PONE-D-23-06543RNAinsecta: A tool for prediction of precursor microRNA in insects and search for their target in the model organism  Drosophila melanogaster.PLOS ONE

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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

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: No

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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

Reviewer #3: 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

Reviewer #3: 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: Authors present a study of models for pre-miRNA identification specifically on insects. I found it to be a well-written and informative, and I believe that your tool will be of great use to researchers working with insects. I also found it resourceful the use of miRNA target prediction as a proxy to detect possible pre-miRNAs.

However, I have a couple of points that I think could be further addressed.

- Firstly, it is mentioned that there are deep learning-based methods such as mire2e for predicting precursor microRNAs. Have you tried implementing these methods in your framework and compared their performance with your current approach? I do not intend to add too much burden to the already extensive tests performed for this work, but there are lots of recent tools to explore pre-miRNAs that improved the ones mentioned in the tables.

- I understand that the high reported accuracy is result of a balanced test set. While this is useful for assessing the recall of the model, it may not give a complete picture of the precision of the tool. Have you considered how will the model be used? for example, when searching thousands of sequences in search of pre-miRNAs, which will be the false positives rates?

- There are tools to assess if model have a hairpin-like structure. This may be helpful to improve negative dataset, as both human pre-miRNAs and coding RNA are very different to the actual pre-miRNAs.

Overall, I think RNAinsecta makes a valuable contribution to the field, so I hope this suggestions helps to improve the manuscript.

Reviewer #2: In this study, Nath et al. developed a novel tool for specially predicting precursor microRNA of insects. This study is very solid and the tool developed deserves further consideration for the related researchers. Although many details have been considered well, some obvious defects should be addressed in detail. (1) For example, as everyone knows, the performance of prediction is dependent on construction of negative samples. The more difference between positive and negative samples, the better of performance. The authors should give an intuitive comparison between positive and negative samples. (2) To further improve the accuracy of target prediction, the more tools for target prediction should be used. Then, the intersection of predicted results from tools are provided back to users. (3) In general, the ROC curve is very smooth. But as depicted in Fig. 2, the authors should clarify why. Suggest the authors use more datasets to do comparison of performance between different tools.

Reviewer #3: The authors of the manuscript entitled “RNAinsecta: A tool for prediction of precursor microRNA in insects and search for their target in the model organism Drosophila melanogaster” describe a microRNA discovery model for insects. They make use of and combine previously reported features to train various models on known insect miRNA and derived pseudo insect miRNA. The authors briefly discuss how microRNAs in insects have distinct differences from human, mouse, and plant microRNAs, specifically, MFE and GC%. They report that due to these differences, published methods perform poorly on insects. The authors put forth two models from their experimentation, an SVM and a random forest model trained on a SMOTE-balanced dataset. The model’s hyperparameters were tuned using random search, grid search, and 10-fold cross validation.

The authors clearly describe the features used and their methodology for hyperparameter tunning. However, some clarification can especially to the introduction and discussion to further improve the understanding of the manuscript.

Major points:

- The authors estimate the generalizability of their classifiers on an artificially “balanced” dataset. In practise the ratio of positive:negative miRNA would not be balanced. In fact, the class imbalance for a sequence-based miRNA discovery method can be as high as 1000 negatives for each positive miRNA, when considering all hairpins in a genome that look like a pre-miRNA. The authors should report performance on a “naturally imbalanced” test set, reflecting the realistic deployment of the predictor to an entire insect genome. Prevalence-corrected precision can be used to estimate performance at different class imbalance levels, for example.

- The website predicts possible targets for the miRNA inputted by users; however, the prediction seems to be made between the pre-miRNA and mRNA. It has been reported in literature that mature miRNA target mRNA, and not pre-miRNA.

- The ROC curves reported appear to be incorrect since they only contain three points (bottom left, some mid-point, and top-right). Instead, ROC curves should be more smooth, illustrating the achievable TPR and FPR for many different decision thresholds. Perhaps the Python sklearn “predict” function was used to predict binary classes “0” or “1” instead of “predict_proba” that produces a prediction confidence between 0 and 1. The graphs should be recomputed along with summary statistics, such as AUC-ROC.

Minor points:

- The authours should review the consistency of acronyms in the document. For example, “pre-mirna” and “pre-miRNA” are both present in the manuscript.

- It is not clear in the methodology if the SMOTE and/or NM was applied to the X_test or V_test datasets. Considering that the application of those methods would constitute a methodological issue, it would be best if that distinction was made abundantly clear. This relates to the major issue of testing on “artificially balanced” test sets above.

- The authors consider many metrics to estimate performance, most of which are not wholly suitable for representing model performance in the presence of class imbalance. The authors do consider MCC, but few discussions are made on those results. Additional discussion of the different models performance based on MCC, F1 measure, or prevalence-corrected precision should be added to the manuscript

o Additionally, the authors should consider reporting Area under the precision recall curve (AUPRC), when also using the prevalence-corrected precision, as it is representative of the performance of classifiers on “naturally imbalanced” datasets.

- The authors report the performance of a KNN model architecture among others. Was a consideration made to normalize the features as it has a significant effect on the performance of KNN model, especially if the feature has a variety of ranges?

- It would be beneficial in the introduction to include more discussion of the difference between insect miRNA biogenesis and animal/plant miRNA biogenesis. Especially, since the foundation of the motivation of the manuscript is that miRNAs from insects are very different from animal and plant miRNA and thus require their own microRNA discovery predictor.

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Reviewer #1: Yes: Leandro Bugnon

Reviewer #2: No

Reviewer #3: No

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RNAinsecta: A tool for prediction of precursor microRNA in insects and search for their target in the model organism  Drosophila melanogaster.

PONE-D-23-06543R1

Dear Dr. Bora,

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,

Abu Sayed Chowdhury, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for addressing reviewers' questions, comments, and suggestions.

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

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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 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

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 #1: Yes

Reviewer #2: Yes

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 #1: All my suggestions were addresed. As suggested by the authors, it may require to adjust parameters of mire2e to adapt to this kind of pre-miRNAs (the model has to be retrained and evaluated, it is a complete new work).

Great work!

Reviewer #2: (No Response)

Reviewer #3: The authors have addressed all my comments adequately. It is unfortunate that they have chosen not to illustrate their model's performance using ROC curves, even if the methods against which they are comparing have chosen to only output binary prediction scores. Continuous prediction scores (i.e., prediction and confidence indicator) can be used to create ROC curves and provide valuable information for potential users.

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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: Yes: Leandro Bugnon

Reviewer #2: No

Reviewer #3: No

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