iMutSig: a web application to identify the most similar mutational signature using shiny

There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows comparisons of pairs of mutational signatures to be easily performed. Cosine similarity measures the extent of signature similarity. To compare mutational signatures of different formats, one signature type (COSMIC or pmsignature) is converted to the format of the other before the signatures are compared. iMutSig provides a simple and user-friendly web application allowing researchers to download published mutational signatures of either type and to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download from GitHub.


Introduction
Each human is subject to a variety of mutational processes throughout their lifetime. These processes result in a catalog of somatic mutations in the tissue creating a unique mutational profile 1 . A mutational signature captures the pattern of the mutations and contexts in which those mutations occur (i.e., the neighboring bases). Examples of important mutational processes with distinct mutational signatures include aging and ultraviolet (UV) radiation. Additionally, many research groups are performing analysis to discover de novo mutational signatures in cancer [1][2][3][4] .
Currently, there are two frameworks used to characterize and visualize mutational signatures 5,6 . The first, proposed by Alexandrov et al., uses a vector of 96 probabilities to capture the composition of the six nucleotide substitutions (C >A, C >T, C >G, T >A, T >C, T >G) and the neighboring base immediately on each of the 5′ and 3′ side of the mutated base 1  proposed a mixed-membership model, pmsignature, which substantially reduced the number of parameters needed to characterize a signature 9 . They achieved this by assuming independence across bases, thereby reducing the number of parameters from 6*4*4-1 = 95 to (6-1)+(4-1) +(4-1) = 11 9 . The reduction in the number of parameters is greater if more flanking bases are included. However, the independence assumption might prevent signatures with dependent neighboring bases from being discovered, thereby resulting a fewer signatures. Shiraishi identified 27 signatures, all of which can be downloaded from their GitHub repository 9 . In this paper, we will refer to signatures resulting from these two methods as "COSMIC signatures" with version numbers (for those resulting from Alexandrov et al.'s method) and "PM signatures" (for those resulting from Shiraishi et al.'s method).
A large number of researchers have published scientific findings resulting from the COSMIC signature-based method 10-12 , which was defined as the "gold standard" in the field by Baez-Ortega et al. 6 . Meanwhile, an increasing number of researchers are using the pmsignature-based method for samples with lower numbers of somatic variants due to it requiring fewer parameters 9,13,14 . Given that both methods are widely used, investigators need the ability to compare results from their analysis with those reported in earlier databases, which may have been produced using the alternate method. For example, researchers have adopted both tools for gastric cancer and tried to compare and integrate the information from two data sources in a somewhat ad hoc manner 15 . No rigorous tool exists for this task. In this paper we present iMutSig, an easy-to-use tool that allows users to 1) input a new mutational signature, 2) compare it using cosine similarity to all published signatures from both the COSMIC and PM signature databases, 3) identify the most similar signatures previously reported, and 4) to assemble the information characterizing those signatures using simple point-and-click navigation.

Implementation
In order to measure the similarity between mutational signatures across two databases, we need to represent PM signatures in a way that is comparable with those from COSMIC, or represent COSMIC signatures in a way comparable to PM signatures. We call the first of these methods the "expand" method, where we expand the PM signature into a probabilistic vector with the same length as the COSMIC signature, i.e., 96. The conversion in the opposite direction, from the COSMIC signature into the PM signature format is called the "collapse" method. In the collapsed format, the PM signature is represented by a vector of 14 probabilities, the probabilities for the six possible nucleotide substitutions and the probabilities for the four possible bases at each of the two flanking base positions. In the "expand" method, to calculate each of 96 resulting probabilities in the vector, we take the constituent components that make up the COSMIC signature -which refer to the nucleotide substitution and two flanking bases at the -1 and +1 position -calculate the

