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BioinformaticsPLoS Computational Biology
Peer-reviewed journal article

IRIS-EDA: An integrated RNA-Seq interpretation system for gene expression data analysis

Brandon MonieriD, Adam McDermaidiD, Cankun WangiD, Jing ZhaoiD, Allison J. MilleriD, Anne Y. FennelliD, Qin MaiD

Abstract

MotivationNext-Generation Sequencing has made available much more 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, many times involving differential gene expression (DGE). This process enables comparison across samples of two or more factor levels. A recurring issue with DGE analyses is the complicated nature of the comparisons to be made, in which a variety of factor combinations, pairwise comparisons, and main or blocked main effects need to be tested.\n\nResultsHere we present a tool called IRIS-DGE, which is a server-based DGE analysis tool developed using Shiny. It provides a straightforward, user-friendly platform for performing comprehensive DGE analysis, and crucial analyses that help design hypotheses and to determine key genomic features. IRIS-DGE integrates the three most commonly used R-based DGE tools to determine differentially expressed genes (DEGs) and includes numerous methods for performing preliminary analysis on user-provided gene expression information. Additionally, this tool integrates a variety of visualizations, in a highly interactive manner, for improved interpretation of preliminary and DGE analyses.\n\nAvailabilityIRIS-DGE is freely available at http://bmbl.sdstate.edu/IRIS/.\n\nContactqin.ma@sdstate.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

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