Abstract
BackgroundApplication of microarrays in omics technologies enables quantification of many biomolecules simultaneously. It is widely applied to observe the positive or negative effect on biomolecule activity in perturbed versus the steady state by quantitative comparison. Community resources, such as Bioconductor and CRAN, host tools based on R language that have become standard for high-throughput analytics. However, application of these tools is technically challenging for generic users and require specific computational skills. There is a need for intuitive and easy-to-use platform to process omics data, visualize, and interpret results.ResultsWe propose an integrated software solution, eUTOPIA, that implements a set of essential processing steps as a guided workflow presented to the user as an R Shiny application.ConclusionseUTOPIA allows researchers to perform preprocessing and analysis of microarray data via a simple and intuitive graphical interface while using state of the art methods.
Highlights
Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously
Implementation eUTOPIA is developed in R programming language with a graphical interface layer designed by using R Shiny [3] web development framework
EUTOPIA is capable of processing data from four microarray platforms: Agilent gene expression two-color microarray data (Samples specific to different colors channels), Agilent gene expression one-color microarray data, Affymetrix gene expression microarray data, and Illumina methylation microarray data
Summary
Application of microarrays in omics technologies enables quantification of many biomolecules simultaneously. EUTOPIA requires that user provides a detailed phenotype information file (Additional file 2) with all biological and technical variables of the samples in the experiment. Known biological (e.g., treatment, disease status, age, tissue, etc.) and technical (e.g., dye, array, etc.) variables are provided by the user in the phenotype information, while unknown sources of variations can be identified by using the sva function from sva R package [11].
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