We thank our Reviewer for their support of our work and insights. We also value the suggestions, as it helps us improve upon the work and broaden the interest.

We have just published a revised version of the manuscript (v2), which expands on the utility of the maps, situates them in context of other similar work, and introduces new map features to increase interactivity and overall experience.

Some of the maps' new features (since original submission):

Interactivity

Maps are draggable.

Improvements:

Over 120 individual displays, all SARS-CoV-2 genes are now presented.

Improvements 2), 3) and 4) in particular are in response to our Reviewer's comment on spot overlap, and calculating the ratio in such a fashion (instead of log10) enables a better resolution on close-by high-frequency mutations (such as the D614G). Most displays will show missense mutation to minimize display density, but we also offer representations by types (missense vs silent) and all-encompassing. With tooltip, the mutation type is shown as either its effect in amino acid space (eg. N501Y) or silent when the nucleotide variation has no predicted effect.

Authors have developed a web based visualization tool for longitudinal evolution of SARS-CoV-2 genomes.

Although they have made unique representation of longitudinal strain developments, it is not clear the utility of the tool. For instance, while concentric circle representation of daily genomes is visually appealing, it limits the duration to a year and inner part inevitably becomes crowded compared with outer area.

We thank our Reviewer for the valuable insights provided and spending the time to review our work. We acknowledge limitations of the display, and we stress that our original work on this was done in December, on 200,000 GISAID genomes and one year's worth of data. Our preprint became public January 2021 and we subsequently submitted this work to F1000Research, summarizing the 2020 pandemic-associated SARS-CoV-2 variants for year 2020. A circular representation is an aesthetic choice, allowing to get a bird's eye view of the breadth of mutations.  

Lack of interactivity is also an issue. There must have been a way to magnify the area.

This is a great suggestion. We have now added the ability to pan and zoom on each map, making the maps more interactive.

Furthermore, in mutation prone loci, the dots are overlapped and not easy to see what is going on. For these reasons, utility of the tool is limited; more improvements need to be done before it gains large user base.

The maps were first built to visually quantify the appreciable variability that exists in rapidly evolving SARS-CoV-2 genomes. Since, we have added spike-specific views, and variants of concerns (VOCs) to the list of maps available to the community. We also provide the tools to generate the maps, such that advanced users may customize and generate additional views of interest, as needed

We wanted to add to our previous response to our Reviewer. Once again, we are grateful for your suggestions to improve upon interactivity of the maps. Since your Review, we have worked to improve the user experience and we list below some of the new features:

Interactivity

Maps are draggable.

Overall improvements

Over 120 individual displays, all SARS-CoV-2 genes are now presented.

The authors present interactive mutation time maps for SARS-CoV-2, which provide a highly resolving view of when, where and how frequent a particular mutation was detected in the sampled SARS-CoV-2 genome sequences provided via GISAID. The manuscript itself is rather short. It is briefly describing the methodological approach of how the mutations have been detected and mapped to the reference genome. The combined Results and Discussion section is equally concise and comprises a description of what is seen in the interactive maps together with few example observations that can be made with these maps. The Discussion section ends with the expression of the hope that the maps presented here “will help researchers in their exploration of SARS-CoV-2 mutations and their predicted effect over time.”

Overall, the topic that is touched in this manuscript is highly relevant, as variations of SARS-CoV-2 is something that currently is and will be of major concern in the future. Here, the graphs present a very nice access to the information that is represented by the ever-increasing amount of viral genome sequences world-wide. The data presentation is appealing, and it allows to overview the general trends of SARS-CoV-2 evolution. However, we see considerable room for (essential) improvement.

