DiCoExpress: a workspace to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models.

Background RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realized neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Nevertheless, performing an RNAseq analysis remains a challenge for the biologists. Results DiCoExpress is a workspace implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses the pre-existing R packages as well as FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models with no effort thanks to the automated contrast writing function. DiCoExpress proposes a list of comparisons based on the experimental design, and the user needs only to choose the one(s) of interest for his research question. A co-expression analysis is implemented using the coseq package. Identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user, and several result outputs proposed. We used DiCoExpress to analyze a publicly available Bra ssica napus L. RNAseq dataset on the transcriptional response to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. Conclusions DiCoExpress is an R workspace to allow users without advanced statistical knowledge and programming skills to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models . Hence, with DiCoExpress, the user can focus on the statistical modeling of gene expression according to the experimental design and on the interpretation of the results of such analysis in biological terms.