USCF Yamanaka LabDr. Perli used the guides suggested by Weissman Machine learning algorithm using phenotype based scoring and off-target filtering to do Real-Time qRT-PCR experiments. And found few guides hitting the target better compared to others. This itself proves that a better Machine Learning based algorithm that is trained with quality data would predict guides better.
When we used our algorithm to predict scores of the guides they are very close to lab results compare to Weissman lab scores. You can find more here.
NYC_EarthiansNYC Earthians another iGEM 2020 competing team was doing a project based on CRISPR targeting the APOL3 gene in COW (Bovine).
When they tried to find guide RNAs they ended up with 100 odd guides and didn't know what to use and which one will be effective.
They in fact approached us to see if our tool can help them to find the right guide RNA. Our tool which can find good guides based on our Machine Learning Model including Off-target stringency is the right choice. We are in the process of adding support to Bovine so we could not give them a guide right away. This itself is another proof of our concept, how finding a good sgRNA based on ML/AI for different CRISPR systems can help biologists. We will continue adding support and will let them know once we finish testing.