A GUI for the identification of differentially expressed genes that supports Reproducible Research.

Authors: Dr Francesco Russo and Dr Claudia Angelini (IAC-CNR)

Additionally, Dario Righelli is collaborating to the development of RNASeqGUI since version 0.99.3

Last update (version 1.1.2) June, 2016

RNASeqGUI R package is a graphical user interface for the identification of differentially expressed genes from RNA-Seq experiments.

RNASeqGUI is implemented in R following and expanding the idea presented in tuxette-chix.

RNASeqGUI includes several well known RNA-Seq tools, available as command line in Bioconductor.

RNASeqGUI is divided into seven sections. Each section is dedicated to a particular step of the data analysis process. The first section covers the exploration of the bam files. The second concerns the counting process of the mapped reads against a genes annotation file. The third focuses on the exploration of count-data, on the normalization procedures and on the filtering process. The fourth is about the identification of the differentially expressed genes that can be performed by several methods, such as: EdgeR Exact Test, EdgeR GLM for Multi Factors, DESeq, DESeq for Complex Design, DESeq2, DESeq2 for Complex Design, NoiSeq, BaySeq. The fifth section regards the inspection of the results produced by these methods and the quantitative comparison among them. The sixth section regards the Gene-Set and Pathway analysis.

Finally, in the spirit of Reproducible Research in the seventh section we find the Report button that the user can click to generate the report (in html format) that stores of all steps performed during the analysis. The report includes the documentation of the methods used along with the plots generated and all the chunks of codes that have been executed during the RNASeqGUI usage. Moreover, this section also contains the Utility Interface that allows different types of modifications of the input count files.

Cached Computation is used to speed up repetitive and computational expensive function calls by using results stored in pre-computed data-bases.

Moreover, results can be viewed and explored on a web browser thanks to ReportingTools library that allows the user to navigate through them.

This software is not just a collection of some known methods and functions, but it is designed to guide the user during the entire analysis process. Moreover, the GUI is also helpful for those who are expert R-users since it speeds up the usage of the included RNASeq methods drastically.

To cite us, please use :

F. Russo and C. Angelini. RNASeqGUI: a GUI for analysing RNA-Seq data. Bioinformatics, 30 (17): 2514-2516, (2014) doi: 10.1093/bioinformatics/btu308

F. Russo, D. Righelli and C. Angelini. Advancements in RNASeqGUI towards a Reproducible Analysis of RNA-Seq Experiment. BioMed Research International, 2016, 7972351.
doi: http://dx.doi.org/10.1155/2016/7972351