Deep learning to predict the lab-of-origin of engineered DNA.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 30087331.
- Also identified by DOI 10.1038/s41467-018-05378-z and PMC identifier 6081423.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.
Medical subject headings
- DNA
- Deep Learning
- Genetic Engineering
- Neural Networks, Computer