  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. Top-scoring teams dramatically outperformed previous models at identifying
the true lab-of-origin of engineered 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.
