CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis

• Published in: Genome Biology Vol. 19; no. 1; p. 169
• Main Authors: Schoonenberg, Vivien A. C., Cole, Mitchel A., Yao, Qiuming, Macias-Treviño, Claudio, Sher, Falak, Schupp, Patrick G., Canver, Matthew C., Maeda, Takahiro, Pinello, Luca, Bauer, Daniel E.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 19.10.2018; Springer Nature B.V; BMC
• Subjects: Amino acids; Animal Genetics and Genomics; Artificial intelligence; Bioinformatics; Biomedical and Life Sciences; Cell Line; Comprehensive; Computer applications; CRISPR; CRISPR screen; CRISPR-Cas systems; CRISPR-Cas Systems - genetics; data collection; Datasets; Deoxyribonucleic acid; DNA; Evolutionary Biology; Gene Editing; Gene mapping; Genes; Genome; Genome editing; Genomes; genotype-phenotype correlation; Genotypes; Human Genetics; Humans; Insights from Genome Editing; Kinases; Learning algorithms; Life Sciences; Method; Microbial Genetics and Genomics; Molecular Sequence Annotation; Mutagenesis; Mutagenesis - genetics; Mutation; Open Reading Frames - genetics; Open source software; Phenotypes; Pipelines; Plant Genetics and Genomics; prediction; Protein structure; Protein Structure, Secondary; Proteins; Ribonucleic acid; RNA; RNA, Guide, CRISPR-Cas Systems - genetics; RNA, Messenger - genetics; RNA, Messenger - metabolism; Saturating; Structure-function relationships; Tiling; Visualization; Domains; Visualization; Mutagenesis; Saturating; CRISPR screen; Tiling; Software; Comprehensive; Protein structure
• ISSN: 1474-760X; 1474-7596; 1474-760X
• DOI: 10.1186/s13059-018-1563-5

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 .