ideal: an R/Bioconductor package for interactive differential expression analysis
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SOFTWARE
ideal: an R/Bioconductor package for interactive differential expression analysis Federico Marini1,2* , Jan Linke1,2 and Harald Binder3
*Correspondence: marinif@uni‑mainz.de 1 Center for Thrombosis and Hemostasis (CTH), University Medical Center of the Johannes Gutenberg University Mainz, Langenbeckstr. 1, 55131 Mainz, Germany Full list of author information is available at the end of the article
Abstract Background: RNA sequencing (RNA-seq) is an ever increasingly popular tool for transcriptome profiling. A key point to make the best use of the available data is to provide software tools that are easy to use but still provide flexibility and transparency in the adopted methods. Despite the availability of many packages focused on detecting differential expression, a method to streamline this type of bioinformatics analysis in a comprehensive, accessible, and reproducible way is lacking. Results: We developed the ideal software package, which serves as a web application for interactive and reproducible RNA-seq analysis, while producing a wealth of visualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expression analysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility. Conclusion: ideal is distributed as an R package in the Bioconductor project (http://bioconductor.org/packages/ideal/), and provides a solution for performing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand. Keywords: RNA-Seq, Differential expression, Interactive data analysis, Data visualization, Transcriptomics, R, Bioconductor, Shiny, Web application, Reproducible research
Background Over the last decade, RNA sequencing (RNA-seq, [1]) has become the standard experimental approach for accurately profiling gene expression. Complex biological questions can be addressed, also thanks to the development of specialized software for data analysis; these aspects are, e.g., reviewed in the works of Conesa et al. [2] and Van den Berge et al. [3], which cover a broad spectrum of the possible applications.
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