Synthetic biology offers new approaches to combat viruses, bacteria and other pathogens, with researchers from the Würzburg Helmholtz Institute for RNA-based Infection Research and the Helmholtz AI Cooperative using AI to develop a more accurate machine learning approach to predict the efficacy of CRISPR technologies. Molecular biological CRISPR technologies specifically alter or silence genes and inhibit protein production to combat pathogens and genetic diseases. One such tool is CRISPRi, which binds to DNA without cutting it to suppress gene expression, and researchers have created a machine learning approach to improve predictions of this method.
CRISPRi screens were used to train a machine learning approach to predict the efficacy of engineered guide RNAs deployed in the system. By applying a new machine learning method, the scientists have greatly improved the prediction accuracy for CRISPRi experiments. The team established comprehensible design rules for future CRISPRi experiments and validated their approach by conducting an independent screen targeting bacterial genes, showing that their predictions were much more accurate. The study also reveals that integrating data from multiple data sets significantly improves the predictive accuracy of guide RNAs.
Prior to the study, the lack of data was a major limiting factor for prediction accuracy. The new approach will be helpful in planning more effective CRISPRi experiments in the future and serve both biotechnology and basic research. Gene features matter, combining data from multiple CRISPRi screens improves predictions, and the study provides valuable insights for designing more effective CRISPRi experiments by predicting guide RNA efficiency.
The ainewsarticles.com article you just read is a brief synopsis; the original article can be found here: Read the Full Article…
