Hi,

I have followed Trinity guidelines and assembled a denovo assembly as well as used that assembly as a reference to quantify the reads using RSEM and finally followed it with Differential expression analysis using DESeq2. For Differential expression analysis, I have used RSEM.genes.results rather than the RSEM.isoform.results, since I was not sure using isoform level expression, is accurate or not compared to gene-level expression.

But the problem now is how do I select the best transcript/isoform for the Differential expressed genes because without that I cannot extract the sequence from Trinity assembly as the Trinity assembly has sequences for isoforms and not genes.

I have thought of doing this in several ways - selecting the longest isoform, clustering all the isoforms, and then select the longest isoform but I was wondering if I can get a consensus of all the isoforms for the gene of interest.

Using isoforms instead of genes is not incorrect *per se*, but it is noisier (particularly so for a *de novo* assembled transcriptome) and needs larger sample sizes and deeper sequencing per sample, so as https://www.biostars.org/u/25721/ already said, I would indeed recommend gene-level expression analysis. 

There is no need to use `tximport`, as its method has been [implemented][1] in Trinity. The **RSEM.genes.results** should be identical to importing the counts with `tximport`.

What is the "best" isoform is open to debate, but I would argue the longest is not the best. I remember seeing Trinity authors recommending selecting the most expressed isoform, but I can't find the link.

>but I was wondering if I can get a consensus of all the isoforms for the gene of interest.

It seems you want a "super-transcripts" representation of the transcripts, Trinity has implemented this as well, see the [SuperTranscripts][2] wiki page.


  [1]: https://github.com/trinityrnaseq/trinityrnaseq/wiki/Trinity-Transcript-Quantification
  [2]: https://github.com/trinityrnaseq/trinityrnaseq/wiki/SuperTranscripts