Hi, 

This might be another naive question. I have RNA-Seq data from 2 different types of samples (pain vs no-pain), and I want to **identify novel lncRNA/eRNA in pain**. I also have DHS/ATAC data for that gives me accessible regions in the DNA, some of which would be regulatory. 

One way to do this is to use Trinity (using all samples) for de-novo transcript assembly, then use Salmon **on this assembled transcriptome** to find transcripts that are differentially expressed, and then among those, the transcripts that don't have prior annotation would be 'novel' transcripts. Cufflinks can also be used for this.

Another way would be to just directly run Salmon on the samples, using the **hg38 transcriptome**, and find differentially expressed transcripts, some of which could be novel.

I want to know if doing this via the first method will lead to *more* novel transcripts, and are there any merits to the first method over the second, in general.

Thank you!
I would not use Trinity on human samples. Trinity is mainly usable when you do **not** have a reference genome.

Since you have a reference genome **and** a well annotated transcriptome you are much better of using tools which perform guided de-novo transcript reconstruction. The most well known tools for that are Cufflinks and StringTie. I have written more about the detailed steps and considerations [here][1].


  [1]: https://bioconductor.org/packages/devel/bioc/vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html#what-quantification-tools-should-i-use