PONE-D-21-31782NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profilesPLOS ONE

Dear Dr. Eslahchi,

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles

Summary:

The authors present NN-RNALoc a new machine learning classifier for the prediction of mRNA subcellular localization. While there have been multiple previously published classification models for this task, the authors incorporate two novel sets of features, protein-protein interaction networks and distance-based subsequence profiles. The incorporation of these new features results in small improvements over previous methods. The improvements in classifier accuracy, while small, are noteworthy accomplishments. However, one key area of improvement for this manuscript would be to show some biological insights their new model can identify that previous classifier cannot. As it stands now, all of the figures in the results section are benchmarking tables. Benchmarks are crucial when developing a new model but so are biological insights, which this paper is currently lacking.

Comments:

1. The text has many grammatical and spelling errors, which need to be addressed to be able to better appreciate the work presented. For instance, “dimension” and “distance” are misspelled on line 57 and the Figure 2 legend, respectively.

2. The authors spend a considerably large amount of text on the introduction which spans, 144 lines of text, over 2 pages even enumerating machine learning guidelines from a previous study. I would recommend the introduction be condensed into more concise text which would make the transition to methods and results smoother.

3. Why is a distance-based sub-sequence of k=2 optimal, why not larger values? It seems like k=2 is capturing information already present in the k-mers counts and would be interesting to hear the authors discuss their methodology for selecting k=2.

4. In table 3 and 4, two benchmarks are performed, however, the authors utilize two different metrics for evaluation. Table 3 is correlation based while table 4 uses the standard multi-class accuracy metrics. This is slightly confusing because they are all performing the same classification tasks, the metrics used should be the same between benchmarks to enable better comparisons.

5. The authors state the advantages of their distance-based sub-sequence profiles many times but do not directly quantify their benefits. It would be informative for the authors to create a new model only using k-mers then they can compare the accuracies of this model to the NN-RNALoc(noPPI) model to directly estimate the effects of their new distance-based sub-sequence profiles. This would allow the visualization of the increases in accuracy from k-mers, PPI and distance-based features.

6. The utilization of novel features to improve classifier accuracy is very interesting, however it would be equally intriguing to see why these features increase accuracy. For example, what are the most informative distance-based subsequence profiles for each subcellular location? Are some of these, or their respective k-mers enriched for RNA-binding motifs? In addition, are certain subcellular locations enriched for certain protein-protein interactions? I would recommend adding a figure exploring these questions.

Reviewer #2: Authors proposed a deep learning framework for mRNA sub-cellular locations prediction. Authors have proved that information of proteins assists model to predict sub-cellular locations more precisely. The paper seems interesting and will be helpful for biomedical researchers.

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Reviewer #1: No

Reviewer #2: No

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PONE-D-21-31782R1NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profilesPLOS ONE

Dear Dr. Eslahchi,

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 Dec 21 2022 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.

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We look forward to receiving your revised manuscript.

Kind regards,

Nguyen Quoc Khanh Le

Academic Editor

PLOS ONE

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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 #3: (No Response)

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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 #3: Partly

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

Reviewer #3: No

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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 #3: Yes

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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 #3: Yes

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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 #3: In this study, the authors developed an ANN based computational model for localization prediction of mRNA. Following are my major concerns that need to be addressed before acceptance.

1. The authors should cite the existing work on mRNA localization. Following articles must be cited.

Asim, M.N., Ibrahim, M.A., Malik, M.I., Zehe, C., Cloarec, O., Trygg, J., Dengel, A. and Ahmed, S., 2022. EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction. Computational and Structural Biotechnology Journal.

Meher, P.K., Rai, A. and Rao, A.R., 2021. mLoc-mRNA: predicting multiple sub-cellular localization of mRNAs using random forest algorithm coupled with feature selection via elastic net. BMC bioinformatics, 22(1), pp.1-24.

Wang, D., Zhang, Z., Jiang, Y., Mao, Z., Wang, D., Lin, H. and Xu, D., 2021. DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism. Nucleic Acids Research, 49(8), pp.e46-e46.

Zhang, Z.Y., Yang, Y.H., Ding, H., Wang, D., Chen, W. and Lin, H., 2021. Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Briefings in Bioinformatics, 22(1), pp.526-535.

Yan, Z., Lécuyer, E. and Blanchette, M., 2019. Prediction of mRNA subcellular localization using deep recurrent neural networks. Bioinformatics, 35(14), pp.i333-i342.

2. The authors compared the accuracy with only two existing tools such as RNATracker an mRNALoc. The other tools (mentioned in comment 1) should also be considered to claim the superiority of the NN-RNALoc.

3. There are several shallow learning (SVM, Random forest, XGBoost, LightGBM etc.) and deep learning models are available. The performance of ANN (used in this study) should be compared with these methods as well.

4. The NN-RNALoc can predict an mRNA to any one localization. However, it is the very fact that a single mRNA could be present in more than one location. So, how the proposed study will address this problem?

5. The area under receiver operating characteristics curve (AU-ROC) and precision-recall curve (AU-PRC) should be included in the performance metrics.

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Reviewer #3: No

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PONE-D-21-31782R2NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profilesPLOS ONE

Dear Dr. Eslahchi,

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 revise the manuscript according to the comments raised by the reviewers.

Please submit your revised manuscript by Jun 22 2023 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,

Suyan Tian

Academic Editor

PLOS ONE

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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.

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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 #4: All comments have been addressed

Reviewer #5: (No Response)

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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 #4: Yes

Reviewer #5: Yes

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

Reviewer #4: Yes

Reviewer #5: Yes

**********

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 #4: Yes

Reviewer #5: Yes

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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 #4: Yes

Reviewer #5: Yes

**********

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 #4: The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence repres. This work is meaningful in this field. This work can be accepted.

Reviewer #5: 1.Most figures presented in the paper are pixelized. They can be converted to vectorized ones to improve the resolution.

2.The "Evaluation criteria" section should be placed in the Materials and Methods section instead of Results.

3.The tables in the paper are not using standard three-line tables. Please use three-line tables instead.

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Reviewer #4: No

Reviewer #5: No

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NN-RNALoc: neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles

PONE-D-21-31782R3

Dear Dr. Eslahchi,

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,

Suyan Tian

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All comments raised by the reviewers have been addressed perfectly, the manuscript is acceptable for being published by the journal.

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