The platform provides statistical analyses of RNA-seq count data.
Before any project starts, we help the user to design his experiment in order to ensure that it really addresses the biological question he wishes to address. Replication is the basis of the experimental design. A minimum of three biological replicates are needed to control the false positive rate to the desired level. Then, starting from count data we apply an in-house R-based statistical pipeline that performs the quality control of the data and detects possible problems if any, the normalization and the test of differential expression. DESeq2 is the default method of this package, but edgeR can be used as well. Results are provided in the form of tab-delimited text files of differentially expressed genes. A pdf report describes the whole analysis process and keeps track of the full set of parameters and software versions associated with the analysis.
Both simple and complex designs can be analyzed with this pipeline. For designs involving a single biological factor we also have developed a R-based analysis pipeline called SARTools. Using this R package and 2 associated R script templates, the user can perform the analysis by himself and get results in the form of tab-delimited text files and a html report that contains the same information as the pdf report. This analysis pipeline relies on both DESeq2 and edgeR bioconductor packages.