Dear Dr. Wu,

Thank you very much for submitting your manuscript "DeepPL: a deep-learning-based tool for the prediction of bacteriophage lifecycle" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several 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).

Important additional instructions are given below your reviewer comments.

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,

Yang Lu, Ph.D.

Academic Editor

PLOS Computational Biology

Stacey Finley

Section 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: The authors introduce a deep learning tool based on natural language processing techniques to predict bacteriophage lifestyles from nucleotide sequences (DeepPL). This tool uses the DNABERT model, a variant of the popular BERT model adapted for DNA sequences, and demonstrates an accuracy of 94.65%, with sensitivity and specificity rates of 92.24% and 95.91% respectively. DeepPL performs comparably to existing tools and has shown promising results in both lab-verified phage genomes and metagenomic datasets, achieving 100% accuracy on individual phage genomes and varying high accuracies on phage contigs. However, I have several concerns about this work:

1. Figure 1 Clarification: Figure 1 is difficult to interpret. For instance, in the Feature Extraction panel, it's unclear what the annotations P, O, Q, CI represent-are these proteins? Also, does DeepPL require protein inputs? What does the highlighted ACGT sequence signify? In the Model Training panel, what does "12 X" mean? Is it indicating that the input K-mer vectors are processed through 12 Transformer blocks? Are the pre-training and fine-tuning steps using the same Transformer block structures? He, et al [1] mention the use of residual connections to prevent overfitting. It would be beneficial to include it in the Transformer block layer normalization.

2. Dataset Description: It would be beneficial to include a brief description of the datasets in the Results section for better clarify and context.

3. Performance Assessment: The rationale behind using a balanced training set in the 5-fold cross-validation should be explained. Additionally, Table 1 seems redundant and could potentially be omitted for brevity.

4. Method Comparison: The comparison with previously published methods should include a ROC curve to better illustrate the trade-offs between sensitivity and specificity. Also, it would be good if author could explain the low sensitivity observed with PHACTs. The running time comparison of different methods should be included.

5. Case Study 1: Both PhaTYP and DeepPL predict with 100% accuracy for the lifestyle of 18 phages, yet one questionable phage, Sa1791w, presents a controversial lifestyle, making the conclusion that PhaTYP has lower accuracy than DeepPL. The conclusion is unconvincing.

6. Case Study 2: Clarify what “blastn” refers to in the text. Additionally, explain the presence of several NAs in Table 4 and ensure consistency in the formatting of the ‘Sequence length (bp)’ column across tables. The sequencing platform and assembly method should also be included. For Figure 2, include the sample size for each bar and consider using blue/red for better visual distinction as complementary colors.

7. Data Collection Consistency: The authors state that representative lysogenic gene markers were selected, but performance on uncoded genes or a mix (partial coded) would be interesting to see. Otherwise, I feel like no difference between PhaTYP and DeepPL. Discrepancies in the number of lytic and lysogenic samples between the manuscript and Supplementary Data 2 should be addressed.

8. K-mer, sliding window and Learning Rate: Clarify what “k-mer 6 sequences” refers to – is it sequences of six nucleotides? The decision to use a skip step for a balanced training dataset, use 100 bp in sliding window and the relationship between learning rate and epoch should be elaborated on.

9. Minor. At line 111, could you please clarify whether you are referring to the conversion from amino acid sequences to nucleotide sequences?

10. Minor. In the discussion section, consider including references [2, 3], which discuss the use of phylogenetic tree information (highlighting greater similarity among closely related entities) to improve the predictive power and association test power.

[1] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Manhattan, New York, NY, United Stated: Institute of Electrical and Electronics Engineers, 2016, 770–8.

