CRISPR-SURF: discovering regulatory elements by deconvolution of CRISPR tiling screen data

CRISPR-SURF: discovering regulatory elements by deconvolution of CRISPR tiling screen data

Jonathan Y. Hsu, Charles P. Fulco, Mitchel A. Cole, Matthew C. Canver, Danilo Pellin, Falak Sher, Rick Farouni, Kendell Clement, Jimmy A. Guo, Luca Biasco, Stuart H. Orkin, Jesse M. Engreitz, Eric S. Lander, J. Keith Joung, Daniel E. Bauer & Luca Pinello

The methodology underlying the CRISPR-SURF framework leverages the concept that single guide RNAs (sgRNAs) represent a functional readout for base pairs within the perturbation range. This range depends on the CRISPR screening approach used: CRISPR–Cas nucleases introduce insertion and deletion (indel) mutations of varying lengths (typically <30 bp, although potentially varying with cell type), whereas CRISPRi and CRISPRa strategies may remodel chromatin structure across hundreds of nucleotides. Importantly, each CRISPR technology offers its own advantage: CRISPRi and CRISPRa strategies increase the likelihood of detecting regulatory elements, given their larger perturbation ranges, whereas CRISPR–Cas nucleases provide higher resolution on the boundaries of regulatory elements, given their sharper perturbation windows. Because each sgRNA perturbs variable-size regions around its target site, the sgRNA data from CRISPR tiling screens can be seen as imprecise measurements of an underlying genomic regulatory signal. To address this variable, we model these imprecise measurements by means of a convolution operation that accounts for the perturbation profiles associated with different CRISPR technologies.