential expression analysis and visualization.

• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNAs;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;
• Dysregulation type:including All (all dysregulation gene feature ), Up (up-regulation gene feature), and Down (down-regulation gene feature);¬
• KEGG-p-value: a threshold p-value for the enriched KEGG term;
• KEGG-q-value: a threshold q-value for the enriched KEGG term.
• Number of terms: a number of the KEGG terms to show.

Figure 16. The web interface of the KEGG GSEA.

Description: The GO TreePlot analysis function aims to perform the Gene Ontology (GO) enrichment analysis on the significant differential circRNAs based on the annotations of their host genes in the GO resource. The results are displayed as a table and tree plot. The tree plot can be freely download and saved as a PDF file with high resolution (Figure 17). A menu at the table header can be used to set columns and rows to show the interesting information. The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table. Moreover, the GO ID links to the GO resource for the detail information of the GO term.

R package(s): The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, orgDbs, topGO, and Rgraphviz packages are used for the GO enrichment analysis, involving in the data processes, differential expression analysis and visualization.

• Log2FC cutoff: a threshold value of log2FC (two-folds change) to define the significant differential circRNA;
• p-value cutoff: a threshold p-value to define the significant differential circRNA;
• Dysregulation type:including All (all dysregulation gene feature ), Up (up-regulation gene feature), and Down (down-regulation gene feature);
• GO-p-value: a threshold p-value for the enriched GO term;
• GO-q-value: a threshold q-value for the enriched GO term.
• Number of terms: a number of the GO terms to show.

Figure 17. The web interface of the GO Treeplot.

Description: Linear graph in the Co-expression module is a helpful application to perform correlation analysis of two circRNAs in the specific physiological and pathological condition by three methods, including pearson, kendall, and spearman. The result displays as a linear graph, which can be freely download and saved as a PDF file with high resolution (Figure 20).

R package(s):The dplyr package is used for the data processes. The ggscatter function in the ggpubr package is used for the co-expression analysis of two circRNAs and drawing a linear graph.

• circRNA1: a circBase ID;
• circRNA2: a circBase ID;
• Method: including pearson, kendall, and spearman;

Figure 20. The web interface of the Linear graph.

Description: Boxplot in the Co-expression module is a helpful application to perform a paired correlation analysis of two circRNAs in the specific physiological and pathological condition by setting methods (including t.test and wilcox.test). The result displays as a box plot, which can be freely download and saved as a PDF file with high resolution (Figure 21).

R packages:The dplyr package is used for the data processes. The ggscatter function in the ggpubr package is used for the co-expression analysis of two circRNAs and drawing a box plot.

• circRNA1: a circBase ID;
• circRNA2: a circBase ID;
• Method: including t.test and wilcox.test;

Figure 21. The web interface of the Boxplot in the Co-expression module.

Description: Corrplot in the Co-expression module is a helpful application to depict the correlationships among multiple circRNAs in the specific physiological and pathological condition. The result displays as various visualizations by setting the method parameter, which can be freely downloaded and saved as a PDF file with high resolution (Figure 22). ( *: p<=0.05; **: p<=0.01; ***: p<=0.001)

R package(s): The dplyr package is used for the data processes. The cor.test function is used for the co-expression analysis. The ggplot2 and corrplot packages are used for the corrplot.

• circRNA(s): at least three circBase IDs;
• Method: including circle, square, ellipse, number, color, pie, and shade.

Figure 22. The web interface of the Corrplot.

Description: CircosPlot in the co-expression module is a helpful application to depict the correlationships among multiple circRNAs in the specific physiological and pathological condition. The result displays as a circos plot, which can be freely downloaded and saved as a PDF file with high resolution (Figure 23).

R package(s): CircosPlot in the Co-expression module is a helpful application to depict the correlationships among multiple circRNAs in the specific physiological and pathological condition.

• circRNA(s): at least three circBase IDs;
• Method: including pearson, kendall, and spearman.

Figure 23. The web interface of the CircosPlot.

