Function Reference (Supplementary Table)
This page lists every function/output currently available in OmicsBridge (v1.3.2), grouped by section and sub-section, with its output type, purpose, underlying method, and any usage notes. It was prepared as a supplementary table in response to reviewer feedback. Rows are colour-coded by Section.
Download the original table (.xlsx) →
| # | Section | Sub-section | Function Name | Output Type | Output Detail | Example Purpose | Method | Note |
|---|---|---|---|---|---|---|---|---|
| 1 | Database and Data Upload | Database | Browse Database (Browse/Edit/Delete) | - | Editable table of all datasets | Browse, filter, edit, delete dataset entries | No special library. Built-in R functions read and save the table; the DT library displays it as an editable table. | Deleting a row also deletes its file from disk. |
| 2 | Database and Data Upload | Data upload | Upload New Dataset | - | New dataset registered + file saved | Upload a new dataset with metadata | No special library. Built-in R functions read and check the uploaded file before saving it to the registry. | Gene column must be "id"; count samples need Group_RepX naming. |
| 3 | Count & Comparison Data Overview | Overview and Analysis > Data Table | Show the Data Table | Table | The dataset's table/excel sheet. Optionally normalised (CPM/TPM/FPKM); interactive DT table | View raw or normalised expression values for a Count Table dataset before further analysis | No special library for CPM. TPM/FPKM use a bespoke gene-length lookup (GENCODE v41 annotation) joined by human gene symbol. | TPM/FPKM only support human genes currently (gene length keyed by human gene symbol); genes without a match are skipped and reported. CPM works for any species. |
| 4 | Count & Comparison Data Overview | Overview and Analysis > Plot & Downstream Analysis | Main Plot | Scatter plot | Interactive scatter/volcano plot + companion bar plot; downloadable tables of highlighted or filtered genes | Visualise and highlight significant or gene-set-matched hits in a comparison dataset (e.g. DEGs, CRISPR screen hits) | Uses ggplot2 to draw the plot; the highlighting/filtering logic is custom R code, no special library. | Only used with "Comparison data". Genes can be highlighted manually, by threshold filter, from MSigDB HALLMARK/custom gmt pathways, or from custom gene sets. |
| 5 | Count & Comparison Data Overview | Overview and Analysis > Plot & Downstream Analysis | Main Plot | Bar plot | Bar chart of the currently highlighted/filtered genes | Compare and rank highlighted genes' exact scores more precisely than reading them off the scatter plot | Uses ggplot2 to draw the plot; no other special library. | Shares the same highlight source as the Scatter Plot tab; updates automatically when the highlighted genes change. |
| 6 | Count & Comparison Data Overview | Overview and Analysis > Swarm plot | Swarm plot | Swarm plot | Swarm plot of selected genes' expression across samples/replicate groups; companion "Expression scores" table (downloadable) | Compare expression levels of genes of interest across replicate groups | Uses ggplot2 (with ggbeeswarm) to draw the plot; no other special library. | Can reorder/select groups, exclude specific samples, switch to log2 scale, and choose a colour palette or a single flat colour. |
| 7 | Count & Comparison Data Overview | Overview and Analysis > Gene correlation | Pair-wise gene expression correlation | Cluster heatmap | Clustered heatmap of pairwise correlations across all input genes | See at a glance which of several genes of interest correlate with each other | Correlation computed with Hmisc::rcorr (Pearson/Spearman); heatmap drawn with ggplot2, genes ordered by hierarchical clustering. scatter plot drawn with ggplot2. | Shown by default, until a specific gene pair is selected in the correlation table. |
| 8 | Count & Comparison Data Overview | Overview and Analysis > Gene correlation | Two genes correlation investigation | Scatter plot | Scatter plot of expression values for one selected gene pair, with optional regression line | Inspect the relationship between two specific genes in detail | Points can be coloured by sample group or a single flat colour. | |
| 9 | Count & Comparison Data Overview | Overview and Analysis > Heatmap | Expression Pattern Analysis and Cluster Heatmap | Cluster heatmap | Standardised (z-score) expression heatmap of selected genes across samples, with gene clustering; downloadable "Expression scores" table | Visualise expression patterns of a gene set across samples/conditions at a glance | ggplot2 draws the heatmap; base R hierarchical clustering groups the genes. | Dragging rows in the sample table reorders the heatmap's Y-axis sample order. Selecting a cluster number shows which genes fall into it. |
| 10 | Count & Comparison Data Overview | Overview and Analysis > PCA plot | PCA plot | PCA/tSNE/UMAP plot | 2D scatter plot of samples (PCA, t-SNE, or UMAP), coloured by group; selecting points lists their sample names | See how samples cluster or separate overall, e.g. by treatment or replicate group | PCA via base R prcomp; t-SNE via Rtsne; UMAP via the umap package (user-selectable). | "Hide labels" and "Colour by groups" toggle point styling; brush-select points on the plot to list their sample names below it. |
| 11 | Count & Comparison Data Overview | Downstream analysis > ORA: GO/KEGG analysis | ORA analysis | Result table | Full ORA results table (terms, gene counts, p-values); downloadable | Get the complete list of enriched GO/KEGG terms for inspection or export | clusterProfiler runs the enrichment (enrichGO/enrichKEGG); results shown as a plain table. ggplot2 draws a custom bar and bubble plot from the results. ggraph draws a custom network layout from the results. | Gene symbols only; ENSEMBL IDs not supported. |
| 12 | Count & Comparison Data Overview | Downstream analysis > ORA: GO/KEGG analysis | ORA analysis | Bar plot | Bar plot of enriched terms, showing gene count and p-value | Quickly compare top enriched terms by significance and gene count | clusterProfiler runs the enrichment (enrichGO/enrichKEGG); results shown as a plain table. ggplot2 draws a custom bar and bubble plot from the results. ggraph draws a custom network layout from the results. | Number of terms shown, font sizes, colours, and background are adjustable. |
| 13 | Count & Comparison Data Overview | Downstream analysis > ORA: GO/KEGG analysis | ORA analysis | Bubble plot | Bubble plot of enriched terms, showing gene ratio per term | Alternative view emphasising the proportion of detected genes per term, not just p-value | clusterProfiler runs the enrichment (enrichGO/enrichKEGG); results shown as a plain table. ggplot2 draws a custom bar and bubble plot from the results. ggraph draws a custom network layout from the results. | Number of terms shown, font sizes, colours, and background are adjustable. |
| 14 | Count & Comparison Data Overview | Downstream analysis > ORA: GO/KEGG analysis | ORA analysis | Network plot | Network diagram linking top enriched terms to their detected genes | See which genes drive multiple enriched terms at once | clusterProfiler runs the enrichment (enrichGO/enrichKEGG); results shown as a plain table. ggplot2 draws a custom bar and bubble plot from the results. ggraph draws a custom network layout from the results. | Only the top terms are shown; may need a wider figure to render without errors. |
| 15 | Count & Comparison Data Overview | Downstream analysis > GSEA analysis | GSEA analysis | Result table | GSEA results table (pathway, p-value, adjusted p-value, ES, NES, gene set size); downloadable | Get the full list of enriched pathways with statistics for export | fgsea runs the enrichment on a ranked gene list. fgsea results plotted with ggplot2. | Selecting a non-numeric ranking column causes an error. |
| 16 | Count & Comparison Data Overview | Downstream analysis > GSEA analysis | GSEA analysis | Line plot | GSEA enrichment plot (running score line) for one selected pathway | Visually inspect the enrichment profile of a pathway of interest | fgsea runs the enrichment on a ranked gene list. fgsea results plotted with ggplot2. | Line colours and font sizes are adjustable. |
| 17 | Count & Comparison Data Overview | Downstream analysis > TF activity inference | TF activity inference | Bar plot | Bar plot of TF activity scores (positive = activated in treatment vs control) | See which transcription factors are most activated/repressed between two conditions | decoupleR's run_ulm estimates TF activity from a DESeq2 'stat' column; ggplot2 draws the bar plot. | Only works with DESeq2-processed RNA-seq comparison data. |
| 18 | Count & Comparison Data Overview | Downstream analysis > TF activity inference | TF activity inference | Result table | Table of TFs with their activity scores and statistics; downloadable | Export the full list of inferred TF activities for further analysis | decoupleR's run_ulm estimates TF activity from a DESeq2 'stat' column; ggplot2 draws the bar plot. | Only works with DESeq2-processed RNA-seq comparison data. |
