Analysis of the first genetic engineering attribution challenge

Journal article

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.

Authors: Crook, OM; Warmbrod, KL; Lipstein, G; Chung, C; Bakerlee, CW

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Communications
• Volume: 13
• Issue: 1
• Pages: 7374-7374
• Article number: 7374
• Publication date: 2022-11-30
• DOI: 10.1038/s41467-022-35032-8
• EISSN: 2041-1723
• ISSN: 2041-1723
• Language: English
• Keywords: Biology; Attribution; Data science; Machine learning; Competition (biology); Computer science; Artificial intelligence