Description: The NetworkPlot analysis function aims to depict the correlationships among multiple circRNAs in the specific physiological and pathological condition. The result displays as a network plot, which can be freely downloaded and saved as a PDF file with high resolution (Figure 24).

R package(s): The dplyr, stringr and reshape2 package is used for the data processes. The cor.test function is used for the co-expression analysis. The igraph packages are used for the NetworkPlots.

• circRNA(s): at least three circBase IDs;
• Correlation cutoff: : a threshold value for the significant correlation;
• Method: including pearson, kendall, and spearman.

Figure 24. The web interface of the NetworkPlot.

Description: GO enrichment in the Co-expression module is a helpful application to identify the biological functions of circRNA based on the GO annotations of the host genes of its co-expression circRNAs in the specific physiological and pathological condition. The results are displayed as a table, a bar plot, and a dot plot. The bar plot and dot plot can be freely downloaded and saved as a PDF file with high resolution (Figure 25). A menu at the table header can be used to set columns and rows to show interesting information. The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table. Moreover, the GO ID links to the GO resource for the detail information of the GO term.

R package(s): The cor.test function is used for the co-expression analysis. The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, topGO, and Rgraphviz packages are used for the GO enrichment analysis, involving in the data processes, dot plot, and bar plot.

• circRNA: a circBase ID;
• Correlation cutoff: a threshold value to define the significant correlation;
• Correlation p-value cutoff: a threshold p-value to define the significant correlation;
• GO-p-value: a threshold p-value for the enriched GO term;
• GO-q-value: a threshold q-value for the enriched GO term;
• GO-type: including All (all GO terms), BC (biological processes GO terms), CC (cell components GO terms), and MF (molecular functions GO terms);
• Method: including pearson, kendall, and spearman;

Figure 25. The web interface of the GO enrichment in the Co-expression module.

Description: KEGG enrichment in the Co-expression module is a helpful application to identify the biological pathways of circRNAs based on the KEGG pathway annotations of the host genes of its co-expression circRNAs in the specific physiological and pathological condition. The results are displayed as a table, a bar plot, and a dot plot, The bar plot and dot plot can be freely downloaded and saved as a PDF file with high resolution (Figure 26). A menu at the table header can be used to set columns and rows to show interesting information. The filter box allows to quick search. In addition, right clicking on the table body displays an instruction form to copy and export the table. Moreover, the KEGG ID links to the KEGG resource for the detail information of the KEGG term.

R package(s): The cor.test function is used for the co-expression analysis. The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, Rgraphviz, and pathviewpackages are used for the KEGG enrichment analysis, including the data processes, dot plot, and bar plot.

• circRNA: a circBase ID;
• Correlation cutoff: a threshold value to define the significant correlation;
• Correlation p-value cutoff: a threshold p-value to define the significant correlation;
• KEGG-p-value: a threshold p-value for the enriched KEGG term;
• KEGG-q-value: a threshold q-value for the enriched KEGG term;
• Method: including pearson, kendall, and spearman;

Figure 26. The web interface of the KEGG enrichment in the Co-expression module.

Description: The ROC (Receiver Operating Characteristic) analysis function has designed to plot ROC curves to estimate the predictive performance of interesting circRNA(s) for phenotype. The result displays as graphs, which can be freely downloaded and saved as a PDF file with high resolution (Figure 30).

R package(s):The tidyverse package is used for the data processes. The ROCR, plotROC, ggpubr, and randomcoloR package is used for the ROC analysis.

• Input circRNA(s): enter one or more circBase IDs.

Figure 30. The web interface of the ROC.

Description: The Correlation aims to perform correlation analysis and draw a linear graph for an interesting circRNA and an interesting mRNA/lncRNA gene or miRNA in the specific physiological and pathological condition by three methods, including pearson, kendall, and spearman (Figure 40).

R package(s):The dplyr package is used for the data processes. The ggscatter function in the ggpubr package is used for the co-expression analysis of a circRNA and a mRNA/lncRNA gene/miRNA, and and drawing a linear graph.

