Hi everyone:
I am interested in quantifying change in repetitive elements ( LTR here) transcription after treatment and I come up with following ideas:

1. Directly map RNA-seq data to genome with hisat2 and quantify with repetitive element annotation from Repeatmasker, followed by collecting elements from the same class to compare them. But I am not sure about how to set up maximum allowed multiple alignment value (For most RNA-seq it requires to be uniquely mapped but the value would be much higher since repetitive elements happens lots of times).
2. I got consensus repetitive element sequence fastq from Repbase, is it possible to view these repeat elements as "transcriptome" and use salmon (or similar transcriptome based tools) to map reads on it? 

I am not familiar with this area and I would appreciate any suggestions . Thanks for help!

Update: Since I am only interested in LTR, I have modified the question. It looks possible to extract uniquely mapped reads and combine with Repeatmasker annotation. Direct quantification looks like will fail since repetitive elements are abundant in mRNA.

You can use STAR and then put though [TEtranscript](https://github.com/mhammell-laboratory/tetoolkit). You can allow multiple entries with STAR and it generally produces better alignments than hisat2 (in my experience at least). Our group that works on repeat elements uses this method.

While you can use the consensus repeat sequence, you end up biasing things for how close the expressed repeats are to the consensus. Consensus sequences are mostly useful for showing a profile over a single instance where you can label structure easily.