| 19 | Compare Across Datasets | Analysis > Get the overlap | Cross-dataset overlap investigation | Bar plot | Table of how often each gene ranks in the top/bottom X% across datasets, plus a per-gene bar plot | Find genes that consistently show up as top/bottom hits across many experiments | No special library. Custom R ranking/counting logic; ggplot2 draws the bar/scatter plot. | Manual colour-range and Y-axis-range overrides available, in addition to auto-scaling. |
| 20 | Compare Across Datasets | Analysis > Compare one gene | Cross-dataset gene investigation | Bar plot / Scatter plot | Bar plot or scatter plot comparing one score across datasets for chosen genes, plus the underlying data table | See whether a specific gene's effect size/direction is consistent or experiment-specific across datasets | No special library. Custom R ranking/counting logic; ggplot2 draws the bar/scatter plot. | Switch between Bar plot and Scatter plot via "Plot type"; manual colour-range and Y-axis-range overrides available. |
| 21 | Integrate Two Datasets | Side by Side comparison | Side by Side comparison | side-by-side Scatter plot | Two side-by-side scatter plots (Data1, Data2), with genes selected on one mapped/highlighted on the other; downloadable overlap-genes table | See how genes of interest identified in one dataset behave in a second, comparable dataset | No special library. Custom R gene-mapping logic; ggplot2 draws the plots. | Gene-name column must be "id" in both datasets for merging. Genes absent from the other dataset won't highlight. |
| 22 | Integrate Two Datasets | Integration Plot | Integration Plot | Scatter plot | Merged scatter plot (Data1 vs Data2 scores, optional 3rd colour variable); downloadable filtered/selected gene tables | Directly compare a score (e.g. LFC) between two datasets in one plot, e.g. to assess concordance | No special library. ggplot2 draws the plot. | Optional y=x / y=-x reference lines. Gene-set filter supports HALLMARK (human/mouse) and custom (uploaded gmt or registered gene sets). |
| 23 | Clinical Data Analysis | Data View | Data View and metadata edit | Table | Cohort metadata summary + 4 raw data tables (gene expression, survival, meta data, mutation); metadata is directly editable | Pick a patient cohort and inspect its underlying data before analysis | No special library. Built-in R read/filter; DT for the tables. | Gene expression table shows only the first 1000 rows by default. Metadata columns can be added/deleted in-app. |
| 24 | Clinical Data Analysis | Survival analysis > Kaplan–Meier curve | Survival analysis | Kaplan–Meier curve | Kaplan-Meier survival curve comparing user-defined high/low expression groups, with p-value and hazard ratio | Test whether a gene's expression level is associated with patient survival | survival/survminer packages (Surv, survfit, ggsurvplot); Cox model for hazard ratio. | Groups can be split by median, top/bottom 25%, custom %, or manually by sample name. |
| 25 | Clinical Data Analysis | Survival analysis > Expression distribution | Gene expression distribution check | Histogram | Distribution of the selected gene's expression across the cohort | Sanity-check the expression distribution before choosing a group-split threshold | No special library. ggplot2 draws the histogram. | Helps decide an appropriate splitting criterion. |
| 26 | Clinical Data Analysis | Gene correlation | Pair-wise gene expression correlation | Cluster heatmap | Clustered heatmap of pairwise correlations across all input genes in the cohort | See at a glance which of several genes of interest correlate with each other in patients | Correlation via Hmisc::rcorr (Pearson/Spearman); heatmap via ggplot2, hierarchical clustering. scatter plot via ggplot2. | Shown by default until a specific gene pair is selected. |
| 27 | Clinical Data Analysis | Gene correlation | Two genes correlation investigation | Scatter plot | Scatter plot of expression values for one selected gene pair, with optional regression line | Inspect the relationship between two specific genes in detail | Correlation via Hmisc::rcorr (Pearson/Spearman); heatmap via ggplot2, hierarchical clustering. scatter plot via ggplot2. | Same multi-gene pairwise pattern as Count & Comparison Data Overview's Gene correlation. |
| 28 | Clinical Data Analysis | Mutation analysis > Frequency Plot | Mutation frequency analysis | Bar plot | Frequency Plot: bar plot of mutation count/percentage per gene across the cohort | See which genes are most frequently mutated in a cohort | No special library. Base R mutation frequency tally; ggplot2 draws the bar plot. | Number of genes shown is adjustable. |