[2] Walkup, J., Dang, C., Mau, R.L. et al. The predictive power of phylogeny on growth rates in soil bacterial communities. ISME COMMUN. 3, 73 (2023). https://doi.org/10.1038/s43705-023-00281-1

[3] Hong, Q., Chen, G. & Tang, ZZ. PhyloMed: a phylogeny-based test of mediation effect in microbiome. Genome Biol 24, 72 (2023). https://doi.org/10.1186/s13059-023-02902-3

Reviewer #2: The authors present a deep-learning tool (DeepPL) based on the previously published DNABERT (Bidirectional Encoder Representations from Transformers for DNA language) model. The goal of DeepPL is to classify phage genomes as either lytic or lysogenic based on their sequence. DeepPL uses the pre-trained DNABERT model and fine-tunes it for the new task in the space of Bacteriophages. The authors demonstrate that their tool performs favorably to four other methods. It is an interesting problem and as we need to improve our understanding of the interactions between viruses and bacteria, I think this work is timely. Overall, I find the paper to be well written, however, I have two major concerns about the way key parameters were selected as discussed below.

Major remarks:

Line 300 “The comparison of different parameters indicated that k mer 6 and sequence size of 100 bp yielded reasonably balanced results and were further used in the current study.“ The comparison is nowhere to be found in the manuscript and no description is given as to what a balanced result is

How did you decide on the size of the skip step for lytic phage genomes? What tests were done to justify a much larger step size of 91? Why should different skip-steps be used to begin with (you can balance the sets at k-mer level rather than gene level)? When you use skip-step=1, you will have many overlapping k-mers, with skip-step=91, none at all. How would this affect the model training and performance? I found the lack of any evidence and discussion to support the choice of (all) your parameters astonishing

You report results based on five fold cross-validation. Yet, in the Data Collection section (line 283) it is stated that “additional 374 complete phage genomes with reliable lifecycle labels were collected from the NCBI database as the test dataset.” So, was it a 5-fold cross-validation or an external testset?

Minor remarks:

It’s unclear in the caption of Figure 1 what threshold 1 is as it is not in the figure.

How was the aggregation of frames done to obtain a score for threshold2?

Line 281: “Therefore, a total of 1,262 virulent phage genomes with the strictly lytic cycle were verified and used to balance the training dataset.” - expand to make clear you balance the number of genes.

Fig2: colors are hard to distinguish

Table3: mark in bold the best performer in each category

Table4: hard to read as column 1 takes two or there rows

Reviewer #3: Uploade as attachment

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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 #1: Yes

Reviewer #2: Yes

Reviewer #3: No: The code and the downloadable data are not found in the manuscript.

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Reviewer #1: No

Reviewer #2: Yes: Borislav Hristov

Reviewer #3: No

Figure 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. 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 us at figures@plos.org.

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Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Attachments

Attachment

Submitted filename: review.pdf

Dear Dr. Wu,

Thank you very much for submitting your manuscript "DeepPL: a deep-learning-based tool for the prediction of bacteriophage lifecycle" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

The authors should address the remaining critiques to strengthen the paper.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

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

[1] A letter containing a detailed list of your responses to all 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).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. 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,

Yang Lu, Ph.D.

Academic Editor

PLOS Computational Biology

Stacey Finley, Ph.D.

Section Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors addressed all my comments.

Reviewer #2: The authors have addressed my comments and have improved the quality and rigor of their manuscript. However, they still need to include some details in the main text. I don't think that responses that point to github scripts, i.e "The script (batch_process_results.py) provided in the GitHub provided the function of adjusting the setting of threshold 2 and generating the error rate of test dataset." are adequate. Please, include in the paper a simple description of how you choose the value of your parameter. The reader of a journal article is not supposed to parse a github script. The justification of using a larger step size of 91 should also be included in the main text.

Reviewer #3: The review is uploaded as an attachment.

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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 #1: Yes

Reviewer #2: Yes

Reviewer #3: None

**********

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

Reviewer #3: No

Figure 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. 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 us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

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.

Attachments

Attachment

Submitted filename: 2nd-review.pdf

Dear Dr. Wu,

We are pleased to inform you that your manuscript 'DeepPL: a deep-learning-based tool for the prediction of bacteriophage lifecycle' 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,

Yang Lu, Ph.D.

Academic Editor

PLOS Computational Biology

Stacey Finley

Section Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have addressed all my comments.

Reviewer #2: The authors have addressed my outstanding comments.

Reviewer #3: The authors have addressed all my questions, I suggest to accept this paper.

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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 #1: None

Reviewer #2: None

Reviewer #3: None

**********

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

Reviewer #3: No