• Input 1: a circBase ID;
• Input 2 type: mRNA/lncRNA gene or miRNA (if data sets contain miRNA transcriptomic data) ;
• Input 2: a symbol of mRNA/lncRNA gene or miRNA;
• Method: including pearson, kendall, and spearman.

Figure 40. The web interface of the Correlation.

Description: The BoxPlot aims to perform correlation analysis and draw a linear graph for an interesting circRNA and an interesting mRNA/lncRNA gene or miRNA in the specific physiological and pathological condition by three methods, including pearson, kendall, and spearman (Figure 41).

R packages:The dplyr package is used for the data processes. The ggscatter function in the ggpubr package is used for the co-expression analysis of two circRNAs and drawing a box plot.

• Input 1: a circBase ID;
• Input 2 type: mRNA/lncRNA gene or miRNA (if data sets contain miRNA transcriptomic data) ;
• Input 2: a symbol of mRNA/lncRNA gene or miRNA;
• Method: including pearson, kendall, and spearman.

Figure 41. The web interface of the BoxPlot.

Description: The CorrPlot in the co-expression module aims to to depict the correlationships among several interesting circRNA(s), mRNA/lncRNA genes and miRNAs in the specific physiological and pathological condition by two methods (including t.test and wilcox.test), respectively. The analysis result visualizes as a boxplot (Figure 42).

R package(s): The dplyr package is used for the data processes. The cor.test function is used for the co-expression analysis. The ggplot2 and corrplot packages are used for the corrplot.

• Input circRNA(s),mRNA/lncRNA genes, or miRNAs: at least three IDs or symbols;
• Method: including circle, square, ellipse, number, color, pie, and shade.

Figure 42. The web interface of the CorrPlot.

Description: The CircosPlot analysis function aims to depict the correlationships among several interesting circRNA(s), mRNA/lncRNA genes and miRNAs in the specific physiological and pathological condition. The result displays as a circos plot, which can be freely downloaded and saved as a PDF file with high resolution (Figure 43).

R package(s): The dplyr package is used for the data processes. The cor.test function is used for the co-expression analysis. The ggplot2, corrplot and circlize packages are used for the circos plot.

• Input circRNA (s), mRNA/lncRNA genes, or miRNAs: at least three IDs or symbols;
• Method: including pearson, kendall, and spearman.

Figure 43. The web interface of the CircosPlot.

Description: The NetworkPlot analysis function aims to analysis function aims to depict the correlationships among several interesting circRNA(s), mRNA/lncRNA genes and miRNAs in the specific physiological and pathological condition. The result displays as a network plot, which can be freely downloaded and saved as a PDF file with high resolution (Figure 44).

R package(s): The dplyr, stringr and reshape2 package is used for the data processes. The cor.test function is used for the co-expression analysis. The igraph packages are used for the network plots.

• circRNA(s): at least three circBase IDs;
• Correlation cutoff: : a threshold value for the significant correlation;
• Method: including pearson, kendall, and spearman.

Figure 44. The web interface of the NetworkPlot.

Description: The GO in the co-expression module is a helpful application to discover and identify the biological functions of an interesting circRNA in the specific physiological and pathological condition based on the annotations of its-correlated mRNA/lncRNA genes, and miRNAs in the Gene Ontology (GO) resource. The parameters of correlation cutoff and correlation p-value cutoff are designed to define a threshold for the significant correlation, while the parameters of GO-p-value cutoff and GO-q-value cutoff are aimed to define a threshold for the enriched GO term. In addition, GO enrichment provides three methods to respectively conduct the co-expression analyses, including pearson, kendall, and spearman. Moreover, the GO-type parameter has designed to perform various GO enrichment analyses on all of GO terms (All), biological process GO terms (BP), cell components GO terms (CC), or molecular functions GO terms (MF), respectively (Figure 45).

R package(s): The cor.test function is used for the co-expression analysis. The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, topGO, and Rgraphviz packages are used for the GO enrichment analysis, involving in the data processes, dot plot, and bar plot.

