Dear Dr. Hollenbach,

Thank you very much for submitting your manuscript "High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING" 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. Specifically, we would like the issue of comparison to two recently published tools brought by both reviewers to be fully addressed as well as other major concerns raised. 

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[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).

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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,

Ferhat Ay, Ph.D

Associate Editor

PLOS Computational Biology

Jian Ma

Deputy 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: Authors describe a new version of the PING tool for genotyping of killer cell immunoglobulin-like receptor (KIR) genes that are critically important in immunogenomics studies and applications. Authors benchmark PING on many reference datasets and demonstrate its accuracy. While I am positive that this manuscript will present an interest for readers of PLOS Computational Biology, I highly recommend addressing the following comments regarding the description of the pipeline and benchmarking against the existing tools prior to publication:

1. I find the placement of the “K-mer similarity analysis” section confusing. Seems like the purpose of this section is to introduce the definition of shared k-mers that is used for demonstration of high repetitiveness of KIR genes, and the k-mer analysis itself is not a part of the PING tool. If so, I recommend combining this section with the section in Results as the definition of shared k-mers is pretty straightforward and probably shouldn’t take the whole section of the Methods.

2. It is difficult to create the correspondence between pipeline blocks in Figure 1 and sections of the Methods. Could you please add names of blocks from Figure 1 to the titles of sections to make understanding the pipeline easier?

3. Sections describing preprocessing of the KIR gene database and the pipeline are currently intermixed - e.g., the “Copy number determination workflow” section corresponding to the pipeline is located between sections describing manipulations with the database. Since it complicates following the Methods, I recommend making a separate section “Preprocessing the database” and make sections “Imputation of uncharacterized regions and extension of untranslated regions to generate comprehensive alignment reference sequence” and “Designing a minimized reference allele set” its subsections.

4. Line 223: initial genotyping and final genotyping are not introduced before and their purposes are unclear. Are they explained later in lines 274-289? In any case, add a short description of these steps before using them.

5. The overall description of the PING pipeline largely overlaps with the description provided in Norman et al., AJHG, 2016. Please provide a more specific description of the differences from the previous work in addition to one provided in lines 88-98.

6. Could you please comment on using long read technologies for genotyping and haplotyping KIR loci? Considering their relatively small sizes, I can imagine that a 70 kbp locus can be completely spanned by a single Pacbio / Nanopore read. Have you tried to use long read data in addition to short reads?

7. Have you benchmarked the new version of PING against recently released tools by Jieming Chen et al., Briefings in Bioinformatics, 2020 (doi.org/10.1093/bib/bbaa223) and Roe and Kuang, Front Immunol, 2020 (doi.org/10.3389/fimmu.2020.583013)? Please also add citations of these papers to the Introduction.

Reviewer #2: The manuscript describes a new tool for determination genotype and copy number for KIR genes. The tool is of huge interest and beautifully tackled the reference alignment part. It is also impressive to see that the tool is further applied to real-life scenario to see KIR genes copy number in population cohorts.

Strengths:

The manuscript stick to the theme in defining the problem and presenting a viable solution to it.

The underlying codes and reference sequences are made available through github repo.

Comments:

1. The workflow is interesting, however there seems to be no source of ground truth. Since, the authors have validated it in a simulated data, how we can be sure about the accuracy values?

2. While determining the genotypes, the authors are detecting the individual alleles and there are a few tools available for determine alleles in KIR data, the authors have not presented any comparison or references to those tools.

3. It is shown in the Figure 4 that even with 250-mer, few of the genes shared a high percentage with related genes. In such a case where the short read size is around 250 bases, how come any reliable selection of gene could be made?

4. One of the claim in the manuscript is that the reference alignment is improved in the workflow because of using an allele diversity, instead of single gene reference. However, no data is shown for improvement, and whether it is significant.

5. Genotype aware alignment might be carrying forward some biases and there would be issues in working with novel genotypes. Authors have not commented anything on this point.

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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

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Hollenbach,

We are pleased to inform you that your manuscript 'High-throughput Interpretation of Killer-cell Immunoglobulin-like Receptor Short-read Sequencing Data with PING' 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,

Ferhat Ay, Ph.D

Associate Editor

PLOS Computational Biology

Jian Ma

Deputy 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 #2: The authors have positively address the comments raised to them. They have also revised the manuscript to adapt those suggestions/comments. Furthermore, I am glad to see that the authors have acknowledged in the discussion section about possible biases or any limitations of the study or the field because of lack of ground truth data. I am pleased to recommend the acceptance of this revised article.

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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 #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 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 #2: No