Cargando…
Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
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...
Autores principales: | Martinez-Fundichely, Alexander, Dixon, Austin, Khurana, Ekta |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9512825/ https://www.ncbi.nlm.nih.gov/pubmed/36163358 http://dx.doi.org/10.1038/s41467-022-32945-2 |
Ejemplares similares
-
CNCDatabase: a database of non-coding cancer drivers
por: Liu, Eric Minwei, et al.
Publicado: (2020) -
Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network
por: Dhingra, Priyanka, et al.
Publicado: (2017) -
DeepMILO: a deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure
por: Trieu, Tuan, et al.
Publicado: (2020) -
Whole Genome Sequencing identifies reciprocal translocation hT2(I;III) breakpoints.
por: Flibotte, Stephane, et al.
Publicado: (2021) -
Accurate Detection of Recombinant Breakpoints in Whole-Genome Alignments
por: Westesson, Oscar, et al.
Publicado: (2009)