DEbPeak aims to explore, visualize, interpret multi-omics data and unravel the regulation of gene expression by integrating RNA-seq with peak-related data (eg: ChIP-seq, ATAC-seq, m6a-seq et al.) . It contains eleven functional panels:

  • Quality Control (QC) : QC on count matrix (proportion of genes detected in different samples under different CPM thresholds and the saturation of the number of genes detected) and samples (sample clustering based on euclidean distance and pearson correlation coefficient, batch effect detection and correction, outlier detection).

  • Principal Component Analysis (PCA) : Scree plot and pairs plot to select the useful PCs, biplot to inspect sample similarity with corresponding genes with larger loadings, visualize genes with larger positive and negative loadings on selected PCs (bar plot and heatmap), perform GO enrichment analysis on genes with larger positive and negative loadings on selected PCs, visualize samples with three selected PCs.

  • Differential Analysis (DA) : Obtain differentially expressed genes from RNA-seq data, obtain differentially accessible/binding peaks from peak-related data and visualize the results interactively or download PDF (VolcanoPlot, ScatterPlot, MAPlot, RankPlot, GenePlot, Heatmap)

  • Functional Enrichment Analysis (FEA) : GO enrichment analysis and KEGG enrichment analysis for differentially expressed genes or differentially accessible/binding peaks, Gene Set Enrichment Analysis for all genes of RNA-seq.

  • Motif Enrichment (MotifEnrichment) : Motif enrichment on differentially accessible/binding peaks.

  • Preprocess ChIP-seq (PreprocessChIP) : Get consensus peaks from replicates, visualize peak accessibility/binding profile, peak annotation (assign peaks with the genomic binding region and nearby genes).

  • Integrate with ChIP-seq (DEbChIP) : Integrate RNA-seq with ChIP-seq based on consensus peaks (consensus mode) or differentially binding peaks (diff mode), integration summary (integration table, venn diagram and quadrant diagram), GO enrichment on integrated results.

  • Preprocess ATAC-seq (PreprocessATAC) : Get consensus peaks from replicates, visualize peak accessibility/binding profile, peak annotation (assign peaks with the genomic binding region and nearby genes).

  • Integrate with ATAC-seq (DEbATAC) : Integrate RNA-seq with ATAC-seq based on consensus peaks (consensus mode) or differentially accessible peaks (diff mode), integration summary (integration table, venn diagram and quadrant diagram), GO enrichment on integrated results, motif enrichment on integrated results.

  • Integrate with ChIP-seq and ATAC-seq (DEbCA) : Integrate RNA-seq with ChIP-seq and ATAC-seq based on consensus mode of DEbChIP and DEbATAC, integration summary, GO enrichment on integrated results.

  • Utils : Useful functions when dealing with RNA-seq data, including gene name conversion (gene symbol, ENSEMBL gene ID, ENTREZ ID) and count normalization(DESeq2’s median of ratios, TMM, CPM, TPM, RPKM).

This website is free and open to all users and there is no login requirement.

Last updated: 2023/03/10.

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Count Matrix

Sample Metadata

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Count QC-CPM

Count QC-Saturation

Sample relationship     Download PDF

Sample PCA     Download PDF

Outlier detection     Download PDF

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Scree Plot

Biplot     Download PDF

PC pairs plot     Download PDF

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Loading bar plot     Download PDF

Loading heatmap     Download PDF

Positive loading genes (GO table)     Download table

Negative loading genes (GO table)     Download table

Positive loading genes (GO plot)     Download PDF

Negative loading genes (GO plot)     Download PDF

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3D PCA

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All results     Download table

Normalized counts     Download table

Up-regulated genes     Download table

Down-regulated genes     Download table

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Visualization

Visualize

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Volcano Plot     Download PDF

Scatter Plot     Download PDF

MA Plot     Download PDF

Rank Plot     Download PDF

Gene Plot     Download PDF

Heatmap     Download PDF

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Up-regulated genes (table)     Download table

Down-regulated genes (table)     Download table

Up-regulated genes (plot)     Download PDF

Down-regulated genes (plot)     Download PDF

Analyze

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Up-regulated genes (table)     Download table

Down-regulated genes (table)     Download table

Up-regulated genes (plot)     Download PDF

Down-regulated genes (plot)     Download PDF

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GSEA table     Download table

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GSEA Plot     Download PDF

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Enriched motifs     Download TAR

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Consensus peaks     Download table

Analyze

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Binding heatmap     Download PDF

Average binding profile     Download PDF

Region profile     Download PDF

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Peak annotation results     Download table

Pie plot     Download PDF

Distribution plot     Download PDF

Upset plot     Download PDF

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Integration results     Download table

Venn diagram     Download PDF

Quadrant diagram     Download PDF

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GO table     Download table

GO plot     Download PDF

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Consensus peaks     Download table

Analyze

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Binding heatmap     Download PDF

Average binding profile     Download PDF

Region profile     Download PDF

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Peak annotation results     Download table

Pie plot     Download PDF

Distribution plot     Download PDF

Upset plot     Download PDF

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Integration results     Download table

Venn plot     Download PDF

Quadrant diagram     Download PDF

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GO table     Download table

GO plot     Download PDF

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Enriched motifs     Download TAR

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Integration results     Download table

Venn plot     Download PDF

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GO table     Download table

GO plot     Download PDF

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Integration results     Download table

Venn plot     Download PDF

Quadrant diagram     Download PDF

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GO table     Download table

GO plot     Download PDF

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Integration results     Download table

Venn plot     Download PDF

Quadrant diagram     Download PDF

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GO table     Download table

GO plot     Download PDF

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Conversion results     Download table

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Normalized counts     Download table

FAQ

1. How to deal with edgeR results?

DEbPeak is compatible with DESeq2 and edgeR . In this interactive interface, it uses DESeq2 internally, for user with edgeR results, the R package is an ideal choice.

2. How to obtain the R package?

DEbPeak is now available on Github , and is going to Bioconductor .