We thank our Reviewers for their comments, suggestions and diligence with their extensive report. Our response can be found below, in bold face

Major issues:

The authors end the manuscript with the belief that the interactive maps will be of help for the research community working on SARS-CoV-2 variation. We miss two things here:

First, it would be great if the authors show how the data provided by the maps can be used to indeed come up with new conclusions, in particular with respect to the ‘predicted effect over time’. For us, it is entirely unclear how such an analysis should be performed. Exploring the data, this is something that one nicely can do while looking at the plots, some clear signals, e.g. the fate of D614G, can also be extracted. But how to work with the data beyond this simple and straightforward ‘looking’ at the plots? Please, don’t get us wrong here, we consider looking at data a very important aspect of data analysis. Still, the sheer amount of information, which results in very dense plots with many overlapping data points, makes it, in our opinion, very hard to identify emerging variants that should be monitored right from the start. Just to give you an example: D614G is represented by a very prominent circle in the plots. What would be the authors approach to identify and monitor a novel variant, say at position 615 of the reference strain? By looking at the plots, we consider this almost impossible, since the signal will be entirely covered by the prominent mutation at position 614.

We greatly really appreciate community feedback on the potential usefulness of this work, and not only the maps, but additional analysis we were able to provide after we submitted the paper (our Reviewers made mentioned of them below), using the wealth of information we were able to mine from the GISAID genomes (these secondary analysis results, which consists of nucleotide variants and their effect, are tallied each week from each individual SARS-CoV-2 genome).  We originally built the maps to be fairly qualitative, to simply gain a [visual] appreciation for the rapid coronavirus evolution on a year scale/factoring sample regions of origin, and this is what we presented in the manuscript. In our conclusion we give an example of a mutation that is observable from the GISAID genomes, on our maps, at the time reported in published papers; Since submission, the GISAID catalogue has more than doubled in size and maps quickly became dense, as our Reviewer indicated. To help remedy the problem and make the maps more useful, we have since started to provide additional genome and spike views of variants of concerns (VOCs) and have added visualizations for 2021 (a more digestible data fraction, indicated below by our Reviewer). Another type of information that can be extracted from the maps is the speed at which mutations in VOCs have appeared and spreading in additional jurisdictions, which can be readily observed without too much effort. Our Reviewers are correct that variations in close proximity are difficult to see, which is why we provide views for the spike-encoding gene. Still, it would be difficult to differentiate between positions 614 and 615, which is why we provide the SVG-generating script such that interested parties would be able to generate custom views should they chose to (Ideally a more flexible website could help, see response below). 

The analysis is presented using the “ground-zero” strain as a reference. But is this still timely? Numerous variants have now frequencies that go far beyond that of the original nucleotide at a certain position, again, for example the D614G variant. This would allow to ‘purge’ the signal of very successful variants, helping to direct the focus on emerging variants.

Our Reviewer is correct that the comparison is relative. When we started this project in December 2020, it made sense to use the "ground zero" strain genome. We could make the case for selecting another set of references to compare against, but it may lead to disagreements in scientific circles, on the base genome sequence to use. Additional maps may be produced in the future to see evolution within each VOCs, which may be an acceptable proposition.

When it comes to the website itself, we see some room for improvement:

First and foremost, we think the plots are overcrowded with information. Although it is nice to see a global overview of the data across the entire genome, 365 days, and 6 continents, it is impossible (at least for me) to explore this information other than randomly clicking individual data points, as we have outlined above. we think, this approach would benefit from providing the information in more digestible data fractions. Thus far, the user can choose to focus on the spike, but not on the other proteins. It would be helpful, just as a suggestion, to focus also on variants with a certain prevalence. But we are sure that the authors will have way better ideas than our proposals here, once they specify how a user should work with the plots and the data. Looking at  https://nextstrain.org, which also provides a very nice overview of SARS-CoV-2 variation, may give some hints.

In the outermost ring, we detected a variant that is assigned to na/na. What is this supposed to mean?

These variants fall in UTR regions. Thank you for the feedback, in our next release of the maps, we will replace NA by UTR to indicate that this nucleotide variant compared to the reference is found outside coding regions. The last position indicates possible effect in the protein space, which is not applicable in this case.