IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis
Motivation: Next-Generation Sequencing has made available substantial amounts of large-scale genomic and transcriptomic data. Studies with RNA-Sequencing (RNA-Seq) data typically involve generation of gene expression profiles that can be further analyzed using correlation analysis, co-expression analysis, clustering, differential gene expression (DGE), among many other studies. While these analyses can provide invaluable information related to gene expression, interpretation of the results can prove challenging. Results: Here we present a tool called IRIS-EDA, which is a web-server-based expression data analysis tool developed using Shiny. It provides a straightforward and user-friendly platform for performing numerous computational analyses (e.g., principal component analysis, clustering, biclustering, and DGE) on user-provided gene expression data generated from both bulk RNA-Seq and Single-cell RNA-Seq data. Specifically, three most commonly used R-based tools (edgeR, DESeq2, and limma) are implemented in the DGE analysis with seven unique experimental design functionalities, including a user-specified design matrix option. Six discovery-driven methods and tools (correlation analysis, heatmap, clustering, biclustering, PCA, and MDS) are provided for gene expression exploration which is useful for designing experimental hypotheses and determining key factors for comprehensive DGE analysis. Furthermore, this platform integrates seven visualization tools in a highly interactive manner, for improved interpretation of the analyses. It is noteworthy that, for the first time, IRIS-EDA provides a framework to expedite submission of data and results to NCBI's Gene Expression Omnibus following the FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles. Availability: IRIS-EDA is freely available at http://bmbl.sdstate.edu/IRIS/.
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