QoALa: a comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data

PLOS Computational Biology

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Dominik Wodarz

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PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one review is uploaded as an attachment.

Reviewer #1: The review is uploaded as an attachment.

Reviewer #2: Pamornchainavakul et al develop workflow “QoALa” for viral quasispecies diversity comparison using long-read sequencing data. Evaluating the quasispecies composition is a challenging but an important task. The subject is interesting. The work is original and the visual presentation in the manuscript is impressive. However, there is a fundamental concern in the workflow: The noise-minimization step favors the variant with highest frequency at the position will skew the quasispecies profile.

There are also some other concerns:

1. The workflow has been validated by simulation data and real Sample data. But validation results haven’t been presented. Therefore, we can’t evaluate.

2. The process of how the quasispecies sequence were obtained and how the proportion is calculated is not clear.

3. How the SNV profiles between samples are compared is not clear.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: reviewing.pdf

Dear Dr. Pamornchainavakul,

We are pleased to inform you that your manuscript 'QoALa: a comprehensive workflow for viral quasispecies diversity comparison using long-read sequencing data' has been provisionally accepted for publication in PLOS Computational Biology.

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Charlotte K. Y. Ng

Academic Editor

PLOS Computational Biology

Dominik Wodarz

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 have provided clear answers to all my questions and requests for clarification.

I recommend accepting the revised manuscript for publication.

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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.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

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Reviewer #1: No