The authors present RES-Scanner, a software package for genome-wide detection of RNAediting sites using matched RNA-Seq and DNA-Seq data. Although a few such packages already exist, I fully agree there is room for improvement. Here, the main two additional components introduced are an improved alignment scheme and a statistical approach for (per-site) mismatches distribution analysis.

However, I believe the authors should provide more information, and add some further improvements, as follows:

1. First of all, a comparison with the existing packages (such as REDItools and GIREMI) is called for. Does RES-scanner improve on current tools?

2. While comparing, it would be nice to look at human samples (for examples, the GM12878 dataset which was used as benchmark by many studies). Then, the results should be examined separately for Alu repeats, other repeats, non-repetitive non-coding, synonymous coding, and recoding sites (see for example Ramaswami et al, Nat. Methods 2013, Supp tables 2 and 3)

3. Blat alignments could be very time-costly. Pleas compare the run time with other packages.

4. When using the tool, starting from fastq files, one needs to run three different Perl scripts consecutively, each of which generates 2-4 shell scripts that should be also executed manually and consecutively. It would be much nicer to run a single command and get the results as in REDItools, for instance.

Overall, I think this tool could be very helpful for the community. I would like the authors to address the above points before recommending publication.

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In the revised version the authors have compared their new algorithm with existing methods. It turns out that the results of the approach proposed here are quite similar to the existing ones. Thus, in order to justify publication more work need to be done in order to motivate usage of the new tool. Is it significantly better is some regions? What is the reason for the not-so-high overlap between the results of the various methods - could one learn something from the fact a site is detected by both, what can be said on the sites detected by one method and missed by others, etc.

The authors mention four novel points in their strategy - they should analyze quantitatively what is gained by each of these improvements (in light of the overall similar results to previous methods). Are there specific circumstances where the proposed method is superior to current ones?

The issue of runtime should be addressed in the main text, including comparison with other methods - if the results are of comparable quality, at least one should verify the new method does not require much more cpu time. (b) It is true that if computing power is not limited, the critical bottleneck is the 10 hours per chr1. Still, the cumulative cpu time needed is also of interest, as many users do not have a huge number of nodes available. Moreover, for some datasets (such as the GTeX or TCGA datasets), hundreds or thousand of RNA-seq samples should be analyzed.

In summary, I do believe there is room for improving on existing tools for RNA editing detection. However, as the present method seem to be comparable to existing ones, more work is required in order to show some advantage and thus justify publication.

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I thank the authors for their thorough comparative analysis. The full detailed comparison should be added to the paper as a supplementary material, and referred to in the main text, in the section discussing the advantages of the present method over existing ones.

I feel the subsection header "The superiority of RES-Scanner relative to existing softwares" is too presumptuous given the results. "Advantages" could work here.

Runtime data for GIREMI should be included, even though (and actually because) its method is different.

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