CRISPRO: Identification of functional protein coding sequences based on genome editing dense mutagenesis Jin-Soo Kim

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Article number169
JournalGenome biology
Volume19
Issue number1
DOIs
Publication statusPublished - Oct 19 2018

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

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    Schoonenberg, V. A. C., Cole, M. A., Yao, Q., MacIas-Treviño, C., Sher, F., Schupp, P. G., Canver, M. C., Maeda, T., Pinello, L., & Bauer, D. E. (2018). CRISPRO: Identification of functional protein coding sequences based on genome editing dense mutagenesis Jin-Soo Kim. Genome biology, 19(1), [169]. https://doi.org/10.1186/s13059-018-1563-5