Deep learning to predict the lab-of-origin of engineered DNA

• Published in: Nature communications Vol. 9; no. 1; pp. 3135 - 10
• Main Authors: Nielsen, Alec A. K., Voigt, Christopher A.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 07.08.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/61; Artificial neural networks; Bayes Theorem; Deep Learning; Deoxyribonucleic acid; Design; DNA; Gene sequencing; Genetic engineering; Genetic Engineering - methods; Humanities and Social Sciences; Image Processing, Computer-Assisted; Intellectual property; multidisciplinary; Mutation; Neural networks; Neural Networks, Computer; Nucleotide sequence; Plasmids - metabolism; Science; Science (multidisciplinary); Software; Synthetic Biology
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-05378-z

Genetic engineering projects are rapidly growing in scale and complexity, driven by new tools to design and construct DNA. There is increasing concern that widened access to these technologies could lead to attempts to construct cells for malicious intent, illegal drug production, or to steal intellectual property. Determining the origin of a DNA sequence is difficult and time-consuming. Here deep learning is applied to predict the lab-of-origin of a DNA sequence. A convolutional neural network was trained on the Addgene plasmid dataset that contained 42,364 engineered DNA sequences from 2230 labs as of February 2016. The network correctly identifies the source lab 48% of the time and 70% it appears in the top 10 predicted labs. Often, there is not a single “smoking gun” that affiliates a DNA sequence with a lab. Rather, it is a combination of design choices that are individually common but collectively reveal the designer. The synthetic biology era has seen a rapidly growing number of engineered DNA sequences. Here, the authors develop a deep learning method to predict the lab-of-origin of a DNA sequence based on hidden design signatures.