AccScience Publishing / TD / Volume 2 / Issue 2 / DOI: 10.36922/td.0894
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Structural variants integration and visualization: A comprehensive R package for integration of somatic structural variations from multiple callers and visualization

Lei Yu1,2†* Le Zhang2† Lili Wang3 Zhenyu Jia1,2*
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1 Department of Botany and Plant Sciences, University of California, Riverside, California, USA
2 Graduate Program in Genetics, Genomics, and Bioinformatics, University of California, Riverside, California, USA
3 Department of Systems Biology, Beckman Research Institute, Monrovia, California, USA
Tumor Discovery 2023, 2(2), 0894
Submitted: 4 May 2023 | Accepted: 3 July 2023 | Published: 20 July 2023
© 2023 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( )

Whole genome sequencing (WGS) emerges as a powerful tool for detecting structural variations (SVs) in genomes. However, different SV callers can produce variable results due to the distinct rationale and sensitivity of pipelines, highlighting the need for effective tools to compare and merge results from multiple callers. Here, we developed an R package, structural variants integration and visualization, to facilitate the integration, classification, and visualization of SV results from multiple callers, allowing for accurate identification of the most reliable SVs. Our package relies on a complex translocation projection and clustering method, enabling the projection of each translation to a point in a Cartesian coordinate system and visualization of SVs at both whole-genome and individual chromosome levels. Thus, our approach provides a valuable framework for analyzing SVs from WGS data, improving the accuracy and efficiency of SV detection, and enhancing the potential of WGS for clinical and research applications.

Structure variation manipulation
Structure variation visualization
Structure variation analysis
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Conflict of interest
The authors declare that they have no competing interests.
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Tumor Discovery, Electronic ISSN: 2810-9775 Published by AccScience Publishing