CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis
Vivien A. C. Schoonenberg | Mitchel A. Cole | Qiuming Yao | Claudio Macias-Treviño | Falak Sher | Patrick G. Schupp | Matthew C. Canver | Takahiro Maeda | Luca Pinello | Daniel E. Bauer
Abstract CRISPR/Cas9 pooled screening permits parallel evaluation of comprehensive guide RNA libraries to systematically perturb protein coding sequences in situ and correlate with functional readouts. For the analysis and visualization of the resulting datasets, we develop CRISPRO, a computational pipeline that maps functional scores associated with guide RNAs to genomes, transcripts, and protein coordinates and structures. No currently available tool has similar functionality. The ensuing genotype-phenotype linear and three-dimensional maps raise hypotheses about structure-function relationships at discrete protein regions. Machine learning based on CRISPRO features improves prediction of guide RNA efficacy. The CRISPRO tool is freely available at gitlab.com/bauerlab/crispro.
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