Data Visualization

Key characteristics of cancers learned from TCGA (Nature Cancer, 2021): Published as part of a retrospective on the landmark TCGA cancer genomics program, this is a visual representation of the key findings of genomics analyses across 33 cancer types

Key characteristics of cancers learned from TCGA (Nature Cancer, 2021): Published as part of a retrospective on the landmark TCGA cancer genomics program, this is a visual representation of the key findings of genomics analyses across 33 cancer types. Each panel provides a concise summary of findings for each cancer, including incidence, and survival rates, mutational signatures and associated driver genes. Click image for details

Genetic mutations across cancer types (Nature, 2013): Key figure in a landmark paper illustrating patterns of genetic mutations across cancer types. Click image for details

Genetic mutations across cancer types (Nature, 2013): Key figure in a landmark paper illustrating patterns of genetic mutations across cancer types. Click image for details

Patterns of plasma cell subpopulation shift from Primary to Relapse (Nature Communications, 2021): A complex multi-panel data figure illustrating the changes in plasma cell populations in multiple myeloma relapse. Click image for details

Patterns of plasma cell subpopulation shift from Primary to Relapse (Nature Communications, 2021): A complex multi-panel data figure illustrating the changes in plasma cell populations in multiple myeloma relapse. Click image for details

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This figure was part of a successful grant application to HTAN2

This figure was part of a successful grant application to HTAN2

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This illustration was part of a successful grant application to the PDXNet consortium. Click image for details

This illustration was part of a successful grant application to the PDXNet consortium. Click image for details

This figure was part of a successful grant submission to the SenNet consortium

This figure was part of a successful grant submission to the SenNet consortium

BreakpointSurveyor is a bioinformatics and data visualization pipeline developed to visualize complex viral integration events in genomic data. Its innovative 2D visualization technique allows a number of lines of evidence of virus integration into

BreakpointSurveyor is a bioinformatics and data visualization pipeline developed to visualize complex viral integration events in genomic data. Its innovative 2D visualization technique allows a number of lines of evidence of virus integration into the human chromosome in cancer to be portrayed clearly, and was used to screen large numbers of such events. This comprehensive computational pipeline includes bioinformatics preprocessing as well as visualization steps, was published and is available on GitHub (click image). Published as: M.A. Wyczalkowski, et al, Bioinformatics (2017) DOI: 10.1093/bioinformatics/btx362

Visualization of gene expression disregulation in the vicinity of viral integration. The bottom panel is generated by Breakpoint Surveyor. Click the image for code and additional details

Visualization of gene expression disregulation in the vicinity of viral integration. The bottom panel is generated by Breakpoint Surveyor. Click the image for code and additional details

Summary of evidence of viral integration for a cohort of samples from next generation sequencing data, part of the Breakpoint Surveyor project

Summary of evidence of viral integration for a cohort of samples from next generation sequencing data, part of the Breakpoint Surveyor project

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Experiment Design