Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learning.

Genome engineering is undergoing unprecedented development and is now becoming widely available. Genetic engineering attribution can make sequence-lab associations and assist forensic experts in ensuring responsible biotechnology innovation and reducing misuse of engineered DNA sequences. Here we propose a method based on metric learning to rank the most likely labs of origin while simultaneously generating embeddings for plasmid sequences and labs.