RobiNA: a user-friendly, integrated software solution for RNA-Seq-based transcriptomics

Abstract

Recent rapid advances in next generation RNA sequencing (RNA-Seq)-based provide researchers with unprecedentedly large data sets and open new perspectives in transcriptomics. Furthermore, RNA-Seq-based transcript profiling can be applied to non-model and newly discovered organisms because it does not require a predefined measuring platform (like e.g. microarrays). However, these novel technologies pose new challenges: the raw data need to be rigorously quality checked and filtered prior to analysis, and proper statistical methods have to be applied to extract biologically relevant information. Given the sheer volume of data, this is no trivial task and requires a combination of considerable technical resources along with bioinformatics expertise. To aid the individual researcher, we have developed RobiNA as an integrated solution that consolidates all steps of RNA-Seq-based differential gene-expression analysis in one user-friendly cross-platform application featuring a rich graphical user interface. RobiNA accepts raw FastQ files, SAM/BAM alignment files and counts tables as input. It supports quality checking, flexible filtering and statistical analysis of differential gene expression based on state-of-the art biostatistical methods developed in the R/Bioconductor projects. In-line help and a step-by-step manual guide users through the analysis. Installer packages for Mac OS X, Windows and Linux are available under the LGPL licence from http://mapman.gabipd.org/web/guest/robin.

Figure 1.

Flow chart of RNA-Seq-based differential gene-expression analysis steps provided by RobiNA.

Figure 2.

Screen shots showing excerpts of RobiNAs GUI. The left panel shows the experiment designer step that allows the graphical definition of comparisons of interest. The middle panel illustrates the trimming pipeline setup. On the right side examples of quality check plots are shown. The panel shows a base call quality summary plot (upper left), positional base call frequencies (upper right), overall read quality distribution (lower left) and the positional K-mer enrichment plot (lower right).