DEbPeak aims to explore, visualize, interpret RNA-seq data and unravel the regulation of gene expression. It contains eight functional panels:
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Quality Control (QC) : QC on count matrix and samples.
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Principal Component Analysis (PCA) : Visualize the PCA results, analyze the genes with large loadings of specific principal component.
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Differential Expression Analysis (DEA) : Get differentially expressed genes (DEGs) and visualize the results interactively or download PDF.
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Functional Enrichment Analysis (FEA) : GO enrichment analysis, KEGG enrichment analysis, Gene Set Enrichment Analysis.
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Integrate with ChIP-seq (DEbChIP) : Get consensus peaks from replicates, visualize peak profile, peak annotation, integrate with RNA-seq, integration summary, GO enrichment on integrated results.
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Integrate with ATAC-seq (DEbATAC) : Get consensus peaks from replicates, visualize peak profile, peak annotation, integrate with RNA-seq, integration summary, GO enrichment on integrated results, find motif on integrated results.
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Integrate with ChIP-seq and ATAC-seq (DEbCA) : Integrate RNA-seq, ChIP-seq and ATAC-seq based on the peak annotation results of DEbChIP and DEbATAC, integration summary, GO enrichment on integrated results.
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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: 2022/12/1.
Yabing Song: songyb0519@gmail.com.
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3D PCA
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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 .