ArticleNature communications2021
PlasmidHawk improves lab of origin prediction of engineered plasmids using sequence alignment.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Resilience of recombinant antibiotic resistance gene-containing plasmids against common cell culture disposal methods.Cell reports methods · 2026Article
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- Responsible AI in biotechnology: balancing discovery, innovation and biosecurity risks.Frontiers in bioengineering and biotechnology · 2025Review
- The need for adaptability in detection, characterization, and attribution of biosecurity threats.Nature communications · 2024Review
- Cryptographic approaches to authenticating synthetic DNA sequences.Trends in biotechnology · 2024Review
- Identifying widespread and recurrent variants of genetic parts to improve annotation of engineered DNA sequences.PloS one · 2024Article
- Synsor: a tool for alignment-free detection of engineered DNA sequences.Frontiers in bioengineering and biotechnology · 2024Article
- Analysis of the first genetic engineering attribution challenge.Nature communications · 2022Article
- Genomics enters the deep learning era.PeerJ · 2022Review
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
With advances in synthetic biology and genome engineering comes a heightened awareness of potential misuse related to biosafety concerns. A recent study employed machine learning to identify the lab-of-origin of DNA sequences to help mitigate some of these concerns. Despite their promising results, this deep learning based approach had limited accuracy, was computationally expensive to train, and wasn't able to provide the precise features that were used in its predictions. To address these shortcomings, we developed PlasmidHawk for lab-of-origin prediction. Compared to a machine learning approach, PlasmidHawk has higher prediction accuracy; PlasmidHawk can successfully predict unknown sequences' depositing labs 76% of the time and 85% of the time the correct lab is in the top 10 candidates. In addition, PlasmidHawk can precisely single out the signature sub-sequences that are responsible for the lab-of-origin detection. In summary, PlasmidHawk represents an explainable and accurate tool for lab-of-origin prediction of synthetic plasmid sequences. PlasmidHawk is available at https://gitlab.com/treangenlab/plasmidhawk.git .
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.