MAFDash: An easy-to-use dashboard builder for mutation data

Ashish Jain; Mayank Tandon

doi:10.12688/f1000research.118761.1

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MAFDash: An easy-to-use dashboard builder for mutation data

[version 1; peer review: 2 not approved]

Ashish Jain ^1,2, Mayank Tandon ^1,2

PUBLISHED 06 Jul 2022

Author details Author details

¹ CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20814, USA
² Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA

Ashish Jain
Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Review & Editing

Mayank Tandon
Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the RPackage gateway.

This article is included in the Bioinformatics gateway.

Abstract

Characterizing the somatic mutation landscape of a cohort of patients has become a routine task in cancer research in recent years. Such studies are often highly interdisciplinary, requiring iterative analysis that must be evaluated at each step by many researchers. Therefore, there is a growing need for reporting tools that can easily generate interactive reports for sharing data and results with collaborators. Here we present an R package, MAFDash, that tries to simplify summarization and visualization of mutation data from Mutation Annotation Format (MAF) files. The output HTML dashboard is a self-contained report that can be used for downstream analysis and sharing results. MAFDash is freely available on Github (https://github.com/CCBR/MAFDash).

Keywords

MAF, Mutation, Single Nucleotide Variants, Visualization, Dashboard, WES, WGS

Corresponding authors: Ashish Jain, Mayank Tandon

Competing interests: No competing interests were disclosed.

Grant information: This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2022 Jain A and Tandon M. This is an open access article 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.

How to cite: Jain A and Tandon M. MAFDash: An easy-to-use dashboard builder for mutation data [version 1; peer review: 2 not approved]. F1000Research 2022, 11:748 (https://doi.org/10.12688/f1000research.118761.1) First published: 06 Jul 2022, 11:748 (https://doi.org/10.12688/f1000research.118761.1) Latest published: 06 Jul 2022, 11:748 (https://doi.org/10.12688/f1000research.118761.1)

Introduction

In the last decade, the cost of next-generation sequencing (NGS) has gone down exponentially as both throughput and novel methods continue to advance.¹ For human clinical research, this has been reflected in an ever-growing number of datasets describing genomic variation among both normal and disease cohorts, including the 1000 Genomes Project Consortium,² and the more recent gnomAD project,³ which still serve as important benchmarks of normal genomic variation in humans. Similar efforts for characterizing somatic mutations in cancer research have been completed for 33 tumor types by The Cancer Genome Atlas (TCGA) consortium,⁴ and over 1,700 cancer cell lines in the Cancer Cell Line Encyclopedia (CCLE) project from the Broad Institute.⁵ Although both TCGA and CCLE provide mult-omics data, single nucleotide polymorphisms (SNPs) and small insertion/deletions (Indels) from NGS data are often used as starting points for downstream analyses diving deep into the biological pathways and identifying drug target genes in these cancers.⁶

Both TCGA and CCLE provide somatic mutations freely as Mutation Annotation Format (MAF) files. This format is used to report high quality somatic variants for cohorts of cancer patients as it is more readable and portable than the traditional variant call format (VCF), and is therefore a common starting point for downstream analysis of somatic SNP/Indel data. R packages like ‘maftools’⁷ are frequently used by bioinformaticians to read, summarize, and perform statistical tests on data from MAF files, and they provide excellent functions for basic visualization, and flexible manipulation of the underlying data.

Since MAF files can contain a large number of annotations (e.g. the vcf2maf tool from MSKCC⁸ produces 136 columns of annotations with a default installation of Variant Effect Predictor⁹), selecting useful information and preparing it for discussion with researchers requires expertise. To simplify this task, we have developed MAFDash, an R package that helps to quickly create HTML dashboards for summarizing and visualizing data from MAF files. The resulting HTML file serves as a self-contained report that can be used to explore and share the results. MAFDash provides preset functions for extracting and organizing somatic variant data into interactive tables and figures. The goal of this package is to provide a simplified interface to filter and present data from MAF files suitable both for highly customized reports, as well as routine output from variant calling pipelines. The package also provides functions to generate individual plots as a ggplot2¹⁰ or ComplexHeatmap¹¹ object giving users more flexibility.