| 29 | Clinical Data Analysis | Mutation analysis > Survival Analysis | Mutation survival analysis | Kaplan–Meier curve | Kaplan-Meier curve comparing wild-type vs. mutant patients for a selected gene | Test whether mutation status of a gene is associated with survival | survival/survminer packages. | Part of the same Mutation analysis feature, in its own results tab. |
| 30 | Clinical Data Analysis | Mutation analysis > Gene expression compare | Mutation and gene expression investigation | Box/Violin/Swarm plot | Expression of a chosen gene compared between wild-type and mutant patients | See whether a mutation is associated with expression changes in another gene | No special library. ggplot2/ggbeeswarm draw the plot. | Plot type selectable: Box, Violin, Swarm, or Violin+Swarm. |
| 31 | Clinical Data Analysis | Gene expression across subtypes | Gene expression across subtypes | Box/Violin/Swarm plot | Gene expression compared across patient metadata subgroups, with statistical test results | Compare a gene's expression across clinical subtypes (e.g. tumour stage, treatment group) | No special library. Wilcoxon (2 groups) or Kruskal-Wallis (3+ groups) test; ggplot2/ggbeeswarm for plotting. | Can restrict to exactly two subtypes. Plot type selectable: Box, Violin, Swarm, or Violin+Swarm. |
| 32 | Clinical Data Analysis | Signature analysis > Survival Analysis | Signature survival analysis | Kaplan–Meier curve | Kaplan-Meier curve comparing high/low signature-score patients | Test whether a gene set's activity score is associated with survival | GSVA or ssGSEA (GSVA package) for scoring; survival/survminer for the curve. ggplot2 draws the plot and the histogram. | Groups split by median, top/bottom 25%, or custom %. |
| 33 | Clinical Data Analysis | Signature analysis > Score Comparison | Signature score across subtypes | Box/Violin/Swarm plot | Signature score compared across a chosen metadata subgroup | Compare pathway/signature activity across clinical subtypes | GSVA or ssGSEA (GSVA package) for scoring; survival/survminer for the curve. ggplot2 draws the plot and the histogram. | Can restrict to exactly two subtypes. |
| 34 | Clinical Data Analysis | Signature analysis > Distirbution | Signature score distribution | Histogram | Distribution of signature scores across the cohort | Sanity-check the score distribution before choosing a group-split threshold | GSVA or ssGSEA (GSVA package) for scoring; survival/survminer for the curve. ggplot2 draws the plot and the histogram. | Helps decide an appropriate splitting criterion. |
| 35 | Clinical Data Analysis | Deconvolution | Run deconvolution | Table | Table of estimated immune/stromal cell-type abundance scores per sample | Estimate immune cell composition from bulk RNA-seq for a cohort | MCPcounter or xCell packages (user-selectable). | Results feed the Heatmap/Barplot, Correlation with genes, and Association with mutations features below. |
| 36 | Clinical Data Analysis | Deconvolution > Heatmap/Barplot | Deconvolution Heatmap | Cluster heatmap | Heatmap of cell-type abundance across selected samples | Visualise immune composition patterns across samples/subtypes at a glance | No special library. ggplot2 draws the heatmap. | |
| 37 | Clinical Data Analysis | Deconvolution > Heatmap/Barplot | Deconvolution stacked bar plot | Bar plot | Bar plot of cell-type abundance across selected samples | Compare immune cell-type proportions between samples | No special library. ggplot2 draws the heatmap. | "Percentile plot" option switches to percentile-scaled values. |
| 38 | Clinical Data Analysis | Deconvolution > Correlation with genes | Correlation with genes | Scatter plot | Scatter plot + correlation table relating gene expression to one cell type's abundance | Test whether a gene's expression tracks with a specific immune cell type's abundance | Correlation via base R cor.test (Pearson/Spearman); ggplot2 for the scatter plot. | Table downloadable. |
| 39 | Clinical Data Analysis | Deconvolution > Association with mutations | Association with mutations | Box/Violin/Swarm plot | Statistical test table + plot comparing one cell type's abundance between mutant and wild-type patients for each gene | See whether mutations in a gene are associated with the abundance of a specific immune cell type | No special library. Wilcoxon rank-sum test (base R); ggplot2/ggbeeswarm for plotting. | Plot type selectable: Box, Violin, Swarm, or Violin+Swarm. Mutant samples can be split by amino acid change (swarm plot). Table downloadable. |
