PONE-D-24-52867A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: focus on pseudoknots identificationPLOS ONE

Dear Dr. Tahi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Emanuele Paci

Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear Dr Tahi,

Please find the reviews below. While your manuscript is publishable on PLoS ONE, I ask you to address the enclosed reviews, particularly that of reviewer 1 asking for major revision.

Kind regards

Emanuele Paci

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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?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper proposes a novel divide-and-conquer method, DivideFold, to address the challenges of predicting RNA secondary structures, particularly pseudoknots, in long sequences. DivideFold recursively partitions RNA sequences into smaller fragments, applies existing structure prediction models to each fragment, and reassembles the overall structure. The method demonstrates superior performance in pseudoknot prediction compared to existing models, with significant computational efficiency for long RNA sequences. Furthermore, the authors provide open access to the DivideFold source code and datasets, enhancing the reproducibility of the research.

The paper splits the dataset based on sequence similarity, but this approach poses a risk of overfitting. RNA sequences within the same family often share characteristic structural patterns, and splitting solely based on sequence similarity can lead to an overlap between the training and test sets, artificially inflating the model's performance.

To address this issue, it is recommended to split the data based on RNA families. For instance, if bpRNA_1m is used for training, an independent dataset such as bpRNAnew dataset (Sato et al, 2021) should be used for testing. This approach would provide a more realistic evaluation of the model's generalization capabilities, particularly for unseen RNA families.

In DivideFold, detecting cut points for splitting sequences into inner and outer fragments requires predicting two cut points simultaneously. This necessitates evaluating the relationships between all pairs of nucleotides, leading to a computational complexity of at least O(n^2).

The paper claims that the cut point detection algorithm operates with O(n) complexity, but this seems to omit the simultaneous prediction of inner and outer cut points or rely on simplifications. A more detailed explanation of the algorithm and its computational complexity is necessary for clarity and validation.

The paper describes the removal of pseudoknots before cut point selection, but it does not clarify how the nucleotide pairs involved in pseudoknots ((i, j) and (k, l) for i<k<j<l) are="" common="" criteria="" for="" include:="" might="" pseudoknot="" removal="" removal.="" selected="selected">- Minimizing free energy for thermodynamic stability,

- Prioritizing longer or more stable stems,

- Heuristic approaches to preserve the overall structure.

The criteria for removal should be clearly explained, because which base pairs to remove would affect the accuracy of the prediction.</k<j<l)>

Reviewer #2: The accurate prediction of RNA secondary structures is still a great challenge, especially for RNAs with long sequence and/or pseudoknots. This work proposed a novel approach to solve this problem by partitioning a long sequence into shorter ones. For this approach, the key is how to maintain the stems formed by long-range bases. The most novel point of this work is proposing a method to ensure that the long-range pairing is not disrupted by the partition. The benchmarks show that in most cases their method overperforms the existing methods in prediction of secondary structure and pseudoknots for long RNAs, including computation time. The results may also be important to understand the mechanism of RNA secondary structures. This paper is clearly written too. I recommend it for publication after minor revisions.

1. Line 151: “For each motif, we check for matches in the sequence through regular expressions and count the number of possible configurations for the motif to be inserted at each point in the sequence.” Give details using a picture

2. It is better to give the average performance of cut-point prediction because this is the key to this approach.

3. It is interesting to know how about the performance of this approach for middle length, e.g., 500-1000nt. Suggest giving a brief discussion.

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Reviewer #1: No

Reviewer #2: No

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PONE-D-24-52867R1A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: focus on pseudoknots identificationPLOS ONE

Dear Dr. Tahi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 14 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Emanuele Paci

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

One of the reviewers has still major concerns on the cross validation of your results. If you cannot fulfill their request, which seems to me justified, please make it clear that your results may be seriously affected by the procedure you used and should be taken with caution.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: (No Response)

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

Reviewer #1: Yes

Reviewer #2: (No Response)

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

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

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

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: (No Response)

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (Sato et al., 2021) and many other papers have pointed out that it is not possible to detect overfitting using only sequence-wise cross-validation with tools such as CD-HIT. In fact, some of the papers you cited as examples of sequence-wise cross-validation in your rebuttal believe that sequence-wise cross-validation alone is not sufficient, and they also conduct evaluation using family-wise cross-validation. To give an example, E2Efold (Chen et al., ICLR 2020) was evaluated for accuracy using only sequence-wise cross-validation, and while it achieved good accuracy there, subsequent validation by several papers by other groups pointed out that it fell into severe overfitting (e.g. Sato et al., 2021; Foo et al., 2022). Therefore, the current situation, where accuracy is evaluated only by sequence-wise cross-validation, cannot be acceptable. If the evaluation using the bpRNAnew dataset that I requested is inappropriate due to the problem of sequence length, you should construct an appropriate dataset to conduct the family-wise cross-validation.

Reviewer #2: (No Response)

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7. 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 #1: No

Reviewer #2: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: focus on pseudoknots identification

PONE-D-24-52867R2

Dear Dr. Tahi,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Emanuele Paci

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments: