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Tisa Ammann

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Aug 2, 2024, 8:24:03 PM8/2/24

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I tried to create a similar one using 'ggplot' and combine all of them into one plot, but it takes very long time. Is there an easier way to combine the oncoplot, different clinical information and side legends together?

Also, what is the best way to create the oncoplot clustered by groups and sort based one some value? Like the image below, it was build based on subtypes and sorted by mutations rate I guess. The link is below: -mutational-landscape-of-33-cancer-genes-in-Chinese-breast-tumors-n305-Genomic_fig1_332730584

This repository contains a simple Excel spreadsheet for creating oncoplots to illustrate genetic mutations in a patient population. It does not require or depend on any other software to use. Edit the gray-shaded cells with a designated mutation type ID (below) to identify the observed mutation (rows) for each patient (columns).

This repository contains a simple Excel spreadsheet for creating swimmer plots to illustrate patient stories. It does not require or depend on any other software to use. Edit any of the cells with blue text to customize the swimmer plots which are available in both horizontal and vertical orientations.

Data for rightside barplot. Must be a data.frame with two columns containing to gene names and values. Default 'NULL' which draws distibution by variant classification. This option is applicable when only 'drawRowBar' is TRUE.

Default 'NULL' which draws absolute number of mutation load for each sample. Can be overridden by choosing one clinical indicator(Numeric) or by providing a two column data.frame contaning sample names and values for each sample. This option is applicable when only 'drawColBar' is TRUE.

If MAF file was read without clinical data, provide a custom data.frame with a column Tumor_Sample_Barcode containing sample names along with rest of columns with annotations.You can specify which columns to be drawn using 'clinicalFeatures' argument.

Takes maf file as input and plots it as a matrix. Any desired clincal features can be added at the bottom of the oncoplot by providing clinicalFeatures.Oncoplot can be sorted either by mutations or by clinicalFeatures using arguments sortByMutation and sortByAnnotation respectively.

Is there any way to get the oncoplot with out sorting gene names based on the frequency. I have a list of genes which are sorted based on my analysis. I am wondering if there is a way to get the oncoplot in the same of order as mentioned in the list and not based on the frequency.

In this tutorial, we are interested in analyzing gene expression and protein abundance differences between two types of TCGA kidney cancers, Kidney Renal Clear Cell Carcinoma (KIRC) and Kidney Renal Papillary Carcinoma (KIRP). We will build a cohort of patients with these cancer types and extract their respective gene expression and protein abundance data using Google BigQuery.

From here, we can use either R or Python to perform higher level analyses. In this example, we will be running an R notebook in the Google Cloud AI Platform Notebooks environment.If you prefer, you can run this example in a local R environment instead.

CoMut plot is widely used in cancer research publications as a visual summary of mutational landscapes in cancer cohorts. This summary plot can inspect gene mutation rate and sample mutation burden with their relevant clinical details, which is a common first step for analyzing the recurrence and co-occurrence of gene mutations across samples. The cBioPortal and iCoMut are two web-based tools that allow users to create intricate visualizations from pre-loaded TCGA and ICGC data. For custom data analysis, only limited command-line packages are available now, making the production of CoMut plots difficult to achieve, especially for researchers without advanced bioinformatics skills. To address the needs for custom data and TCGA/ICGC data comparison, we have created CoMutPlotter, a web-based tool for the production of publication-quality graphs in an easy-of-use and automatic manner.

We introduce a web-based tool named CoMutPlotter to lower the barriers between complex cancer genomic data and researchers, providing intuitive access to mutational profiles from TCGA/ICGC projects as well as custom cohort studies. A wide variety of file formats are supported by CoMutPlotter to translate cancer mutation profiles into biological insights and clinical applications, which include Mutation Annotation Format (MAF), Tab-separated values (TSV) and Variant Call Format (VCF) files.

In summary, CoMutPlotter is the first tool of its kind that supports VCF file, the most widely used file format, as its input material. CoMutPlotter also provides the most-wanted function for comparing mutation patterns between custom cohort and TCGA/ICGC project. Contributions of COSMIC mutational signatures in individual samples are also included in the summary plot, which is a unique feature of our tool.

