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. 2022 Nov 30;13:7374. doi: 10.1038/s41467-022-35032-8

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a The creation of any synthetic nucleic-acid sequence involves numerous design decisions, each of which leaves a mark in the resulting sequence. Genetic engineering attribution (GEA) aims to use these marks to identify the designer. b Misclassification rate (1-(Top-N accuracy)) of past ML approaches to GEA on the Addgene plasmid database, compared to BLAST (left) and the results of the Genetic Engineering Attribution Challenge (GEAC, right). Lower misclassification rates indicate higher accuracy. Our BLAST method achieves higher accuracy than previous implementations; see Methods for details. c In the GEAC, teams were provided with engineered plasmid sequences from Addgene, alongside basic metadata for each plasmid. Lab-of-origin labels were provided for the training dataset, but withheld from the leaderboard and holdout test datasets. In the Prediction Track, teams competed to identify these withheld labs-of-origin with the greatest top-10 accuracy. In the Innovation Track, high-scoring teams from the Prediction Track were then invited to submit reports describing their approaches to a panel of expert judges for assessment.