Chen Hong1,2,3, Robin Thiele1,2, Lars Feuerbach1. 1. Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany. 2. Faculty of Biosciences, Heidelberg University, Heidelberg, Germany. 3. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Abstract
BACKGROUND: Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighbouring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis. RESULTS: We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. CONCLUSION: The GenomeTornadoPlot R-package provides a novel method to both visualize and score cancer-relevant CNVs. Exploratory or hypothesis-driven analyses can be performed on the basis of the integrated PCAWG data or in combination with data provided by the user. AVAILABILITY: GenomeTornadoPlot is written in R script and released via github: <https://github.com/chenhong-dkfz/GenomeTornadoPlot/>. The package is under the license of GPL-3.0.
BACKGROUND: Analysis of focal copy number variations (CNVs) is highly relevant for cancer research, as they pinpoint driver genes. More specifically, due to selective pressure oncogenes and tumor suppressor genes are more often affected by these events than neighbouring passengers. In cases where multiple candidates co-reside in a genomic locus, careful comparison is required to either identify multigenic minimally deleted regions of synergistic co-mutations, or the true single driver gene. The study of focal CNVs in large cancer genome cohorts requires specialized visualization and statistical analysis. RESULTS: We developed the GenomeTornadoPlot R-package which generates gene-centric visualizations of CNV types, locations and lengths from cohortwise NGS data. Furthermore, the software enables the pairwise comparison of proximate genes to identify co-mutation patterns or driver-passenger hierarchies. The visual examination provided by GenomeTornadoPlot is further supported by adaptable local and global focality scoring. Integrated into the GenomeTornadoPlot R-Package is the comprehensive PCAWG database of CNVs, comprising 2976 cancer genome entities from 46 cohorts of the Pan-cancer Analysis of Whole Genomes project. CONCLUSION: The GenomeTornadoPlot R-package provides a novel method to both visualize and score cancer-relevant CNVs. Exploratory or hypothesis-driven analyses can be performed on the basis of the integrated PCAWG data or in combination with data provided by the user. AVAILABILITY: GenomeTornadoPlot is written in R script and released via github: <https://github.com/chenhong-dkfz/GenomeTornadoPlot/>. The package is under the license of GPL-3.0.
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