-->PCOMPBIOL-D-25-00973

MPCI: A novel metric for quantifying DNA methylation patterns in NGS data

PLOS Computational Biology

Dear Dr. Mehrmohamadi,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology'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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If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: Hoda Mohammadzade, Naghme Nazer, and Mahya Mehrmohamadi. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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If you did not receive any funding for this study, please simply state: u201cThe authors received no specific funding for this work.u201d

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: DNA methylation abnormality is vital to many diseases. Due to the nature of DNA methylation, which would only occur in CpG sites, focusing on methylation haplotype rather than individual sites can be more beneficial in consistently distinguishing between the healthy and abnormal methylation profiles. The author introduced a new metric MPCI which overcome the disadvantages of current metrics such as MHL and showed more superior performance in distinguishing different cell types as well as better disease detection ability from simulated cell-free DNA.

Major comments:

What is the rationale of summing up the MS(R) and MS(RT)? I understand that the author want to also consider the consistency among different reads. But since the distance for rows represent the similarity among different reads and the distance for columns represent the similarity among the CpG sites, it seems not distinguishing between the difference of the different CpG sites in the candidate region like what MHL does. Is there any biological evidence that can prove that small portions of reads with consecutive CpGs should be considered the same level of methylation the same as the randomly methylated ones? (as the Figure 1 showed)

The Figure 4 is a little cherry-picked to me. Is there any example where the MHL can better distinguish than MPCI for some DMRs?

Also since the definition of MHL and MPCI is different and the range is also different, it seems to be not intuitive and convincing to directly compare the absolute difference of the exact values. MHL is sensitive to the length of the considered region and could be underestimated if there are more NAs and length is large.

When selecting the CpG with different numbers, do they overlap with each other? For a specific region, how to determine what CpG number is the best?

The author described some other metrics than the MHL, have you compared these metrics with MPCI?

Reviewer #2: Nazer et al. developed a simple yet effective metric, MPCI, to quantify DNA methylation patterns in regions where nearby CpGs are concordantly methylated or unmethylated. The authors demonstrated that MPCI outperforms existing metrics, particularly MHL, which only quantifies the level of co-methylation. The manuscript presents interesting findings, but the following issues should be addressed before it can be considered for publication in PLoS Computational Biology:

(1) The schematic plots in Figures 1B and 1C appear to be inconsistent. If M represents mean methylation in Figure 1B, it should equal 1 for the control region. Similarly, beta in Figure 1C should equal 0 for the control region. Please verify and correct these illustrations.

(2) MPCI appears to be mathematically equivalent to (MHL - uMHL). If we define dMHL = MHL - uMHL, how does its performance compare to MPCI? I recommend including dMHL in all comparative figures and providing a scatter plot showing the relationship between MPCI and dMHL.

(3) The authors consistently use 400 randomly selected regions for benchmarking. However, it is unclear how sensitive the results are to region selection. Given that MHL has been shown to perform optimally in MHB regions, could you repeat the analysis in Figure 2 using all MHB regions and compare the performance of MHL versus MPCI? Additionally, statistical significance tests are missing in Figure 2C and should be added.

(4) In Figure 3, since the samples are simulated, the authors should generate multiple independent cohorts (each with 100 healthy and 100 disease samples) rather than repeatedly resampling from a single cohort. This would provide a more robust assessment of performance variability.

(5) In Figure 5B, it appears that dMHL could achieve a similarly high ranking as MPCI. This observation warrants further discussion.

(6) To properly benchmark cfDNA-based cancer detection, the authors should validate their approach using real cfDNA methylation datasets rather than relying solely on simulated data. Several public datasets are available for this purpose, and their inclusion would significantly strengthen the manuscript's conclusions.

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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.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: No: No new data was generated in this study but the authors should make their scripts available.No new data was generated in this study but the authors should make their scripts available.

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Reviewer #1: No

Reviewer #2: Yes: Jiantao ShiJiantao Shi

Figure resubmission:

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Reproducibility:

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PCOMPBIOL-D-25-00973R1

MPCI: A novel metric for quantifying DNA methylation patterns in NGS data

PLOS Computational Biology

Dear Dr. Mehrmohamadi,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology'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 Apr 22 2026 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 ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

* A letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* 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, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

We look forward to receiving your revised manuscript.

Kind regards,

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Reviewers' comments:

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 the previously raised issues.

Reviewer #2: My comments were largely addressed. However, I found that the authors did not describe the tools used in the analysis or the parameters employed. For example, it is unclear how different read-level metrics were calculated; the authors should describe the tools for alignment, read filtering, and coverage thresholds in detail in the Methods section. In addition, the authors should make all their scripts publicly available to ensure reproducible research. Currently, I only see the R function for MPCI calculation.

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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.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: No: The authors should make all their scripts publicly available to ensure reproducible research.The authors should make all their scripts publicly available to ensure reproducible research.

**********

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Reviewer #1: No

Reviewer #2: No

Figure resubmission:

-->While revising your submission, we strongly recommend that you use PLOS’s NAAS tool (https://ngplosjournals.pagemajik.ai/artanalysis) to test your figure files. NAAS can convert your figure files to the TIFF file type and meet basic requirements (such as print size, resolution), or provide you with a report on issues that do not meet our requirements and that NAAS cannot fix.-->-->

After uploading your figures to PLOS’s NAAS tool - https://ngplosjournals.pagemajik.ai/artanalysis, NAAS will process the files provided and display the results in the "Uploaded Files" section of the page as the processing is complete. If the uploaded figures meet our requirements (or NAAS is able to fix the files to meet our requirements), the figure will be marked as "fixed" above. If NAAS is unable to fix the files, a red "failed" label will appear above. When NAAS has confirmed that the figure files meet our requirements, please download the file via the download option, and include these NAAS processed figure files when submitting your revised manuscript.-->

Reproducibility:

To enhance the reproducibility of your results, we recommend that authors of applicable studies deposit 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

Dear Dr. Mehrmohamadi,

We are pleased to inform you that your manuscript 'MPCI: A novel metric for quantifying DNA methylation patterns in NGS data' 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.

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

Best regards,

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

Ilya Ioshikhes

Section Editor

PLOS Computational Biology

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

Reviewer's Responses to Questions

Comments to the Authors: <br/>Please note here if the review is uploaded as an attachment.

Reviewer #2: I have no further comments.

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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.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 published, this will include your full peer review and any attached files.

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Reviewer #2: No