Kate D Crawford

The paper uses high-throughput variant libraries and machine learning to identify design rules for improving retron gene editors in both yeast and human genome editing.

The paper experimentally characterizes retrons to determine their RT-DNA sequences, production rates, and editing efficacy across bacterial, phage, and human genomes.

The paper improves retron architectures to enhance DNA production across different organisms, leading to more efficient genome editing.