Abstract
Investigators discuss new findings in Machine Learning. According to news reporting originating in Stanford, California, by NewsRx journalists, research stated, “Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biolog-ical discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage.” Financial supporters for this research include UCSD Eureka! Scholarship, National Institutes of Health (NIH) - USA, Defense Advanced Research Projects Agency (DARPA), Emergent Ventures, Shurl and Kay Curci Foundation, Rainwater Charitable Foundation, Arc Institute as a Core Investigator. The news reporters obtained a quote from the research from Stanford University, “Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons.”
NETL
NSTL
Elsevier