Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores.

ABSTRACT: Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and

SUBMITTER: Toghrayee Z 

PROVIDER: S-EPMC10776685 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature