In this test, I'll be using the azacitidine mRNA-seq data set that I have previously analysed. To make the count matrix, I used featureCounts.
First step in the process is to your RNA-seq count data. It can be done in tab or comma separated formats. Once uploaded, you're given a configuration screen to specify the format of the data and the sample groups. Make sure you specify which column contains the gene names/accession numbers. Hit the "view" button and you'll get the smear plot. You can use the mouse to highlight genes. At the bottom there is a table of most statistically significant genes and the search function allows you to quickly find your favourite genes.
|Degust interface with smear plot.|
|Parallel coordinates plot.|
|Venn diagram of differentially expressed genes. * Denotes the use of DEB online tool.|
In summary, Degust is a valid tool for RNA-seq analysis for simple comparisons (ie unpaired, 2-sample groups) that is faster and more user friendly than DEB. Its attractive, intuitive and responsive interface suggests that it will be a popular tool for expression analysis.
It would be great if it could also deal with more complicated experimental setups such as sample pairing, ANOVAs and GLMs; and I see downstream pathway analysis such as GSEA as a natural extension to Degust.
write.table(topTags(et,n=20000), file="aza_mRNA_edger.xls", quote=FALSE, sep= "\t")