Nature BiotechnologyGenomicsPeer-reviewed journal article
Model-directed generation of CRISPR-Cas13a guide RNAs designs artificial sequences that improve nucleic acid detection
Sreekar MantenaiD, Priya P. PillaiiD, Brittany A. PetrosiD, Nicole L. WelchiD, Cameron MyhrvoldiD, Pardis Christine SabetiiD, Hayden C. MetskyiD
Abstract
Generating maximally-fit biological sequences has the potential to transform CRISPR guide RNA design as it has other areas of biomedicine. Here, we introduce model-directed exploration algorithms (MEAs) for designing maximally-fit, artificial CRISPR-Cas13a guides--with multiple mismatches to any natural sequence--that are tailored for desired properties around nucleic acid diagnostics. We find that MEA-designed guides offer more sensitive detection of diverse pathogens and discrimination of pathogen variants compared to guides derived directly from natural sequences, and illuminate interpretable design principles that broaden Cas13a targeting.
- assay: CRISPR screen
- research focus: drug response / drug discovery
- research focus: sequence-to-function modeling
- approach: deep learning
- approach: statistical model
- Citations
- 41
- FWCI
- 5.58
- Field percentile
- top 2%
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