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- W4297221569 abstract "Abstract Structural variations (SVs) in cancer cells often impact large genomic regions with functional consequences. However, identification of SVs under positive selection is a challenging task because little is known about the genomic features related to the background breakpoint distribution in different cancers. We report a method that uses a generalized additive model to investigate the breakpoint proximity curves from 2,382 whole-genomes of 32 cancer types. We find that a multivariate model, which includes linear and nonlinear partial contributions of various tissue-specific features and their interaction terms, can explain up to 57% of the observed deviance of breakpoint proximity. In particular, three-dimensional genomic features such as topologically associating domains (TADs), TAD-boundaries and their interaction with other features show significant contributions. The model is validated by identification of known cancer genes and revealed putative drivers in cancers different than those with previous evidence of positive selection." @default.
- W4297221569 created "2022-09-28" @default.
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- W4297221569 date "2022-09-26" @default.
- W4297221569 modified "2023-09-26" @default.
- W4297221569 title "Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers" @default.
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- W4297221569 doi "https://doi.org/10.1038/s41467-022-32945-2" @default.
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- W4297221569 hasPublicationYear "2022" @default.
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