Next generation sequencing technologies have enabled the generation of huge quantities of
biological data, and nowadays extensive datasets at different ‘omics levels have been generated. Systems
genetics is a powerful approach that allows to integrate different ‘omics level and understand the biological
mechanisms behind complex diseases or traits. In the recent past, transcriptomic studies with microarrays
have been replaced with the new powerful RNA-seq technologies. This has led to detection of novel gene
transcripts, novel regulatory mechanisms, allele specific gene expression and numerous non-coding RNAs
(ncRNAs). The integration of transcriptomics data with genomic data in a systems genetics context
represents a valuable possibility to go deep into the causal and regulatory mechanisms that generate
complex traits and diseases. However RNA-Seq data have to be treated carefully and the choice of the right
methodology could have a great impact on the final results. Furthermore the integration of different level is
not trivial. Here we give a comprehensive systems genetics overview of the methods and tools for analysis
and the integration of RNA-Seq data including ncRNAs. We focused principally on merits and demerits of
tools for post mapping quality control, normalization, differential expression analysis, gene network
analysis, and integration of different omics data in order to generate a comprehensive guideline to systems
genetics analysis using RNA-Seq data.