Methods

Implementation

MAFDash is a package intended for use with the R programming language.¹² The report is generated with a parameterized R Markdown script to arrange all the information. If a MAF object is provided, an interactive table is generated to provide client-side, dynamic filtering of the variant data. In addition to the dashboard generation, it also consists of a variety of functions to generate high quality figures to visualize mutation data. We also provided detailed documentation and a test dataset to demonstrate usage of these functions. Static plots are generated using the R packages ‘maftools’,⁷ ComplexHeatmap,¹¹ ‘circlize’,¹³ and ‘ggplot2’.¹⁰ Interactive visualizations are implemented using ‘canvasXpress’¹⁴ and ‘plotly’.¹⁵

Operation

MAFDash was developed and tested on 2019 Macbook Pros with 2.4GHz 8-core Intel Core i9 processors and 16 Gb of memory, running Mac OS X 10.15.7 (Catalina). The source code and documentation is hosted on Github (https://github.com/CCBR/MAFDash).

Functions for TCGA data

The function getMAFdataTCGA(…) retrieves TCGA mutation data in MAF format. This function takes the cancer code(s) as input and outputs the TCGA mutation data called from Mutect2,¹⁶ or other callers as available. This function internally uses the ‘TCGAbiolinks’ R package¹⁷ to download the data and then uses internal processing to output the mutation data in a clean format. For annotation information, the getTCGAClinicalAnnotation(…) function extracts and processes common clinical features provided with the TCGA data including pathological state, tissue site, age, gender, race, and vital status, and generates reasonable preset colors suitable for use with ‘ComplexHeatmap’. The processed mutation data along with the clinical annotations can be further analyzed by utilizing the various visualization functions in MAFDash.

Filtering of mutations

The filterMAF(…) function in MAFDash automatically detects the presence of relevant columns and re-casts them appropriately for numeric or text-based filtering. These include tumor read frequency and depth, frequency in population databases (gnomAD¹⁸ and ExAC¹⁹), and consensus mutation calls from multiple variant callers. Such criteria are frequently used for determining tumor mutational burden (TMB) from whole-exome sequencing data.²⁰

This function also can also remove a preset list of commonly mutated genes,²¹ or a custom set of genes. Finally, data is processed in definable chunks of lines (default of 10,000 lines), which is intended to help filter large MAF files without getting “out of memory” issues.

Visualizations of summarized mutation data

MAFDash consists of various functions for visualizing summarized mutation data across a cohort of samples. Below are the different functions that are provided.

• generateBurdenPlot(…): It generates a dotplot and a barplot to show the comparison of the total number of mutations across the samples. The mutations are also grouped based on its type.
• generateMutationTypePlot(…): It generates a barplot showing the distribution of the silent and non-silent mutations across the input samples.
• generateOncoPlot(…): It generates a heatmap that summarized the top mutated genes across the input samples.
• generateOverlapPlot(…): It generates a circular plot to show the common mutations across the input samples.
• generateRibbonPlot(…): It generates a heatmap to show the cosine similarity between the mutated genes using the result from maftools’ somaticInteractions(…) function.
• generateTiTvPlot(…): It plots the frequency of transitions and transversions of the gene mutations in the input datasets.
• generateTCGAComparePlot(…): It computes and plots the mutation load of the input MAF against all 33 of the TCGA cohorts derived from MC3 project. It also calculates the significant mutational load differences between the cancers.

Mutational signatures and etiologies

Mutational signature matrix for single-base substitutions (SBS) were retrieved from COSMIC v3.2.²² Text in the “Acceptance criteria” section of each signature page was retrieved from the COSMIC website using R scripts. This free text was lightly filtered and manually curated yielding 25 broad categories for 78 total signatures and is provided with the package repository in tabular format (Table 1).

Table 1. SBS Mutational signatures and associated etiologies curated from COSMIC v3.2.

Signature	Etiology (Scraped)	Etiology (Broad)
SBS45	8-oxo-guanine introduced during sequencing	8-oxo-guanine introduced during sequencing
SBS24	Aflatoxin exposure	Aflatoxin exposure
SBS5	Aging/Tobacco smoking/NER deficiency	Aging/Tobacco smoking/NER deficiency
SBS84	AID activity	AID activity
SBS85	AID activity	AID activity
SBS2	APOBEC activity	APOBEC activity
SBS13	APOBEC activity	APOBEC activity
SBS22	Aristolochic acid exposure	Aristolochic acid exposure
SBS32	Azathioprine exposure	Azathioprine exposure
SBS30	BER deficiency	BER deficiency
SBS36	BER deficiency	BER deficiency
SBS88	Colibactin exposure	Colibactin exposure
SBS17a	Damage by ROS	Damage by ROS
SBS18	Damage by ROS	Damage by ROS
SBS17b	Damage by ROS/5FU chemotherapy	Damage by ROS
SBS10c	Defective POLD1 proofreading	Defective POLD1 proofreading
SBS10d	Defective POLD1 proofreading	Defective POLD1 proofreading
SBS90	Duocarmycin exposure	Duocarmycin exposure
SBS54	Germline variants contamination	Germline variants contamination
SBS42	Haloalkanes exposure	Haloalkanes exposure
SBS3	HR deficiency	HR deficiency
SBS8	HR deficiency/NER deficiency	HR deficiency
SBS6	MMR deficiency	MMR deficiency
SBS15	MMR deficiency	MMR deficiency
SBS21	MMR deficiency	MMR deficiency
SBS26	MMR deficiency	MMR deficiency
SBS44	MMR deficiency	MMR deficiency
SBS20	MMR deficiency + POLD1 mutation	MMR deficiency
SBS14	MMR deficiency + POLE mutation	MMR deficiency
SBS31	Platinum chemotherapy	Platinum chemotherapy
SBS35	Platinum chemotherapy	Platinum chemotherapy
SBS10a	POLE exonuclease domain mutation	POLE exonuclease domain mutation
SBS10b	POLE exonuclease domain mutation	POLE exonuclease domain mutation
SBS28	POLE exonuclease domain mutation	POLE exonuclease domain mutation
SBS9	Polymerase eta somatic hypermutation	Polymerase eta somatic hypermutation
SBS43	Possible sequencing artifact	Sequencing artifact
SBS51	Possible sequencing artifact	Sequencing artifact
SBS55	Possible sequencing artifact	Sequencing artifact
SBS56	Possible sequencing artifact	Sequencing artifact
SBS57	Possible sequencing artifact	Sequencing artifact
SBS58	Possible sequencing artifact	Sequencing artifact
SBS59	Possible sequencing artifact	Sequencing artifact
SBS27	Sequencing artifact	Sequencing artifact
SBS60	Sequencing artifact	Sequencing artifact
SBS47	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS48	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS49	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS50	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS52	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS53	Sequencing artifact (blacklisted cancer samples for poor quality)	Sequencing artifact
SBS46	Sequencing artifact (early releases of TCGA)	Sequencing artifact
SBS1	Spontaneous deamination of 5-methylcytosine	Spontaneous deamination of 5-methylcytosine
SBS11	Temozolomide chemotherapy/MMR deficiency + temozolomide	MMR deficiency
SBS87	Thiopurine chemotherapy	Thiopurine chemotherapy
SBS29	Tobacco chewing	Tobacco
SBS4	Tobacco smoking	Tobacco
SBS92	Tobacco smoking	Tobacco
SBS12	Unknown	Unknown
SBS16	Unknown	Unknown
SBS19	Unknown	Unknown
SBS23	Unknown	Unknown
SBS33	Unknown	Unknown
SBS34	Unknown	Unknown
SBS37	Unknown	Unknown
SBS39	Unknown	Unknown
SBS40	Unknown	Unknown
SBS41	Unknown	Unknown
SBS89	Unknown	Unknown
SBS91	Unknown	Unknown
SBS93	Unknown	Unknown
SBS94	Unknown	Unknown
SBS25	Unknown chemotherapy	Unknown
SBS86	Unknown chemotherapy	Unknown
SBS7a	UV light exposure	UV light exposure
SBS7b	UV light exposure	UV light exposure
SBS7c	UV light exposure	UV light exposure
SBS7d	UV light exposure	UV light exposure
SBS38	UV light exposure (indirect effect)	UV light exposure

Etiology annotations for mutational signature analysis

To aid interpretation of mutational signature analysis, we have curated COSMIC signatures etiologies from COSMIC v3.2.²² Specifically, we scraped the COSMIC website to retrieve the proposed etiology for all 78 COSMIC single-base substitution (SBS) signatures, yielding 36 unique etiologies, which we further manually curated into 25 broad categories. The generateCOSMICMutSigSimHeatmap(…) function shows these categorized proposed etiologies as colored row annotations, aimed at quickly identifying distinct or common etiologies across a cohort. Figure 1 shows the SBS signature in each sample in columns, COSMIC mutation signatures in rows, and each cell is colored to indicate the level of similarity between the two.

Figure 1. Heatmap showing the cosine similarity between the mutational signatures from The Cancer Genome Atlas’s Adrenocortical carcinoma cohort with Catalogue Of Somatic Mutations In Cancer (COSMIC) signatures.

Use cases

Interactive HTML reports for MAF data

MAFDash has a function (getMAFDashboard(…)) that generates an HTML dashboard for visualization and analysis of mutation data in MAF format. The dashboard consists of arbitrarily defined or preset interactive plots describing the data. By default, if MAF data is provided, the dashboard visualizes the mutations data in five different tabs.

• Summary plots: Static multi-part figure describing cohort summaries of variant classification, variant type, number of variants per samples and nucleotide change (from ‘maftools’).
• Burden plots: Interactive plots showing the number of variants per samples in the form of a dotplot and barplot, with hover text containing sample and mutation information.
• Oncoplot: Plot summarizing the top mutated genes across the samples.
• Co-occurrence of mutated genes: A circular ribbon plot showing co-occurrence of the mutations, inspired by the somaticInteractions(…) function in ‘maftools’.
• Interactive heatmap: An interactive version of the oncoplot with hover text showing the number of mutations in a gene for a particular sample.

In addition to these plots, an interactive table is generated using the DT²³ and crosstalk²⁴ R packages to provide client-side, dynamic filtering of the variant data. The generated dashboard is self-contained for sharing with collaborators. MAFDash will automatically account for missing data and also provides reasonable defaults for filtering mutation data. Figure 2 shows the dashboard output for Adrenocortical carcinoma (ACC) downloaded from TCGA.⁶

Figure 2. Snapshot of the oncoplot tab of the HTML dashboard created by the getMAFDashboard(…) function using the TCGA Adrenocortical Carcinoma (ACC) dataset.

HTML reports for arbitrary plots

Even without MAF data, MAFDash can be used to generate an HTML report with user generated plot objects. Users can pass any ‘ggplot2’, ‘ComplexHeatmap’, or ‘plotly’ objects, or the location of an image file to include it in the dashboard as a list, and have it rendered as a dashboard with each element as a tab in the report. Figure 3 shows an example dashboard using the iris dataset provided with R.

Figure 3. Snapshot of a custom tabbed report using various types of plots using the example iris dataset.

Conclusions

We developed MAFDash to simplify the process of generating interactive reports for somatic mutation analysis. The ‘maftools’ R package already provides a comprehensive toolkit for organizing and analyzing MAF data, but it exclusively uses base R graphics for plotting, which is not amenable to further modification or interactivity. For example, the tcgaCompare(…) function is an excellent visual comparison of mutation burden with all cancer types in TCGA. To allow interactivity in MAFDash, we implemented the same visualization using ‘ggplot2’, which can trivially be converted to an interactive HTML widget using ‘plotly’. Finally, the self-contained nature of the HTML report, as well as a range of choices for interactive plots, is aimed at easily sharing data and interpretations. Overall, we hope that MAFDash will allow for quick iterations of analysis during collaborations between bioinformaticians and bench scientists.

