Reviewer response for version 1
This article introduces a new statistical method, called DRIMSeq and implemented in a R/Bioconductor package of the same name, to detect isoform expression changes between two conditions from RNA-seq data. 
The same method can be used to search for significant associations between SNPs and isoform quantifications obtained also from RNA-seq data (sQTLs). 
The main novelty of this method with respect to the existing literature on this problem, is the joint modelling of transcript quantification values derived from isoforms of the same gene, by using a Dirichlet-multinomial model. 
This allows the method to account of the intrinsic dependency between quantification values of these isoforms.
The assessment of DRIMSeq on differential isoform usage provides a comparison of its performance with DEXSeq 1 , a statistical method for differential exon inclusion from RNA-seq data, as function of two different "isoform" quantification strategies: exonic-bin (not really "isoform") count values calculated with HTSeq and transcript-quantification values calculated with kallisto 2 .
The experimental results make perfect sense, DRIMSeq works better than DEXSeq with transcript-quantification values and DEXSeq works better than DRIMSeq with exonic-bin count values. 
However, while both methods, and both types of "isoform" quantification input data, allow one to study the post-transcriptional processing of RNA transcripts, the kind of questions that can be addressed with each of them are different. 
Exonic-bin count values and DEXSeq can be used to investigate differential exon inclusion across conditions, which is a consequence of differential isoform usage, while transcript-quantification values and DRIMSeq can be used to directly investigate differential isoform usage.
A potentially interesting outcome of this comparison in the paper could be some sort of guidelines about when is it more sensible to investigate differential exon inclusion or differential isoform usage, depending on factors such as the biological question at hand, sequencing depth or number of biological replicates. 
However, this is apparently beyond the scope of this paper and the experimental results are in principle geared towards convincing the reader that DRIMSeq improves on existing approaches to discover changes in isoform usage, as suggested in the abstract. 
In my view, the experimental results do not address this question and I would suggest the authors to compare DRIMSeq with methods that also work with transcript-quantification values and assess differential isoform usage such as, for instance, Cuffdiff 3 or sleuth 4 .
The experimental results on searching for sQTLs compare favourably DRIMSeq with an existing tool for that purpose, sQTLseekR 5 . 
Evaluating performance in this context is challenging and the idea of assessing enrichment with respect to splicing-related features is a good one. 
However, the (two) presented features in Table 1 could be made more precise. 
It is unclear that a SNP close to a GWAS hit should be necessarily related to splicing and it is also unclear why one should expect splicing-related enrichment more than a few hundred nucleotides away from the intervening exon. 
While it is technically interesting to see a method being used to address two completely different research questions, in my view, mixing both types of analyses makes the article less focused. 
I would argue that both questions deserve separate papers, and that would allow the authors to investigate in depth critical aspects of both types of analysis that are currently not addressed in the current article.
In summary, this article provides an interesting new methodology for the analysis of differential isoform usage from RNA-seq data, it is well-written and the implemented software runs smoothly and is well documented. 
However, in my view, the current experimental results of the article are not that informative for the reader to learn what advantages DRIMSeq provides over other tools for differential isoform usage analysis, and to decide whether he/she should be doing a differential isoform usage, or a differential exon inclusion analysis, if this were a goal of the comparison with DEXSeq.
Minor comments:
- I would replace the term "edgeR ideology" in page 5 by "edgeR strategy".
- In page 9 it is described that the distributions of raw p-values shown in Supplementary Figures S28 and S29 fit "better" when derived from transcript quantification values than from exonic-bin count values, but in fact in both cases the distributions are non-uniform for p-values distributed under the null hypothesis. 
This can be easily shown with the data from vignette of the DRIMSeq package when skipping the step that reduces the transcript set to analyze to speed up the building time of the vignette. 
This is not openly discussed in the paper but I would argue that it is quite critical to know under what technical assumptions the proposed hypothesis test leads to uniform raw p-values under the null, as this has a direct consequence on the control of the probability of the type-I error.
- The sQTL analysis described in pages 9, 10 and 11 uses transcript-quantification values from FluxCapacitor. 
If the entire first part of the paper shows the performance metrics of DRIMSeq using kallisto, in my view, it would make more sense to use kallisto for this analysis as well.
- With regard to the implementation in the R/Bioconductor software package DRIMSeq, the authors have implemented a specialized S4 object class called 'dmDSdata' to act as a container for counts and information about samples. 
Since the package forms part of the Bioconductor project, I think it would better for both, the end-user and the developer authors, that the package re-uses the 'SummarizedExperiment' class as container for counts and sample information. 
This would facilitate the integration of DRIMSeq into existing or new workflows for the analysis of RNA-seq data. 
As an example of the limitations derived from providing a completely new specialized object class, the dimensions of a 'dmDSdata' object in terms of number of features and number of samples cannot be figured out using the expected call to the 'dim()' accessor method. 
Of course the authors may add that method to the 'dmDSdata' object class but, in general, there are obvious advantages derived from enabling data interoperability through the use of common data structures across Bioconductor software packages 6 .
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