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. These embeddings can be used to perform various downstream tasks, such as clustering DNA sequences and labs, as well as using them as features in machine learning models. Our approach employs a circular shift augmentation method and can correctly rank the lab of origin 90% of the time within its top-10 predictions. We also demonstrate that we can perform few-shot learning and obtain 76% top-10 accuracy using only 10% of the sequences. Finally, our approach can also extract key signatures in plasmid sequences for particular labs, allowing for an interpretable examination of the model's outputs.