Amendments from Version 1
We thank the reviewers for their insightful comments. Two major changes have been made to the paper which we believe have improved it significantly.
The first change is that we updated the version of COSMIC signatures from version 3 to version 3.1, announced in June 2020 as the most recently released signatures. The other change was made based on reviewer 2's comment on including another conversion method. Reviewer 2 suggested that we ' collapse' the COSMIC signature to marginal probabilities which are then multiplied together under the independence assumption before comparing the COSMIC to PM signature. We implemented the new method in the Shiny app, introduced it in the Methods section and provided new Results. Now users are able to choose either of these conversion methods (new ' collapse' or original ' expand') to identify the most similar signature of the opposite type. In addition, a new tab featuring heatmaps was implemented to provide an interactive visualization of the cosine similarity between two types of signatures. Cosine similarities are computed after converting one of the signature types to match the format of the other. In addition, we discussed the discrepancy that can arise in identifying the most similar signature of the opposite type, depending on which conversion method is selected (' collapse' or ' expand').
Based on the reviewers' feedback, we have also made a few minor changes including 1) adding a new figure to illustrate how to convert between two types of signatures; 2) correcting the typos in the formula, text, and Shiny app user interface; 3) updating a reference and the Shiny app user interface accordingly.
Any further responses from the reviewers can be found at the end of the article REVISED These are computed using the convertAlexandrov2Shiraishi function from the decompTumor2Sig package 15 . probability of each component for the given PM signature, and then multiply those probabilities using PM signature's assumption of independence. For example, to calculate the probability of the COSMIC signature C[C >A]T we multiply three PM signature's probabilities: P(C at pos -1), P(C >A), and P(T at pos +1). This example is shown in Table 1, Equation 1, and Figure 1.
To perform the "collapse" method, we calculate the marginal probability for each characteristic, the nucleotide substitution and each flanking base, and multiply the probabilities together using the independence assumption. The marginal probability for the nucleotide substitution is computed by summing the probabilities including all 16 combinations of two flanking bases from the COSMIC signature. In a similar manner, the marginal probability of a flanking base is the sum of probabilities across all signatures containing the given flanking base. See an example of P(C>A) and P(C at pos -1) shown in Equation 2: ( at pos 1) After we have represented both forms of signature using probabilistic vectors of the same length n, P and C say, we can directly compare the two signature types. In order to measure the similarity between them we use cosine similarity, CS, defined as shown in Equation 3: Intuitively speaking, cosine similarity is the cosine of the angle between the two vectors. As such, cosine similarity ranges from 0 to 1 (inclusive). In our context, if two mutational signatures have a cosine similarity of 1, they must be identical, i.e., the angle between them is 0°; in contrast, if two mutational signatures have a cosine similarity of 0, they are maximally dissimilar (i.e., orthogonal). Computing the cosine similarity between the input signature and each of the candidate signatures, and then sorting the similarities from highest to lowest value, we identify the candidate signature with the highest cosine similarity as the most similar mutational signature.
Operation iMutSig is built in R with its key features depending on the R package, pmsignature 9 . As shown in Figure 2, the Shiny app currently supports three possible workflows for users to choose from, depending on the type of signatures they have already obtained: 1) starting with a COSMIC signature; 2) starting with a PM signature; 3) starting with a self-defined signature that could follow either the COSMIC or PM format.
The first tab in the Shiny app window, "COSMIC to pmsignature", allows users to select an input COSMIC signature via a Figure 2. Overview of three workflows in the iMutSig interface. The first two tabs allow users to finding the most similar PM signature to an input COSMIC signature (highlighted in green) and vice versa (highlighted in orange). In addition, users can identify the most similar signatures from both data sources to an input signature (highlighted in blue).
drop-down list and returns the best-matched PM signature. The returned results are divided and organized separately in the top and the bottom portion of the page. The top half tab summarizes background information regarding the input signature by presenting: 1) visualized plots of the input signature and its membership among all cancer types, i.e., in which kind of cancers the mutational signatures has been found; 2) a table showing the cosine similarity between this signature and all PM signatures, sorted in decreasing order, along with a visualization of a similarity heatmap with color and intensity proportional to assessed similarity. The bottom half tab presents plots and descriptions of the input COSMIC signature, the most similar PM signature, and a second PM signature that the user can select. Thus, users can easily access all the vital information and results regarding these signatures rather than having to manually gather and organize information from publications. The top half of the tab will be automatically updated via a control panel in the middle section of the tab, which enables users to select a signature to start with and also highlights information about the currently selected signature, the most-similar signature from the alternate model framework, and the cosine similarity.
The second tab was designed in a similar manner to the first tab, but for the case in which we are starting with a PM signature and looking for the most similar COSMIC signature. For the first two tabs, users can choose which version of COSMIC signatures to input from the sub-menus, i.e., v2 or v3.1.
Unlike the first two tabs, the third tab enables users to enter a user-supplied signature, which can be in either PM or COSMIC format, and then identify the most similar signature from each online database. The user will be requested to enter a sub-menu based on the type of the input signature and to upload a commaseparated values (CSV) file containing a single signature. A sample CSV file is provided for download to give the user a better sense of the format of the input file. Then, the tab will be updated to display three tables, one from each data source (COSMIC v2, v3.1 and PM), listing the signatures from that data source and the cosine similarity of each signature with the user-uploaded signature. The tables are ordered from most similar to least similar signature. In addition, the user is able to view figures of the best-matched signatures (i.e., those with highest cosine similarity) from each data source, allowing users to observe any similarities and dissimilarities. Below, users will see a list of cancer types that contain the best-matched signature.
The fourth tab shown in Figure 3 displays the interactive cosine similarity heatmaps between PM signatures and COSMIC signatures for the two conversion methods. One would choose the version of COSMIC signatures (v2 or v3.1) and one of the two conversion methods (COSMIC to PM signature, 'collapse', or PM signature to COSMIC, 'expand'). The PM signature, the COSMIC signature names and the associated cosine similarity value can be visualized by placing the cursor over the heatmap. It is notable that the cosine similarity values tend to be higher using the collapse representation compared to the expand representation.
We attribute this to the difference in model assumptions. When a COSMIC signature is collapsed to the PM signature format the independence assumption is imposed on both signature types. However, when a PM signature is expanded to the COSMIC signature format, the PM signature probability vector still represents the fit under feature independence whereas the COSMIC signature does not. This difference in model assumptions results in lower estimates of cosine similarity. Some discrepancies are found, based on the conversion method selected, when searching for the most similar signature from the opposite database: matching COSMIC v3.

Use cases
We use iMutSig to identify the most similar signature for a given PM/COSMIC signature or a user-supplied signature. Figure 4 shows the input panel after inputting COSMIC v3.1 signature SBS1 and Figure 5 shows the input panel after inputting PM signature P1. If users provide a user-supplied signature of either COSMIC-kind or PM-kind, the results can be seen in Figure 6 and Figure 7. Consider the example shown in Figure 6, where we input COSMIC v2 signature C1. iMutSig returned the most similar signatures COSMIC v3.1 signature SBS1, and PM signature P7 (similarity = 0.947, and 0.948, respectively) along with the names of its associated cancer types. When providing PM signature P1, iMutSig returned COSMIC v2 signature C10, v3.1 signature C10a and PM signature P1 (similarity = 0.816, 0.957, 1.0, respectively).

Conclusions
iMutSig is a user-friendly interactive browser-based application that allows users who have a signature that they have discovered in an analysis of their own data to identify the bestmatched existing mutational signature from the COSMIC and PM databases. It also allows users to directly compare signatures between the two databases. It does this in an interactive way, and also allows straightforward visualization of results. iMutSig enables researchers to easily identify the most similar mutational signature and to easily access characteristic information from both data sources without additional software installation and programming of their own.

Data availability
All data underlying the results are available as part of the article and no additional source data are required.

Software availability
Software 1.

2.
In Implementation, par. 1, when discussing the "expand" method whereby a COSMIC signature is constructed from three of the probabilities in the input PM signature, perhaps it would be good to mention that the PM signature may contain more features than just substitution type and immediate 5' and 3' bases (such as probabilities for -2/+2 bases and transcriptional strand), and that any information about these extra features of the PM signature is lost when "expanding" to the COSMIC format. Adding this comment might be valuable since the example in Table 1 does contain these extra features, and it would also highlight one of the strengths of PM signatures (more information in less parameters).

3.
Implementation, par. signature would be a vector of 96 such mutation types/categories.
The comment above also applies to the next paragraph, which reads: "In a similar manner, the marginal probability of a flanking base is the sum of probabilities across all signatures containing the given flanking base."

