PRIDICT2.0 is an advanced version of the original PRIDICT model designed for predicting the efficiency of prime editing guide RNAs. PRIDICT2.0 predicts the efficiency of large edits (up to 40 base pairs) including replacements, insertions, and deletions. The complementary model, ePRIDICT, improves prediction accuracy by considering the local chromatin environment and genomic context. read more here: link
Additionally, a comprehensive protocol detailing how to use the advanced machine learning models, PRIDICT2.0 and ePRIDICT, for systematic prime editing guide RNA (pegRNA) design with step-by-step instructions, including practical tips for high-throughput screening, see our detailed protocol link
Code: click here
App: click here
PRIDICT1.0 is a deep learning network built to accurately predict prime editing (pegRNA) efficiencies and product purity. The model was trained using a large set of disease-related human mutations to simplify pegRNA design, which is usually a slow, manual process. PRIDICT accurately predicts the efficiency for all standard edit types, including substitutions, small insertions, and small deletions. (read more here: link )
Code: click here
App: click here
