Dear Dr. Nieselt,

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Additional Editor Comments:

All three reviewers recommeded only minor revisions. Congratulations! Please revised the manuscript accordingly to their comments.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: N/A

Reviewer #2: N/A

Reviewer #3: N/A

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: TSSpredator is a useful tool for TSS mapping from prokaryotic RNA-seq data and this webportal is user-friendly in its design with a set of presets to alter sensitivity/specificity of the search. The replicate handling seems reasonable, and the user is provided with a range of files and visualisatio possibilities that make this webportal certainly valuable. The manuscript does a great job explaining all the options.

I cannot comment much on the building of the website as this is beyond my expertise.

There is one important point that remains unclear to me. The “coverage tracks” used by TSSpredator as input appear to be coverage tracks for the entire read length as judged from the figures 1, 4, 7 and 8. Limiting the coverage calculation to the 5’-ends of the reads that actually represent the TSS signal makes more sense and this option is included in the READemption software that the authors recommend for input file generation (“-b first_base_only”) as well as other widely used software like bedtools genomcov or deeptools . I know this rather refers to TSSpredator itself than the webportal described here. But, as a minimum, it should be properly defined in the text whether 5’-end coverage or read coverage (or fragment coverage for paired-end data?) is used.

Besides this point, I have only very minor comments.

Page 2, line 18 “most genome annotations only provide translation start sites” , maybe more precise to say “predicted translation start sites”, generally they are not experimentally verified.

Page 3, line 90 “pre-normalized” This is somewhat ambigous, raw coverage might be a better term here?

Page 8, line 286 Novobiocin is a DNA gyrase inhibitor, thus will affect more processes than “DNA synthesis”

Page 9, line 324 “subset of TSS: those enriched only in the presence of an antibiotic” In the absensce of any statistical testing whether there is actually a significant difference here, I would rather say “passing the TSS calling threshold”.

Reviewer #2: This manuscript is well articulated and provides clear guidance for any usage of this tool. One potential concern is that the algorithm underlying TSSPredator has been previously published, however the authors do not claim novelty with regard to this. In fact, the tool has been available for some time in a command-line interface. Rather what the manuscript offers is increased usability of this tool through a streamlined process and integration with data visualization resources, which was not previously available. Indeed the authors outline 5 goals for the development of this GUI and clearly address how they accomplished each one. Thus I think the manuscript potentially represents a valuable contribution to the community. Indeed the authors even informed their design of the tool based on common approaches used by other studies for data visualization, which is a thoughtful approach. However as the usability of the GUI is central to this project's impact it is important to note that the GUI is slow. This is not necessarily a problem for file upload and analysis as these are expected to take time, however when I was trying to work with the output the website kept crashing. This is a concerning issue for usability of this tool as one of its major assets is data visualization. As the user can already download a set of results, it is worth considering also producing scripts for figure generation to support data visualization that could be easily run locally such as in R Studio (see http://revigo.irb.hr/ for an example of this style of implementation). This could be a workaround of website bandwidth issues if other options are not possible.

Comments:

lines 17-19 consider addressing why standard RNA-seq does not produce comprehensive results for TSS prediction

lines 36-39 You make the claim that TSSs are essential for comparative transcriptomics. However comparative transcriptomics is accomplished without this. Consider more precise wording of what you mean by comparative transcriptomics.

Discussion - consider slightly polishing the wording here. While fine and readable it read less polished than the other sections of the manuscript.

Change file type (e.g., png, jpeg, tiff) for supplementary materials so it is easier to view and does not have to be separately rendered.

Reviewer #3: This manuscript presents the development of a user-friendly frontend for TSSPredator, a tool that identifies transcription starting sites from RNA sequencing data, called TSSPredator-Web. In addition to simplifying the prediction process for less technical researchers, TSSpredator-Web also provides an interface to explore the results dynamically. Finally, the authors demonstrate a use case of the tool to identify both known and potentially novel TSSs associated with antibiotic exposure.

This manuscript clearly describes the current state of the field and identifies the gap that their software is filling. The descriptions of the software and the TSSpredator algorithm are very clear. The use case showcases the capabilities of the software well and demonstrates clearly how a researcher could use it. However, the use case could be expanded a bit to compare TSSpredator with the other mentioned TSS identification approaches.

Overall I would recommend this manuscript be accepted with some minor revisions.

Though the authors describe the TSSpredator approach clearly in section 2.1, since it has not been formally published previously, they may consider making this work that publication and formalizing the algorithm, possibly using pseudo-code or taking a more mathematical approach. Additionally, Figure 1 could be slightly elaborated to include where certain parameter choices are implicated. In fact, the authors might consider integrating Figures 1 and 2 to provide a more complete view of the workflow.

The authors provide a use case of the tool in section 3 to demonstrate its capabilities over existing approaches. Near the end (line 355), the authors propose the potential identification of “previously unannotated transcriptional units.” However, it’s unclear if these are due to the new dataset or if they are unique to the TSSpredator algorithm. A comparison with the other aforementioned approaches would be useful. Overall, a summary of the differences between TSSpredator and the other approaches could put into context how the results from this approach differ.

The authors make clear that TSSpredator-Web is primarily for data exploration, and statistical evaluations of TSS identification are not elaborated on. However, potential researchers may be interested in demonstrating whether the existence of a particular TSS is statistically different between two or more experimental conditions. Though the tool may not be able to achieve this, it might be worth clarifying whether it would be possible to do this using the underlying data behind TSS identification. If not, it may be worth adding to the discussion of limitations.

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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TSSpredator-Web: A web-application for transcription start site prediction and exploration

PONE-D-25-28983R1

Dear authors,

I am pleased to inform you that the revised manuscript was accepted for publication by all three reviewers.

Thank you for submitting your work to the PLOS ONE journal and best regards,

António Machado

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: Congratulations to the authors, I just want to one comment the authors might take into consideration:

p.2, l.19: Besides the inability of standard RNA-seq to distinguish processed 5' ends, it might be worth mentioning the 5' end fragmentation problem resulting from RNA fragmentation prior to reverse transcription and leading generally to lower 5' end coverage compared to the middle body of the gene.

Reviewer #2: The authors have adequately and thoughtfully address my comments as well as those of the other reviewers.

Reviewer #3: (No Response)

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: Yes: Fabian Blombach

Reviewer #2: No

Reviewer #3: No

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