5.
Implementation, par. 2 reads: "To perform the "collapse" method, we calculate the marginal probability for each characteristic, the nucleotide substitution and each flanking base, and multiply the probabilities together using the independence assumption". Are you sure that the probabilities are multiplied together in the "collapse" method? As far as I understand, each marginal probability is obtained as a summation (as shown in Equation 2), and no multiplication is required afterwards -if I am correct, these marginal probabilities already define the PM signature.
Operation, par. 5 reads: "It is notable that the cosine similarity values tend to be higher using the collapse representation compared to the expand representation. We attribute this to the difference in model assumptions. When a COSMIC signature is collapsed to the PM signature format the independence assumption is imposed on both signature types. However, when a PM signature is expanded to the COSMIC signature format, the PM signature probability vector still represents the fit under feature independence whereas the COSMIC signature does not." While I understand this, I am not sure this is the only reason for the difference in cosine similarities. In general, one would expect high cosine similarity values to become less frequent as the number of dimensions increases; in other words, it is much less likely to find two 96-dimensional vectors with a near-zero angle between them than it is to find two 11-dimensional vectors with a similar angle. So it is possible that the mere fact of collapsing a COSMIC signature into a PM signature causes an overall increase in its cosine similarity with every other PM signature (and the opposite would be true when "expanding" signatures). However, I do not know which of these two explanations is more important for the change in similarity values observed in the signature heatmaps.

Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Computational biology, Bioinformatics.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
Reviewer Report 26 November 2020 https://doi.org/10.5256/f1000research.30641.r75097 © 2020 Perduca V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Vittorio Perduca
Université de Paris, CNRS, MAP5 UMR 8145, F-75006, Paris, France Thank you for taking into account my comments and for clarifying the differences between the original "expand" method and the new "collapse" method. I have no further comments.

Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Applied statistics, biostatistics.

Version 1
Reviewer Report 15 July 2020 https://doi.org/10.5256/f1000research.26954.r64570 © 2020 Perduca V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Vittorio Perduca
Université de Paris, CNRS, MAP5 UMR 8145, F-75006, Paris, France This paper presents an original online tool for comparing mutational signatures represented according to two alternative formats, namely COSMIC vectors with the relative frequencies of the 96 types of substitutions on one side 1 , and lower dimensional "pmsignature" vectors on the other side 2 . The article is well written and the method behind the tool is clearly explained. The interactive tool runs smoothly and has the potential to provide useful support to researchers running mutational signature analyses using alternative frameworks.

My comments:
One important point worth stressing is that the proposed solution for comparing "PM signatures" and "COSMIC signatures" is to represent the former lower dimensional probabilistic vectors in the larger space of the latter vectors. This is clearly explained in the methods, but I believe it is worth mentioning explicitly that this happens even when the input is of the COSMIC type.
○ My major concern is that the described method relies on the pmsignature assumption of independence between the mutation features. Could the authors comment about the possible limitations entailed by this assumption?

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Partly Competing Interests: No competing interests were disclosed. This paper presents an original online tool for comparing mutational signatures represented according to two alternative formats, namely COSMIC vectors with the relative frequencies of the 96 types of substitutions on one side 1 , and lower dimensional "pmsignature" vectors on the other side 2 . The article is well written and the method behind the tool is clearly explained. The interactive tool runs smoothly and has the potential to provide useful support to researchers running mutational signature analyses using alternative frameworks.
My comments: One important point worth stressing is that the proposed solution for comparing "PM signatures" and "COSMIC signatures" is to represent the former lower dimensional probabilistic vectors in the larger space of the latter vectors. This is clearly explained in the methods, but I believe it is worth mentioning explicitly that this happens even when the input is of the COSMIC type.