| 40 | Clinical Data Analysis | Compare cohorts > Mutation Frequency | Cross-cohort Mutation frequency analysis | Bar plot | Bar plot comparing mutation frequency (count or %) for a gene across multiple cohorts | See whether a gene is more frequently mutated in some cancer cohorts than others | No special library. Base R tally; ggplot2 draws the plot. | No cohort selection needed beforehand — this analysis picks its own cohorts. |
| 41 | Clinical Data Analysis | Compare cohorts > Gene expression | Cross-cohort Gene expression analysis | Box plot | Box plot comparing a gene's expression levels across multiple cohorts | See whether a gene's expression level differs systematically between cancer types | No special library. Base R tally; ggplot2 draws the plot. | Can take several minutes for many cohorts (e.g. all TCGA). |
| 42 | Clinical Data Analysis | COSMIC | Cancer Gene Census (COSMIC) | Table | Table of input genes matching the COSMIC Cancer Gene Census, with their annotation | Check whether candidate genes are known cancer predisposition genes | No special library. Table lookup against a bundled COSMIC reference file. | If no genes match, the full reference database is shown instead. |
| 43 | Clinical Data Analysis | Upload data | Manage the cohort database | - | Editable registry of all cohorts; upload form for new cohorts with live previews | Upload your own cohort (expression, survival, metadata, optional mutation data), or edit/delete existing ones | No special library. Built-in R read/write/validation for the 4 file types. | Sample-ID column must be named "sample", gene column "id". Deleting a cohort is permanent. |
| 44 | scRNAseq Analysis | Overview (UMAP) | Overview UMAP plot | UMAP plot | UMAP scatter plot of all cells, coloured by cluster or a chosen metadata column, with optional single-group highlighting | Get a quick overview of a scRNA dataset's cell clusters or cell types, or highlight one group of interest | No special library beyond Seurat (for the stored UMAP coordinates); ggplot2 draws the scatter plot. | Colour customisation for the highlighted group is not currently available in the interface (disabled in the code). |
| 45 | scRNAseq Analysis | Feature plots > Feature Plot (UMAP) | Gene Expression Feature Plot | Feature plot | UMAP scatter plot of all cells, coloured by one gene's expression level (gradient), with non-expressing cells shown in a background colour | Visualise which cells express a specific gene of interest, and where they sit within the UMAP | No special library. ggplot2 draws the UMAP scatter plot, coloured by the selected gene's expression. | |
| 46 | scRNAseq Analysis | Feature plots > Violin Plot | Gene Expression Violin Plot | Violin plot | Violin plot of one gene's expression across cell groups, with optional jitter points | Compare a gene's expression across cell groups (e.g. clusters or cell types) | No special library. ggplot2 draws the violin plot (geom_violin), with optional jitter points overlaid. | "Hide jitter plots" is on by default. |
| 47 | scRNAseq Analysis | Feature plots > Dot Plot | Gene Expression Dot Plot | Dot plot | Dot plot of average expression and percent-expressing per gene across cell groups | Compare expression of multiple genes across cell groups at a glance | Seurat package's DotPlot function. | |
| 48 | scRNAseq Analysis | Feature plots > Pie chart | Gene Expression Pie chart | Pie chart | One pie chart per cell group, showing the proportion of cells expressing vs. not expressing the selected gene | See what fraction of cells in each group express a gene of interest | No special library for the calculation. ggplot2 (coord_polar) draws each pie slice; cowplot arranges the per-group pies into a grid. | A cell counts as expressing a gene if its UMI count is 1 or greater. |
| 49 | scRNAseq Analysis | Feature plots > Gene Set Signature Feature Plot | Gene Set Signature Feature Plot | Feature Plot | UMAP scatter plot of all cells, coloured by each cell's AUC signature score for the input gene set | Score each cell for activity of a gene set (e.g. a pathway or TF target set) and see where that activity is highest on the UMAP | AUCell package (AUCell_run) calculates the per-cell signature score; ggplot2 draws the feature plot from the scores. | Calculation takes 1-3 minutes depending on gene set and dataset size. |
| 50 | scRNAseq Analysis | Feature plots > Gene Set Signature Feature Plot | Gene Set Signature Violin Plot | Violin Plot | Violin plot of the AUC signature score across cell groups | Compare a gene set's activity score across cell groups (e.g. clusters or cell types) | AUCell package (AUCell_run) calculates the per-cell signature score; ggplot2 draws the violin plot from the scores. | Shares the same AUC calculation as the Feature Plot output; only the grouping/visualisation differs. |
