iDEP (integrated Differential Expression and Pathway analysis) is a web-based tool for analyzing RNA-seq data, available at http://ge-lab.org/idep/. It reads in gene-level expression data (read counts or FPKM), performs exploratory data analysis (EDA), differential expression, pathway analysis, biclustering, and co-expression network analysis. iDEP also accepts DNA microarray data or other gene-level expression data, such as those from Chip-seq or proteomics studies.
iDEP is a user-friendly Shiny app powered by many widely-used R/Bioconductor packages for analyzing gene expression data. For EDA, it performs hierarchical clustering, k-means clustering, and principal component analysis (PCA). iDEP detects differentially expressed genes using the limma and DESeq2 packages. For a group of co-expressed genes, it identifies enriched gene ontology (GO) terms as well as transcription factor binding motifs in promoter sequences. Pathway analysis can be performed using GSEA(Gene Set Enrichment Analysis), PAGE (Parametric Analysis of Gene Set Enrichment), GAGE (Generally Applicable Geneset Enrichment), or ReactomePA (Reactome Pathway Analysis). Gene expression data can be visualized on KEGG pathway diagrams using pathview. We essentially packed all of the R/Bioconductor packages we often use for analyzing gene expression data in a graphical user interface (GUI).
iDEP can recognize 159 types of common gene IDs from 111 species. It has a knowledge-base derived from the annotation of 163 metazoa and 45 plant genomes in Ensembl as of 12/15/2017. In addition to gene ontology (GO) annotation from Ensembl, additional data are retrieved from KEGG, Reactome, MSigDB (human), GSKB (mouse), and araPath (arabidopsis). In addition, via API access to STRING-db, iDEP can use protein interaction networks and the rich annotations for 115 archaeal, 1678 bacterial, and 238 eukaryotic species.
Together with ultra-fast quantification methods like Kallisto or platforms like Galaxy, it is now possible to complete the analyses of RNA-seq data in hours, from raw sequences to pathways, on your laptop and under a GUI.
See quick demo here. Or watch the demo video below.
To help users prepare input files we also developed a tool for extracting a column from multiple files. A tool for downloading processed public RNA-seq data. And another tool for converting Gene Ontology files into the desired GMT format. ShinyGO is another related tool that conducts enrichment analysis on a set of gene IDs.