Data availability

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

Software availability

• Source code available at: https://github.com/CCBR/MAFDash
• Archived source code at time of publication: https://doi.org/10.5281/zenodo.6421833²⁵
• License: MIT License

Acknowledgements

We would like to thank CCR Collaborative Bioinformatics Resource (CCBR) members for their feedback.

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Version 1

VERSION 1 PUBLISHED 06 Jul 2022

Author details Author details

¹ CCR Collaborative Bioinformatics Resource (CCBR), Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20814, USA
² Advanced Biomedical Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, 21701, USA

Ashish Jain
Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Review & Editing

Mayank Tandon
Roles: Conceptualization, Data Curation, Methodology, Software, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261201500003I. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (1)

version 1

Published: 06 Jul 2022, 11:748

https://doi.org/10.12688/f1000research.118761.1

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© 2022 Jain A and Tandon M. This is an open access article 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.

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Jain A and Tandon M. MAFDash: An easy-to-use dashboard builder for mutation data [version 1; peer review: 2 not approved]. F1000Research 2022, 11:748 (https://doi.org/10.12688/f1000research.118761.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

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Reviewer Report 02 Sep 2022

Heiko Brennenstuhl, Division of Child Neurology and Metabolic Medicine, Centre for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany; Institute for Human Genetics, University Heidelberg, Heidelberg, Germany

Not Approved

https://doi.org/10.5256/f1000research.130605.r147677

In their manuscript "MAFDash: An easy-to-use dashboard builder for mutation data" Jain and Tandon report an R package which allows processing and visualization of mutation data from standardized and widely available MAF files and displays the results in an HTML dashboard. Various ... Continue reading

In their manuscript "MAFDash: An easy-to-use dashboard builder for mutation data" Jain and Tandon report an R package which allows processing and visualization of mutation data from standardized and widely available MAF files and displays the results in an HTML dashboard. Various forms of presentation are generated automatically (tables, burden plots, OncoPlots, interactive heat maps, etc.), which should enable an analysis of the data set through sophisticated visualizations.

The manuscript is well written in an understandable manner and all considerations are clearly and convincingly presented.

Nevertheless, I have some small points of criticism:

MAF files are extremely impractical to handle and can be a great challenge, especially for researchers with limited experience in bioinformatics. In my experience, VCF files are much more common and should therefore be included in the workflow of this tool.
Some of the links within the documentation on github are broken (e.g. https://mtandon09.github.io/MAFDashRPackage/examples/LAML.mafdash.html and https://mtandon09.github.io/MAFDashRPackage/examples/articles/Quick_Start.html)
Local installation does not work (at least in my application): Some dependencies are not available for MAFDash according to my R version (including 'TCGAbiolinks', 'maftools', 'ComplexHeatmap', 'BSgenome-Hsapiens.UCSC.hg38'), the installation is aborted with the remark 'MAFDash_0.2.2.tar.gz' had non-zero exit status'

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Partly
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: human genetics, inborn errors of metabolism

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 21 Jul 2022

Sigve Nakken, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

Not Approved

https://doi.org/10.5256/f1000research.130605.r143350

Jain and Tandon have created a tool for visualization and analysis of cancer mutation data represented through the MAF format. The tool generates a dashboard as its output, hence the name, MAFdash. The tool offers multiple features to analyse mutation ... Continue reading

Jain and Tandon have created a tool for visualization and analysis of cancer mutation data represented through the MAF format. The tool generates a dashboard as its output, hence the name, MAFdash. The tool offers multiple features to analyse mutation data from a cohort of cases/samples, exemplified through oncoplots (highly mutated genes), burden plots, mutational signature analysis, etc.

Currently, the tool suffers from some key limitations that I encourage the authors to handle in a revised version.