| 51 | Epigenome Visualisation | Profile Plot | Profile Plot | Profile plot | Combined line plot (signal intensity) + heatmap of the same signal, one per selected bigWig file, across the specified region | Compare epigenetic signal intensity (e.g. ChIP-seq/ATAC-seq bigWig) across a genomic region for multiple samples at once | rtracklayer reads the bigWig signal; EnrichedHeatmap draws the combined profile line and heatmap. | |
| 52 | Epigenome Visualisation | Genome visualisation | Genome visualisation plot | Genome track plot | Genome browser-style track plot showing read coverage per selected dataset, aligned to the reference genome | Inspect raw sequencing coverage (bigwig/bam) at a specific locus alongside the reference genome | Gviz package draws the genome tracks. | BAM files over a wide range (~100-200kb+) can cause a memory error; colour/height/Y-axis-limit changes require re-plotting to take effect. |
| 53 | Epigenome Visualisation | IGV | IGV | - | Embedded interactive IGV genome browser widget for the selected bed dataset | Browse an uploaded bed file interactively in a familiar IGV interface | igvShiny package embeds the IGV genome browser widget. | Only bed files are supported here (due to memory limits); use Genome visualisation above for bigwig/bam. |
| 54 | Epigenome Visualisation | Find Enhancer/Promoter | Find enhancers/promoters | Table | Table of every tested gene-peak pair with its correlation coefficient and p-value | Screen many genes/peaks at once for genomic regions where chromatin accessibility tracks with nearby gene expression | Base R cor.test (Pearson or Spearman) calculates each gene-peak correlation. | "Check only the same chromosomes of the target genes" is on by default (restricts the search to peaks on the same chromosome as the gene); gene coordinates are looked up from a fixed GRCh38 (hg38) annotation regardless of the data's own genome build. |
| 55 | Epigenome Visualisation | Find Enhancer/Promoter | Find enhancers/promoters | Scatter plot | Scatter plot of one gene's expression vs. one peak's accessibility across matched samples, with an optional fitted correlation line | Inspect the relationship between one specific gene's expression and one specific peak's accessibility across samples | ggplot2 draws the scatter plot from the correlation results. | |
| 56 | Epigenome Visualisation | Find Enhancer/Promoter | Find enhancers/promoters | Bar plot | Bar plot of one gene's correlation with every nearby peak it was tested against | See which peaks near a gene of interest are most correlated with its expression | ggplot2 draws the bar plot from the correlation results. | Bars marked with * have a p-value below the threshold set in the potential enhancer/promoter list panel. |
| 57 | Epigenome Visualisation | Find Enhancer/Promoter | Find enhancers/promoters | Bar plot | Bar plot of one peak's correlation with every gene it was tested against | See which genes near a peak of interest are most correlated with its accessibility | ggplot2 draws the bar plot from the correlation results. | Bars marked with * have a p-value below the threshold set in the potential enhancer/promoter list panel. |
| 58 | Epigenome Visualisation | Find Enhancer/Promoter | Find enhancers/promoters | Peak list | Ranked list of peak IDs correlated with the selected gene(s), below a chosen P-value threshold | Get a ranked list of candidate enhancer/promoter peaks for a gene (or all genes) at a chosen significance threshold | No special library. Bespoke code filters the correlation results table by gene and p-value threshold. | |
| 59 | Epigenome Visualisation | Motif scan | Motif scan | Table | Table of motif enrichment results (raw.score, top.motif.prop) ranked by significance | Screen a set of genomic regions or sequences for known transcription factor binding motifs | PWMEnrich package, with the PWMEnrich.Hsapiens.background and MotifDb PWMLogn.hg19.MotifDb.Hsap datasets, scores motif enrichment. | Downloadable via "Download motif table"; a collapsible "Significant motifs" box lists motif names below a chosen threshold. |
| 60 | Epigenome Visualisation | Motif scan | Motif scan | Motif logo | Sequence logo plot for the selected motif | Inspect the sequence logo of one specific motif hit | ggseqlogo package draws the motif logo plot. | Y-axis can show bits or probability. |
| 61 | Custom Gene Sets | Custom Gene Sets | Browse the Custom Gene Sets | - | Editable table of all saved custom gene sets (name, description, cell type, data source, genes) | Browse, correct, or remove previously saved custom gene sets | No special library. Bespoke code reads/writes the underlying TSV file directly. | The geneset name (first column) cannot be edited; saving edits or deleting rows opens a confirmation dialog first. |
