Approaches for ChIP-Seq data may possibly be applicable so I have included the tag.
My RIP-Seq data has a number of conditions and the same experiment performed in a knockout animal. Both KO and WT have been sequenced. The knockout essentially represents background noise for its corresponding WT sample.
DGE analysis is useful, but I would also like to obtain just the relative counts for all of my aligned data. Using relative counts could be useful because it would tell me "what/how many (relative) transcripts are potentially bound to the protein of interest" rather than "how does the pool of bound transcripts change between experimental conditions".
For a normal RNAseq experiment, the approach would be to normalize the data allowing the counts to be compared between conditions. In my particular case, I have knockouts representing background that must be somehow factored in to the calculation of relative counts. DESeq2 and similar software allow you to set up interaction modeling designs that treat the KO as an interacting term when performing DGE analysis. Is there a similar approach I can use for simply looking at relative counts? If not, how can I obtain relative counts and subtract out any background?