With the rapid evolution of next-generation technologies (NGS) combined with dropping costs, whole-exome sequencing (WES) has become a widely-accepted application for clinical research and diagnostic purposes. In the past few years, over 10,000 exomes across 40 distinct types of human cancer were generated by The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). The Broad institute has released the GATK Best Practice workflow tailored to somatic variant discovery. Researchers can follow this standardize analysis protocol, making their results comparable to TCGA/ICGC projects. Variant annotation is a relatively mature and feasible work because of the state-of-the-art packages like ANNOVAR [1], VEP [2], SnpEff [3], and Oncotator [4]. However, an intuitive and convenient way for visualizing and interpreting genomic data from high-throughput technologies continues to be challenging. Inconsistent file formats used in handling mutation profiles may introduce additional problems in subsequent data integration, visualization and comparison.

CoMut plot [5,6,7] is widely used in cancer research publications as a visual summary of mutational landscapes in cancer cohorts. This summary plot can inspect gene mutation rate and sample mutation burden with their relevant clinical details, which is a common first step for analyzing the recurrence and co-occurrence of gene mutations across samples. There are two web -based applications, the cBioPortal [8] and iCoMut ( ), which allow users to create intricate visualizations from pre-loaded TCGA data. For custom data analysis, only certain file formats such as MAF and TSV format are supported at this stage, which are based on command-line packages [6, 7], making the production of customizable plots difficult to achieve, especially for non-bioinformatics researchers.

To address the needs for custom data and TCGA/ICGC data comparison, we have created CoMutPlotter, a web-based tool, for the production of publication quality graphs and to translate cancer mutation profiles into biological insights and clinical applications. A wide variety of file formats are supported by CoMutPlotter, which include Mutation Annotation Format (MAF), Tab-separated values (TSV) and Variant Call Format (VCF) files. It is worth noting that CoMutPlotter is the first tool of its kind that directly supports VCFs, a dominant output format of all variant discovery pipelines like the GATK Toolkit [9], VarScan [10], and SAMtools [11]. Deciphering signatures of the mutational processes in human cancer is a new trend in cancer research community [12,13,14] because these signatures are footprints of molecular aberrations occurring in tumors. Alexandrov et al. identified a list of 30 reference signatures and about half of these signatures can be attributed to endogenous processes such as enzymatic activity of DNA cytidine deaminases (AID/APOBEC), the deficiency of DNA mismatch repair, or mutations in POLE and to exogenous mutagens like tobacco, ultraviolet light and toxic chemicals [15].

Our specific aim to construct CoMutPlotter is to lower the barriers between complex cancer genomic data and researchers. In addition to specifying the mutation burden and types of individual samples, we also allow the user to plot clinical features with their respective samples, providing intuitive access to mutational profiles from TCGA/ICGC as well as custom cohort studies alongside their clinical attributes. CoMutPlotter also provides the most-wanted function for comparing mutational landscapes between custom cohort and TCGA/ICGC project. To gain insight into the mutational processes that have altered the cancer genome, contributions of COSMIC signatures are quantified at sample resolution and integrated in the summary plot as dot matrix, which is a unique feature of CoMutPlotter. CoMutPlotter is freely available at

CoMutPlotter provides an intuitive web interface to receive mutation profiles obtained from cancer sequencing projects. Mutation Annotation Format (MAF) is widely used in TCGA cancer studies for storing mutation profiles, which is also the basis for many downstream analyses such as variant annotation, driver gene detection, mutual exclusivity analysis, and mutational signature identification. In addition to MAF file, CoMutPlotter also includes function to convert ICGC tab-separated values (TSV) file and standard Variant Call Format (VCF) file to MAF file, making this tool more accessible to wider researchers. CoMutPlotter not only provides complete functions for performing analyses mentioned above but also creates an interactively framework to present and summarize the important characteristics of the multidimensional analysis results from a custom cancer cohort. For the convenience of comparative analysis between custom data and TCGA/ICGA data, 73 mutation profiles were downloaded from TCGA and ICGC Data Portal and compiled as pre-loaded database. The PHP and R script are used to summarize all the generated results into an integrative plot to grasp the global characteristics of a mutation profile and to reveal the co-occurrence of mutations and samples. Download links are also provided to download publication-quality figures, significantly mutated gene list and detailed annotation table (Fig. 1).