| 62 | Custom Gene Sets | Add a Gene Set | Add a Gene Set | - | New custom gene set registered, appearing at the top of the Custom Gene Sets table | Save a personally curated gene list (e.g. a pathway or marker set) for reuse across the interface | No special library. Bespoke code appends a new row to the gene set TSV file. | Geneset name must be unique; line breaks inside any text box keep only the first line. |
| 63 | Tools | Human & Mouse | Human & Mouse Gene Converter | Table | Table converting input gene identifiers between human and mouse orthologs; blank where no ortholog exists | Translate a human gene list into mouse orthologs (or vice versa) to compare across species datasets | No special library. Bespoke code looks up a pre-built BioMart ortholog mapping table (biomart_comparison_chart.tsv), not a live BioMart query. | A single gene may have multiple orthologous matches; downloadable via "Download this table". |
| 64 | Tools | Gene Symbol Ensembl ID | Ensembl ID & Gene Symbol Converter | Table | Table converting input gene identifiers between gene symbol and Ensembl ID; blank where no match exists | Convert a list of Ensembl IDs to gene symbols (or vice versa) for a chosen species | No special library. Bespoke code looks up a pre-built BioMart gene ID mapping table, not a live BioMart query. | Downloadable via "Download this table". |
| 65 | Tools | Find gene positions/annotations | Find gene positions/annotations | Table | Table of gene name (Ensembl ID + symbol), chromosome, start, end, and strand for the input genes or coordinates | Look up a gene's genomic coordinates, or find which gene(s) sit at a specific locus | No special library. Bespoke code queries a GENCODE (GRCh38.p13) annotation file. | Downloadable via "Download this table"; human genome (hg38) only. |
| 66 | Tools | Find gene positions/annotations > Peak annotation | Peak annotation | Table | Table of each input peak (PeakID) with its genomic feature annotation (e.g. Promoter, Exon, Intron, Distal Intergenic) and nearest gene | Annotate a list of ChIPseq/ATACseq peaks with their genomic context and nearest gene | ChIPseeker package (annotatePeak) with a GENCODE v41 TxDb annotation. | Peaks that fail to annotate (bad format, chromosome not in genome, or too wide) are listed by name rather than dropped silently; downloadable via "Download this table". |
| 67 | Tools | Find gene positions/annotations > Peak annotation | Peak annotation | Pie/Bar plot | Pie chart (default) or horizontal stacked bar chart of the proportion of peaks in each genomic feature category | Get a quick visual summary of where a peak set falls relative to genes (promoters vs. introns vs. intergenic, etc.) | Bespoke ggplot2 code, built from the ChIPseeker annotation summary (ChIPseeker's own built-in plot functions don't return a ggplot object). | |
| 68 | Tools | Find gene positions/annotations > Peak annotation | Peak annotation | Gene list | Plain, copyable list of the nearest gene (Ensembl ID or symbol) for each annotated peak | Get a clean gene list from a peak set, ready to paste into another module (e.g. ORA/GSEA) | No special library. Bespoke code extracts the nearest-gene column from the annotation result. | |
| 69 | Tools | Cross-tabulation analysis | Cross-tabulation analysis | Bar plot | 2x2 contingency table, a chi-square/Fisher's exact test result, and a stacked or dodge bar plot of the table (as percentages or raw counts) | Test and visualise the association between two categorical variables (e.g. mutation status vs. treatment response) from manually entered counts | Base R chisq.test/fisher.test for the statistical test; ggplot2 draws the bar plot. | Only 3 plot methods exist: percentile+stacked (default), count+stacked, or count+dodge — there is no percentile+dodge option. |
| 70 | Tools | Venn Diagram | Venn Diagram | Venn diagram | 2-group or 3-group Venn diagram, with a copyable list of elements for any selected overlap/unique category | Check the overlap between 2 or 3 gene lists (or any other element lists) and pull out the shared or unique elements | eulerr package draws the Venn/Euler diagram. | |
| 71 | Tools | Network plot | Network Plot | Network plot | Interactive node-edge network graph | Visualise a custom from-to-weight relationship (e.g. a regulatory network or correlation network) as an interactive graph | visNetwork package (with igraph) renders the interactive network. |