Analysis of the first genetic engineering attribution challenge

Nat Commun. 2022 Nov 30;13(1):7374. doi: 10.1038/s41467-022-35032-8.


The ability to identify the designer of engineered biological sequences-termed genetic engineering attribution (GEA)-would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biotechnology*
  • Cloning, Molecular
  • Genetic Engineering*
  • Genetic Techniques
  • Social Perception