Analysis of the first genetic engineering attribution challenge

Oliver M. Crook, Kelsey Lane Warmbrod, Greg Lipstein, Christine Chung, Christopher W. Bakerlee, T. Greg McKelvey, Shelly R. Holland, Jacob L. Swett, Kevin M. Esvelt, Ethan C. Alley () and William J. Bradshaw ()
Additional contact information
Oliver M. Crook: University of Oxford
Kelsey Lane Warmbrod: Johns Hopkins Center for Health Security, Johns Hopkins Bloomberg School of Public Health
Greg Lipstein: DrivenData Inc
Christine Chung: DrivenData Inc
Christopher W. Bakerlee: altLabs Inc
T. Greg McKelvey: altLabs Inc
Shelly R. Holland: altLabs Inc
Jacob L. Swett: altLabs Inc
Kevin M. Esvelt: altLabs Inc
Ethan C. Alley: altLabs Inc
William J. Bradshaw: altLabs Inc

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-35032-8

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