Dear Mr Karollus,

Thank you very much for submitting your manuscript "Predicting Mean Ribosome Load for 5’UTR of any length using Deep Learning" for consideration at PLOS Computational Biology.

Let me first apologize for the extraordinary time it took to review your article. As you might imagine, the significant load on reviewers and their reluctance to agree on reviews or complete the reviews they agreed to do is the main reason behind this delay. However, I am glad that we obtained two reviews that will allow us to see your paper in the next stage. We have one outstanding review that may still come but in the interest of moving the science forward, we have decided to not wait for it at this time.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Predrag Radivojac

Associate Editor

PLOS Computational Biology

William Noble

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Review is uploaded as an attachment.

Reviewer #2: The authors predict the mean ribosome load from 5’UTR sequences. Overall, the paper is well written, and has a sound methodology towards predicting the translation rate. The main contribution of the paper is the framepooling operation within a neural network where a convolutional neural network takes into account the three possible frames individually by taking the global average and max pooling from each of them. This allows the model to learn features from arbitrary length sequences by having full coverage of the sequences.

Pros:

1. The model performs better or at a similar level of the Optimus models. Specially it performs really well in terms of correlation score for longer than 100nt sequences.

2. The frame pooling operation is novel in the context of this problem.

3. The model performs fairly well in predicting strength of upstream transcription initiation sites.

4. The model achieves fairly well PhyloP conservation scores from its prediction of single nucleotide variants showing it may indeed be learning biologically relevant subsequences.

5. The authors tested their method on endogenous data as MPRA data might not account for the spuriousness that is experienced there. Even though the correlation scores are pretty low, I thank the authors for undertaking this part.

Cons:

1. The absence of a convolutional neural network that takes arbitrary length sequences and uses traditional dilated convolution or max pooling operations mean I am not really sure the frame pooling operation is indeed necessary.

2. No comparison with a random forest that just takes as input 3-grams or 4-grams. This solves the arbitrary length issue, and from experience, it’s hard to do better than a random forest. So, it is essential that there is a comparison to gauge if a neural network is even necessary.

3. One salient point of using a neural network might be to interpret features learnt by the convolutional filters. It will be interesting to see if they are learning motifs but there is not much material on this in the paper.

The paper does a good job of presenting the results in a clear way. The authors have also deposited their code and data properly which is always appreciated. I thank the authors for their hard work and would like to see their responses to my cons comments.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes: Anthony D. Fischer

Reviewer #2: No

Figure Files:

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Attachments

Attachment

Submitted filename: Review of Karollus.docx

Dear Mr Karollus,

We are pleased to inform you that your manuscript 'Predicting Mean Ribosome Load for 5’UTR of any length using Deep Learning' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Predrag Radivojac

Associate Editor

PLOS Computational Biology

William Noble

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: I thank the authors for addressing the questions/concerns I had.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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PLOS authors have the option to publish the peer review history of their article (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 #2: No