Response:
We now summarize explicitly the tasks the software performs at the end of the introduction: "iMutSig, an easy-to-use tool that allows users to 1) input a new mutational signature, 2) compare it using Cosine similarity to all published signatures from both the COSMIC and PM-signature databases, 3) identify the most similar signatures previously reported, and 4) to assemble the information characterizing those signatures using simple point-and-click navigation." My major concern is that the described method relies on the pmsignature assumption of independence between the mutation features. Could the authors comment about the possible limitations entailed by this assumption? ○ Response: We are not recommending one method over another, but are providing a means for comparing signatures that are estimated under the different modeling assumptions. One can transform the format of either signature type to the format used by the other signature type (PM signature to COSMIC or COSMIC to PM signature). In the original submission we only considered expanding the PM signature to the COSMIC format. In this revision we have added the capability of collapsing the COSMIC signature to the PM signature format (see response to next comment). These are not symmetric activities and can lead to differences in the identification of most similar signature from the opposite model. The new method of 'collapsing' the COSMIC signature is described in the section: "Methods/Implementation". The consequences for identifying the most similar signature of the opposite type are described in the last paragraph under "Methods/Operation". In my understanding, another possibility would have been to represent "COSMIC signatures" as 11-dimensional "PM signatures" by eliminating through summation two features out of three. For instance, one could compute the "PM signature" component representing the probability of the substitution S=[C>A] as P(S = [C>A]) = sum_{l, r} P(L=l, S=[C>A], R=r), where L and R denote the -1 and +1 flanking bases, and P(L=l, S=[C>A], R=r) is one of the 96 probabilities in the input "COSMIC signature". This approach does not rely on the pmsignature assumption of independence. (This solution does not make it possible to consider "PM signatures" with more than two flanking bases, but in any case the information about such extra bases is lost when converting "PM signatures" to "COSMIC signatures" using the method described in the paper). Have the authors explored this other method? A comment on this point could possibly help clarifying the reason why the authors have decided to rely on the independence assumption. Response: Thank you for this suggestion. As we noted in our response to your last point, we have added this additional comparison to the app using a function implemented in the decompTumor2Sig package. Interestingly, the cosine similarity values tend to be higher using the above method. We interpret this in the paper as follows: "When a COSMIC signature is collapsed to the PM signature format the independence assumption is imposed on both signature types. However, when a PM signature is expanded to the COSMIC signature format, the PM signature probability vector still represents the fit under feature independence whereas the COSMIC signature does not. This difference in model assumptions results in lower estimates of cosine similarity." (section Methods/Operation).
I suggest to add a figure with the heatmap showing the cosine similarity between the original 27 "PM signatures" and the 30 v2 "COSMIC signatures". This would help understanding the type of correspondence between the two databases.

Response:
The heatmap is now added to the Shiny app and the manuscript.
are widely used, but are remarkably different in their visual aspect, making intuitive comparisons difficult. To my knowledge, this is the first openly available method for comparison between signatures expressed in these two alternative representations.
The methods implemented for conversion between signature representations and for comparison between signatures are straightforward and based on a widely used similarity measure; although the main formula in the paper is incorrect, this mistake does not appear to extend to the implementation. Instead of simply reporting the most similar signature to the chosen signature, the tool provides information about the similarity of the chosen signature to all the signatures available in the alternative representation, allowing better assessment of the results. The user interface is thoughtfully and tastefully designed, making the tool both easy and pleasant to use. All the offered functionalities appear to work correctly and the platform runs smoothly. The authors provide their tool as an interactive website, as well as current and archived versions of the source code. However, there is a lack of information about how to install and run the software locally as an R package, which would increase the long-term usability of the tool.
Below I provide comments regarding major and minor issues in the article and online tool. I also provide a few optional suggestions that may be safely ignored, but which I think would enhance the functionality of the tool.
Please ammend this formula, and make sure that it gives the same values as the formula you have defined in your code (function getCosDistance).

MINOR COMMENTS
2. Introduction, paragraph 1: This is somewhat inaccurate, in the sense that it is the cells in an organism's tissues that are exposed to mutational processes throughout the organism's life, and each cell or tissue develops its own mutational profile. The existence of a "unique mutational profile" thus may be better described as a property of a tissue, organ or tumour: it is not really accurate to say that each human has "his/her unique mutational profile", as this varies widely across tissues (e.g. cells in the blood, skin, liver, and colon have very different mutational spectra), and the differences among individuals also tend to be tissue-specific.
3. Introduction, paragraph 3: in the sentence where the motivation for PM signatures is mentioned ("due to it requiring fewer parameters"), it might be appropriate to add a brief note that the assumption of independence between substitution type and flanking bases also limits the representation of patterns where these features are dependent, although this is seen in relatively few COSMIC signatures (e.g. SBS8, SBS25, SBS35).

4.
Implementation, paragraph 1: it would be useful to complement the example in Table 1 and Equation 1 with a figure that shows the original PM signature in Table 1, and the equivalent COSMIC signature that results from applying Equation 1 to each of the 96 substitution types. This would help the reader to understand the conversion between both graphical representations, which is shown in later figures.

