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

Author

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  • Alec A. K. Nielsen

    (Massachusetts Institute of Technology)

  • Christopher A. Voigt

    (Massachusetts Institute of Technology)

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.

Suggested Citation

  • Alec A. K. Nielsen & Christopher A. Voigt, 2018. "Deep learning to predict the lab-of-origin of engineered DNA," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05378-z
    DOI: 10.1038/s41467-018-05378-z
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    Cited by:

    1. Benjamin D Lee & Anthony Gitter & Casey S Greene & Sebastian Raschka & Finlay Maguire & Alexander J Titus & Michael D Kessler & Alexandra J Lee & Marc G Chevrette & Paul Allen Stewart & Thiago Britto-, 2022. "Ten quick tips for deep learning in biology," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-20, March.
    2. Tomasz Śmiałkowski & Andrzej Czyżewski, 2022. "Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters," Energies, MDPI, vol. 15(24), pages 1-23, December.
    3. 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 & William , 2022. "Analysis of the first genetic engineering attribution challenge," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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