Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers
Identifying structural variants (SVs) under positive selection in cancer is challenging. Here, the authors develop CSVDriver, a method that computes SV breakpoint proximity and the contribution of elements such as topologically associating domains, and identifies loci that show signs of positive sel...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-09-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-32945-2 |
| _version_ | 1818026022980812800 |
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| author | Alexander Martinez-Fundichely Austin Dixon Ekta Khurana |
| author_facet | Alexander Martinez-Fundichely Austin Dixon Ekta Khurana |
| author_sort | Alexander Martinez-Fundichely |
| collection | DOAJ |
| description | Identifying structural variants (SVs) under positive selection in cancer is challenging. Here, the authors develop CSVDriver, a method that computes SV breakpoint proximity and the contribution of elements such as topologically associating domains, and identifies loci that show signs of positive selection and contain known and putative drivers. |
| first_indexed | 2024-12-10T04:25:24Z |
| format | Article |
| id | doaj.art-dd44273016224ecdac1d364c12046c30 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-10T04:25:24Z |
| publishDate | 2022-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-dd44273016224ecdac1d364c12046c302022-12-22T02:02:18ZengNature PortfolioNature Communications2041-17232022-09-0113111510.1038/s41467-022-32945-2Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer driversAlexander Martinez-Fundichely0Austin Dixon1Ekta Khurana2Sandra and Edward Meyer Cancer Center, Weill Cornell MedicineInstitute for Computational Biomedicine, Weill Cornell MedicineSandra and Edward Meyer Cancer Center, Weill Cornell MedicineIdentifying structural variants (SVs) under positive selection in cancer is challenging. Here, the authors develop CSVDriver, a method that computes SV breakpoint proximity and the contribution of elements such as topologically associating domains, and identifies loci that show signs of positive selection and contain known and putative drivers.https://doi.org/10.1038/s41467-022-32945-2 |
| spellingShingle | Alexander Martinez-Fundichely Austin Dixon Ekta Khurana Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers Nature Communications |
| title | Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers |
| title_full | Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers |
| title_fullStr | Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers |
| title_full_unstemmed | Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers |
| title_short | Modeling tissue-specific breakpoint proximity of structural variations from whole-genomes to identify cancer drivers |
| title_sort | modeling tissue specific breakpoint proximity of structural variations from whole genomes to identify cancer drivers |
| url | https://doi.org/10.1038/s41467-022-32945-2 |
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