BACKGROUND: Several R packages exist for the detection of differentially 
expressed genes from RNA-Seq data. The analysis process includes three main 
steps, namely normalization, dispersion estimation and test for differential 
expression. Quality control steps along this process are recommended but not 
mandatory, and failing to check the characteristics of the dataset may lead to 
spurious results. In addition, normalization methods and statistical models are 
not exchangeable across the packages without adequate transformations the users 
are often not aware of. Thus, dedicated analysis pipelines are needed to include 
systematic quality control steps and prevent errors from misusing the proposed 
methods.
RESULTS: SARTools is an R pipeline for differential analysis of RNA-Seq count 
data. It can handle designs involving two or more conditions of a single 
biological factor with or without a blocking factor (such as a batch effect or a 
sample pairing). It is based on DESeq2 and edgeR and is composed of an R package 
and two R script templates (for DESeq2 and edgeR respectively). Tuning a small 
number of parameters and executing one of the R scripts, users have access to 
the full results of the analysis, including lists of differentially expressed 
genes and a HTML report that (i) displays diagnostic plots for quality control 
and model hypotheses checking and (ii) keeps track of the whole analysis 
process, parameter values and versions of the R packages used.
CONCLUSIONS: SARTools provides systematic quality controls of the dataset as 
well as diagnostic plots that help to tune the model parameters. It gives access 
to the main parameters of DESeq2 and edgeR and prevents untrained users from 
misusing some functionalities of both packages. By keeping track of all the 
parameters of the analysis process it fits the requirements of reproducible 
research.