PCOMPBIOL-D-26-00340

Hunting for microsatellite instability in long-read data with Owl

PLOS Computational Biology

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PLOS Computational Biology

Can Yang

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

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Reviewer #1:

please refer Question to author section in attachment.

Reviewer #2:

The manuscript introduces Owl, a computational method for identifying microsatellite instability (MSI) with long read sequencing (PacBio) data. The proposed method is well motivated, and the use of haplotype resolved reads provides advantage to traditional short read methods. In addition, the motif-level and regulatory insights enabled by this framework highlight the added value of long-read sequencing beyond conventional MSI classification. Overall, this method is a valuable contribution to current bioinformatics tools for long-read data.

A few minor revisions would strengthen the validation of the tool:

1. The benchmarking is limited to small set of samples against few tools. Although it is discussed that conventional MSI assays on homopolymers might misclassify sarcoma samples with elevated GGAA repeat instability, it would be great to include a figure panel to illustrate cases where Owl outperforms established tools (if short read sequencing data is available on the same samples) and methods (for example, using canonical marker panels only).

2. Since the method relies on phasing, a breif discussion of how lower sequencing depth and reduced phasing quality affect results would increase the clarity. A comment on applicability to other long-read platforms would be helpful.

3. In line 405-413, parameters (alpha_m, beta_m, k_m and n_m) should be reformatted as subscripts for consistency.

Reviewer #3:

Kronenberg et al created a tool called Owl for detecting microsatellite instabilities using long read sequencing data. While Owl was used over a diverse panel of cell lines and reported some useful results, there are several limitations and unclear design choices. There also appear to be some inconsistencies between Fig 2B and 2C.

Major points:

The set of marker coordinates was derived from RepeatMasker which I think must have been obtained from short read sequencing (SRS). It appears that Owl limits its analysis to those regions obtained by SRS, while in theory it might use the full power of long read sequencing (LRS). It would be interesting if the authors could show (or provide a source that indicates) the marker coordinates derived from RepeatMasker is sufficient. In other words, if they could show that limiting the analysis to those regions defined by SRS, as opposed to defining a marker set based on LRS data, will not be missing some repeat regions with a potential to include MSI.

Fig 2B vs 2C: In Fig 2B, it appears that SNU-1 and TC32 have the highest MSI in terms of the number of % of instable motifs. However, in Fig 2C and as the authors claim in the text, CHLA10 (not SNU-1) and TC32 have the highest number of instable motifs. Is this a mistake or else, how do the authors justify this seemingly inconsistent result?

Fig 2B vs 2C: Please use consistent identifier for SNU-1 and NCC59 in both panels (avoid the NPM identifiers).

The authors devoted a significant part of the analysis to GGAA motif, therefore, they should highlight GGAA (or its representative) on Fig 2B and 2C.

What was Owl’s predictive power (AUROC and/or precision/recall) for detecting MSI based on the literature? While Owl detected five MSI-high samples, it is unclear whether those were the only MSI-high samples or Owl missed some.

The authors compared their LRS MSI calling method to SRS MSI caller (DRAGEN) on only one astrocytoma sample and on only exonic regions. If the authors claim a similar or superior performance of Owl using LRS compared to a SRS MSI caller, they should evaluate more than one sample for which both LRS and SRS are available.

Equally importantly, they could examine WGS regions. This is important as they claimed in the Ewing sarcoma example (and possibly other diseases), MSI can occur in non-coding regulatory regions and therefore, limiting the analysis to exonic SRS data may not produce the full picture (whole-genome and whole-exome results may be consistent, but it should be examined).

Perhaps, a major limitation of this study is the lack of patient data and solely using LRS on cell lines. Are the MSI rates (%) and thresholds applicable in the context of human primary tumor data? Could the authors perform a similar analysis on a limited panel of patient data, or if not, could they provide information as to how this could be addressed when analyzing patient LRS data? It would significantly strengthen the study if Owl is fine-tuned with or run on patient data.

The authors should provide the runtime and system (HPC) configurations used to achieve that runtime and performance.

Did the authors use only one cell line (NA12878) to define the CV threshold of 5%? Why didn’t they use other “normal” cell lines to ensure a consistent CV threshold? This point could be a major limitation and source of variation for real-world LRS clinical data as well as potentially for cell lines. The authors could have a more robust approach of choosing a threshold that does not solely rely on one cell line.

Minor points:

L249: “microstalite” typo

L249: “a intronic” to an intronic

L480: “Singapore: Waiting for Kie-Kyon to reply” to be removed

All figures and specifically Fig 2B: font sizes can be bigger for better readability, especially for Fig 2B x-axis and the legend “%high”

Supp Fig 5: For a clearer visualization, the authors should show “EZH2” gene name on the x-axis (along with a horizontal line showing this is in an intron) similar to what would have shown if we were looking at this in a genome browser such as IGV.

A newer revision of ENCODE cCREs have been published in 2026 and the authors could reference the more recent version, should they decide to do so: https://www.nature.com/articles/s41586-025-09909-9

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Reviewer #1: No: Data availability from Singapore gastric cancer samples is incomplete.

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Dear Mr. Kronenberg,

We are pleased to inform you that your manuscript 'Hunting for microsatellite instability in long-read data with Owl' has been provisionally accepted for publication in PLOS Computational Biology.

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Ruslan Kalendar, PhD

Academic Editor

PLOS Computational Biology

Can Yang

Section Editor

PLOS Computational Biology