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Tumor Discovery Enhanced SV analysis in WGS

projection of translocations onto a Cartesian coordinate oncology. NPJ Precis Oncol, 5: 15.
system combined with clustering techniques. This unique https://doi.org/10.1038/s41698-021-00155-6
approach allows for the application of unsupervised
clustering methods to address translocation merging 3. Li Y, Roberts ND, Wala JA, et al., 2020, Patterns of somatic
structural variation in human cancer genomes. Nature,
challenges, offering a novel way to merge and integrate 578: 112–121.
translocation events identified by different callers.
Furthermore, SVIV provides powerful visualization https://doi.org/10.1038/s41586-019-1913-9
functions that enable users to present SVs clearly and 4. Ayatollahi H, Keramati MR, Kooshyar MM, et al., 2018,
concisely. The package performs all merging procedures BCR-ABL fusion genes and laboratory findings in patients
and visualizations within the R environment, providing with chronic myeloid leukemia in Northeast Iran. Caspian J
convenience to users. The merged results obtained from Intern Med, 9: 65–70.
SVIV can be easily utilized as features for analyzing https://doi.org/10.22088/cjim.9.1.65
associations with clinical outcomes. In summary, the 5. Ross TS, Mgbemena VE, 2014, Re-evaluating the role of
SVIV package offers an efficient solution for merging, BCR/ABL in chronic myelogenous leukemia. Mol Cell
integrating, and visualizing SV data. It serves as a valuable Oncol, 1: e963450.
tool for researchers in the field, empowering them to https://doi.org/10.4161/23723548.2014.963450
merge SV results, integrate diverse caller outputs, and gain
comprehensive insights into the complex landscape of SVs. 6. Gajria D, Chandarlapaty S, 2011, HER2-amplified breast
cancer: Mechanisms of trastuzumab resistance and novel
Acknowledgments targeted therapies. Expert Rev Anticancer Ther, 11: 263–275.
None. https://doi.org/10.1586/era.10.226
7. Foulkes WD, Flanders TY, Pollock PM, et al., 1997, The
Funding CDKN2A (p16) gene and human cancer. Mol Med, 3: 5–20.
None. https://doi.org/10.1007/bf03401664

Conflict of interest 8. Chen X, Schulz-Trieglaff O, Shaw R, et al., 2016, Manta:
Rapid detection of structural variants and indels for germline
The authors declare that they have no competing interests. and cancer sequencing applications. Bioinformatics,
32: 1220–1222.
Author contributions https://doi.org/10.1093/bioinformatics/btv710

Conceptualization: All authors 9. Rausch T, Zichner T, Schlattl A, et al., 2012, DELLY:
Writing – original draft: Lei Yu, Le Zhang Structural variant discovery by integrated paired-end and
Writing – review & editing: Lili Wang, Zhenyu Jia split-read analysis. Bioinformatics, 28: i333–i339.
Ethics approval and consent to participate https://doi.org/10.1093/bioinformatics/bts378
10. Layer RM, Chiang C, Quinlan AR, et al., 2014, LUMPY:
Not applicable.
A probabilistic framework for structural variant discovery.
Consent for publication Genome Biol, 15: R84. 10.1186/gb-2014-15-6-r84
11. Cameron DL, Schröder J, Penington JS, et al., 2017, GRIDSS:
Not applicable. Sensitive and specific genomic rearrangement detection
using positional de Bruijn graph assembly. Genome Res,
Availability of data 27: 2050–2060.
The code can be obtained from: https://github.com/ https://doi.org/10.1101/gr.222109.117
YULEITSINGTAO/SVIV
12. Cameron DL, Baber J, Shale C, et al., 2021, GRIDSS2:
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Volume 2 Issue 2 (2023) 6 https://doi.org/10.36922/td.0894