6.
Note that reference 6 is missing a colon between the author list and title.

7.
The "About iMutSig" web page states that "On the Github page, you can: -install the iMutSig R pacakge and run it locally." However, on the GitHub page I found no instructions on how the package can be installed and run locally using R and Shiny. While I understand that the main purpose of the platform is to be an online tool, some users may be interested in having a local copy. For example, the availability of the tool seemed somewhat variable: I was able to access the website (https://zhiyang.shinyapps.io/imutsig/) on 17 June, but not on 18 June. Although this might be a rare issue, it highlights the advantage of providing users with an alternative way of accessing the tool in the long term. For example, some simple steps for installation and running could be added as a README.md file on the GitHub repository.
8. Note that, in the platform interface, some of the signature names read "COSIMIC" instead of "COSMIC".

OPTIONAL SUGGESTIONS
9. I found it strange that in the tabs "COSMIC to pmsignature" and "pmsignature to COSMIC", the drop-down menus for choosing the COSMIC and PM signatures to compare are located in the middle of the interface, below the top panels that show the chosen signature. It seems to me that it would be more intuitive to place the selection menus at the top of the page, although I might be wrong.

10.
The authors might consider extending the user-supplied signature mode to allow the user to input a set of signatures, and then select the signature to compare using a drop-down menu (as in the other comparison modes), as it is likely that users will be interested in analysing sets of signatures, rather than single signatures. However, I understand this might not be straightforward to implement.

Is the rationale for developing the new software tool clearly explained? Yes
Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Please amend this formula, and make sure that it gives the same values as the formula you have defined in your code (function getCosDistance).

Response:
The formula has been corrected in this new version of the manuscript. We have also verified that the formula was correctly implemented in the getCosDistance function in the software (see below).
getCosDistance <-function(F_1, F_2) { if(length(F_1)!=length(F_2)){ geterrmessage("Two signatures have different number of bases!") } cos <-sum(F_1*F_2)/(sqrt(sum(F_1^2))*sqrt(sum(F_2^2))) return(cos) } MINOR COMMENTS 2. Introduction, paragraph 1: This is somewhat inaccurate, in the sense that it is the cells in an organism's tissues that are exposed to mutational processes throughout the organism's life, and each cell or tissue develops its own mutational profile. The existence of a "unique mutational profile" thus may be better described as a property of a tissue, organ or tumour: it is not really accurate to say that each human has "his/her unique mutational profile", as this varies widely across tissues (e.g. cells in the blood, skin, liver, and colon have very different mutational spectra), and the differences among individuals also tend to be tissuespecific.
4. Implementation, paragraph 1: it would be useful to complement the example in Table 1 and Equation 1 with a figure that shows the original PM signature in Table 1, and the equivalent COSMIC signature that results from applying Equation 1 to each of the 96 substitution types. This would help the reader to understand the conversion between both graphical representations, which is shown in later figures.
Response: : As requested, we have added a new Figure 1 that shows the signature in Table  1 and the 'expanded' signature in COSMIC format to help illustrate Equation 1.
Response: Reference 5 is now updated.

6.
Note that reference 6 is missing a colon between the author list and title.
Response: : The colon has been added to reference 6.

7.
The "About iMutSig" web page states that "On the GitHub page, you can: -install the iMutSig R pacakge and run it locally." However, on the GitHub page I found no instructions on how the package can be installed and run locally using R and Shiny. While I understand that the main purpose of the platform is to be an online tool, some users may be interested in having a local copy. For example, the availability of the tool seemed somewhat variable: I was able to access the website (https://zhiyang.shinyapps.io/imutsig/) on 17 June, but not on 18 June. Although this might be a rare issue, it highlights the advantage of providing users with an alternative way of accessing the tool in the long term. For example, some simple steps for installation and running could be added as a README.md file on the GitHub repository.
Response: Thank you for the suggestion. A new README.md file was added to the GitHub repository (https://github.com/USCbiostats/iMutSig) providing instructions on how to install the necessary packages and host the app locally. The website failure on June 18 th resulted from a scheduled automatic downloading procedure which exceeded the server response time. Subsequently, we found that the COSMIC website posted new versions of signatures (v3.1) under a different website, allowing us to remove the automatic download procedure and avoid such an issue in the future.

8.
Note that, in the platform interface, some of the signature names read "COSIMIC" instead of "COSMIC".
Response: Those typos have been corrected.

OPTIONAL SUGGESTIONS
9. I found it strange that in the tabs "COSMIC to pmsignature" and "pmsignature to