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are powerful tools for pinpointing cells' genetic dependencies — that is, genes that cells require for their survival and/or proliferation. However, such CRISPR screens are sensitive to , where genes that have been repeatedly duplicated within a cell (as commonly happens in cancer cells) can be flagged as essential regardless of whether they are or not.

To limit such false-positive hits, the Ó³»­´«Ã½'s project — a joint effort bringing together researchers from the Ó³»­´«Ã½ Cancer Program’s and teams, the institute's Genetic Perturbation Platform, and other Ó³»­´«Ã½ groups — has developed , a computational method that corrects pooled CRISPR screen data for the copy number effect and provides an unbiased view of cancer cells' genetic dependencies.

As they revealed in , the team benchmarked CERES against genome-scale CRISPR-Cas9 data from 342 cancer cell lines (the largest CRISPR knockout dataset generated in cancer lines to date) curated by the. The method greatly reduced false-positive readouts in the data, pinpointing known dependencies (e.g., KRAS mutations) and allowing new dependencies to become apparent.

The new dependency data complement the Dependency Map team's ongoing efforts to use functional genomic technologies like CRISPR and RNA interference (RNAi) to locate vulnerabilities that arise within cancer cells as they compensate for the loss of critical genes due to mutations or expression changes. Earlier this year the team announced that across 501 CCLE-curated cell lines — the fruits of a nearly 10-year effort.

CERES joins two prior computational methods the team has developed to filter false-positive results from functional genomic screen data: ATARiS and , both of which weed out so-called seed effects that commonly plague RNAi data.

Support for this study was provided by the National Cancer Institute (grant numbers U01CA176058, U01CA199253, and P01CA154303) and the Slim Initiative for Genomic Medicine in the Americas, a project funded by the Carlos Slim Foundation.