Major points

The tool is intended for analysis of MAF data, and the authors exemplify this through data from TCGA. However, most users will primarily be interested in using MAFdash on their own data rather than TCGA/CCLE. Importantly, users who have done cancer genome sequencing typically end up with VCF files after calling (I am not aware of any somatic variant callers that produce MAF?). If the tool does not support any transformation of VCF towards MAF, I am afraid the tool will not be used according to the intentions outlined by the authors, that is to support users with analysis/visualization of mutation data. In other words, the whole premise of the tool is to have a MAF file at hand, but obtaining this from a large-scale sequencing project is not covered by the tool, nor does the tool provide pointers/workflows to how this can be accomplished. Showcasing a complete workflow from variant calling towards MAF towards MAFdash would be helpful for the users, and strengthen the tool significantly. Based on the reasoning above, I think the slogan "Once you call the variants, it's a MAFDash to the finish line" is in my opinon somewhat misleading.
Technically, the tool suffers from multiple issues:

1. https://github.com/CCBR/MAFDash contains a number of pointers to https://github.com/ashishjain1988/MAFDash, this needs to be cleaned up. Similarly, the documentation site https://ashishjain1988.github.io/MAFDash/ contains links to https://mtandon09.github.io/MAFDashRPackage/. Please clean up the GitHub page and the accompanying documentation site. Most importantly, links to the the example reports are non-functioning, which are critically important to showcase the output of the tool. Currently, I am unable to explore any output examples from the tool.

2. The installation procedure is not working properly, the DESCRIPTION file needs cleaning:
- Addition of biocViews: (for Bioconductor packages)
- Move the (large) BSgenome package to Suggests
- Clean out the Depends stuff, just keep R there. Rest go into Suggests/Imports.
- Remove most of the version pinning in the Imports
- Make sure the installation works both on Mac OSX and Linux

- Looking at the function reference (https://ashishjain1988.github.io/MAFDash/reference/index.html), there are a number of misleading elements, i.e.

generateOncoPlot() - Function to generate a dashboard from a MAF file?
filterMAF() and filterMAF2()?
compute_exome_coverage() - is this relevant function for MAFdash?

Minor points

The example case for arbitrary plots using the iris dataset is misleading, please provide a relevant dataset.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

No
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Genomics, translational bioinformatics, precision cancer medicine

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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VERSION 1 PUBLISHED 06 Jul 2022

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Version 1 06 Jul 22	read	read

Sigve Nakken, Oslo University Hospital, Oslo, Norway; University of Oslo, Oslo, Norway; University of Oslo, Oslo, Norway
Heiko Brennenstuhl, University Hospital Heidelberg, Heidelberg, Germany; University Heidelberg, Heidelberg, Germany

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02 Sep 2022 | for Version 1

Heiko Brennenstuhl, Division of Child Neurology and Metabolic Medicine, Centre for Child and Adolescent Medicine, University Hospital Heidelberg, Heidelberg, Germany; Institute for Human Genetics, University Heidelberg, Heidelberg, Germany

16 Views Cite this report Responses(0)

Not Approved

In their manuscript "MAFDash: An easy-to-use dashboard builder for mutation data" Jain and Tandon report an R package which allows processing and visualization of mutation data from standardized and widely available MAF files and displays the results in an HTML dashboard. Various forms of presentation are generated automatically (tables, burden plots, OncoPlots, interactive heat maps, etc.), which should enable an analysis of the data set through sophisticated visualizations.

The manuscript is well written in an understandable manner and all considerations are clearly and convincingly presented.

Nevertheless, I have some small points of criticism:

MAF files are extremely impractical to handle and can be a great challenge, especially for researchers with limited experience in bioinformatics. In my experience, VCF files are much more common and should therefore be included in the workflow of this tool.
Some of the links within the documentation on github are broken (e.g. https://mtandon09.github.io/MAFDashRPackage/examples/LAML.mafdash.html and https://mtandon09.github.io/MAFDashRPackage/examples/articles/Quick_Start.html)
Local installation does not work (at least in my application): Some dependencies are not available for MAFDash according to my R version (including 'TCGAbiolinks', 'maftools', 'ComplexHeatmap', 'BSgenome-Hsapiens.UCSC.hg38'), the installation is aborted with the remark 'MAFDash_0.2.2.tar.gz' had non-zero exit status'

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Partly
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

human genetics, inborn errors of metabolism

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report

28 Views

21 Jul 2022 | for Version 1

Sigve Nakken, Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway; Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway; Centre for Cancer Cell Reprogramming, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

28 Views Cite this report Responses(0)

Not Approved

Jain and Tandon have created a tool for visualization and analysis of cancer mutation data represented through the MAF format. The tool generates a dashboard as its output, hence the name, MAFdash. The tool offers multiple features to analyse mutation data from a cohort of cases/samples, exemplified through oncoplots (highly mutated genes), burden plots, mutational signature analysis, etc.

Currently, the tool suffers from some key limitations that I encourage the authors to handle in a revised version.

Major points

The tool is intended for analysis of MAF data, and the authors exemplify this through data from TCGA. However, most users will primarily be interested in using MAFdash on their own data rather than TCGA/CCLE. Importantly, users who have done cancer genome sequencing typically end up with VCF files after calling (I am not aware of any somatic variant callers that produce MAF?). If the tool does not support any transformation of VCF towards MAF, I am afraid the tool will not be used according to the intentions outlined by the authors, that is to support users with analysis/visualization of mutation data. In other words, the whole premise of the tool is to have a MAF file at hand, but obtaining this from a large-scale sequencing project is not covered by the tool, nor does the tool provide pointers/workflows to how this can be accomplished. Showcasing a complete workflow from variant calling towards MAF towards MAFdash would be helpful for the users, and strengthen the tool significantly. Based on the reasoning above, I think the slogan "Once you call the variants, it's a MAFDash to the finish line" is in my opinon somewhat misleading.
Technically, the tool suffers from multiple issues:

1. https://github.com/CCBR/MAFDash contains a number of pointers to https://github.com/ashishjain1988/MAFDash, this needs to be cleaned up. Similarly, the documentation site https://ashishjain1988.github.io/MAFDash/ contains links to https://mtandon09.github.io/MAFDashRPackage/. Please clean up the GitHub page and the accompanying documentation site. Most importantly, links to the the example reports are non-functioning, which are critically important to showcase the output of the tool. Currently, I am unable to explore any output examples from the tool.

2. The installation procedure is not working properly, the DESCRIPTION file needs cleaning:
- Addition of biocViews: (for Bioconductor packages)
- Move the (large) BSgenome package to Suggests
- Clean out the Depends stuff, just keep R there. Rest go into Suggests/Imports.
- Remove most of the version pinning in the Imports
- Make sure the installation works both on Mac OSX and Linux

- Looking at the function reference (https://ashishjain1988.github.io/MAFDash/reference/index.html), there are a number of misleading elements, i.e.

generateOncoPlot() - Function to generate a dashboard from a MAF file?
filterMAF() and filterMAF2()?
compute_exome_coverage() - is this relevant function for MAFdash?

Minor points

The example case for arbitrary plots using the iris dataset is misleading, please provide a relevant dataset.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

No
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Genomics, translational bioinformatics, precision cancer medicine

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Respond to this report

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MAFDash: An easy-to-use dashboard builder for mutation data

Abstract

Keywords

Introduction

Methods

Implementation

Operation

Functions for TCGA data

Filtering of mutations

Visualizations of summarized mutation data

Mutational signatures and etiologies

Table 1. SBS Mutational signatures and associated etiologies curated from COSMIC v3.2.

Etiology annotations for mutational signature analysis

Figure 1. Heatmap showing the cosine similarity between the mutational signatures from The Cancer Genome Atlas’s Adrenocortical carcinoma cohort with Catalogue Of Somatic Mutations In Cancer (COSMIC) signatures.

Use cases

Interactive HTML reports for MAF data

Figure 2. Snapshot of the oncoplot tab of the HTML dashboard created by the getMAFDashboard(…) function using the TCGA Adrenocortical Carcinoma (ACC) dataset.

HTML reports for arbitrary plots

Figure 3. Snapshot of a custom tabbed report using various types of plots using the example iris dataset.

Conclusions

Data availability

Software availability

Acknowledgements

References

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