• circRNA: a circBase ID;
• Correlation cutoff: a threshold value to define the significant correlation;
• Correlation p-value cutoff: a threshold p-value to define the significant correlation;
• GO-p-value: a threshold p-value for the enriched GO term;
• GO-q-value: a threshold q-value for the enriched GO term;
• GO-type: including All (all GO terms), BC (biological processes GO terms), CC (cell components GO terms), and MF (molecular functions GO terms);
• Method: including pearson, kendall, and spearman.

Figure 45. The web interface of the GO.

Description: The KEGG in the co-expression module has designed to discover and identify the biological pathways of an interesting circRNA in the specific physiological and pathological condition based on the pathway annotations of its-correlated mRNA/lncRNA genes, and miRNAs in the KEGG resource. The KEGG enrichment application provides the parameters of correlation cutoff, correlation p-value cutoff, KEGG-p-value cutoff, and KEGG-q-value cutoff to define a threshold for the significant co-expression circRNA and the enriched KEGG term, respectively. In addition, the KEGG enrichment application offers three methods (including pearson, kendall, and spearman) for custom setting to perform co-expression analyses, respectively (Figure 46).

R package(s): The cor.test function is used for the co-expression analysis. The dplyr, jsonlite, stringr, limma, ggplot2, clusterProfiler, org.Hs.eg.db, Rgraphviz, and pathviewpackages are used for the KEGG enrichment analysis, including the data processes, dot plot, and bar plot.

• circRNA: a circBase ID;
• Correlation cutoff: a threshold value to define the significant correlation;
• Correlation p-value cutoff: a threshold p-value to define the significant correlation;
• KEGG-p-value: a threshold p-value for the enriched KEGG term;
• KEGG-q-value: a threshold q-value for the enriched KEGG term;
• Method: including pearson, kendall, and spearman.

Figure 46. The web interface of the KEGG.

Description: The Survival analysis function aims to perform a survival analysis based on the selected TCGA data set with outputing a survival plot according to the interesting circRNA-associated genomic variant or a circRNA co-located with genomic variant(s) (Figure 49).

R package(s):The dplyr, stringr, jsonlite and limma packages are used for the data processes. The survival and survminer packages are used for the survival analysis and plot.

• Please select variant site or circRNA: Select a input type;
• Please input a variant site or circBase ID: Input circBase ID with hg38 position, the reference and the altered nucleotide when select “Variant sites”; input circBase ID when select “circRNA”. The circRNAs and sites are limited in the range of the collected circRNAs and their associated genomic variants here.

Figure 49. The web interface of the Survival.

Description: The StackPlot analysis function aims to show the distributions of a circRNA-associated genomic variant site or a circRNA co-located with genomic variants between or among different groups, which were defined by the user. (Figure 50).

R packages:The dplyr, stringr, jsonlite and limma packages are used for the data processes. The ggplot2 and patchwork packages are used for the stack plot.

• Please select circRNA-associated genomic variant or circRNA co-located with genomic variants: Select a input type;
• Please input a variant site or circBase ID: Input circBase ID with hg38 position, the reference and the altered nucleotide when select “Variant sites”; input circBase ID when select “circRNA”.

Figure 50. The web interface of the StackPlot.

Description: The Rcircos analysis function aims to show the chromosomal locations of the top/custom circRNAs involving genomic variants along the human genome (hg38) in each users-defined group and the shared circRNA-associated genomic variant site(s) in all of users-defined groups. Tips: it is recommended that the number of the inputs does not exceed 20, and that two groups are used (Figure 51).

R package(s): The dplyr, stringr, tidyr, jsonlite and limma packages are used for the data processes. The circlize, ComplexHeatmap and GenomicRanges packages are used for the circos plot of the hg38.

• Please select an overview type: Select top variants or certain variant sites or circRNAs for visualization. The circRNAs and sites are limited in the range of the collected circRNAs and their associated genomic variants here;
• Please input the number of top variants or variant site(s) or circBase ID(s): Input number when select “Top N variants”; input circBase ID with hg38 position, the reference and the mutated nucleotide when select “Variant sites”; input circBase ID when select “circRNA”.

Figure 51. The web interface of the Rcircos.