Abstract
We present the advancements and novelties recently introduced in RNASeqGUI, a graphical user interface that helps biologists to handle and analyse large data collected in RNA-Seq experiments. This work focuses on the concept of reproducible research and shows how it has been incorporated in RNASeqGUI to provide reproducible (computational) results. The novel version of RNASeqGUI combines graphical interfaces with tools for reproducible research, such as literate statistical programming, human readable report, parallel executions, caching, and interactive and web-explorable tables of results. These features allow the user to analyse big datasets in a fast, efficient, and reproducible way. Moreover, this paper represents a proof of concept, showing a simple way to develop computational tools for Life Science in the spirit of reproducible research.
Highlights
RNA-Seq [1,2,3,4] is the most widely used technology to study genome-wide gene expression and regulatory mechanisms in response to stress conditions or drug treatments and cell development as well as in the onset and progression of several diseases [5], including cancer
We present the novel advancements we introduced in RNASeqGUI [15] with particular focus on the incorporation of the RR
We wrapped some of its functionalities in order to implement, in the novel version of RNASeqGUI, a caching system to create a set of cache database files, stored in the Logs/cache folder, for each analysis flux of RNASeqGUI
Summary
RNA-Seq [1,2,3,4] is the most widely used technology to study genome-wide gene expression and regulatory mechanisms in response to stress conditions or drug treatments and cell development as well as in the onset and progression of several diseases [5], including cancer. The lack of reproducibility has constituted one of the main limitations of GUIs. in recent years, many different tools provide novel functionalities that support developers to build software (including GUIs) capable of keeping track of all actions performed while executing an analysis (see [24] for an overview). Caching constitutes a solution to speed up repetitive and computational expensive code chunks by using intermediate results stored in precomputed databases In this way, a thirdparty user with small computational resources can either replicate some pieces of the analysis or execute an alternative analysis starting from a middle point in the report.
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