Ensemble Detection of DNA Engineering Signatures

Aaron Adler, Joel S. Bader, Brian Basnight, Benjamin W. Booth, Jitong Cai, Elizabeth Cho, Joseph H. Collins, Yuchen Ge, John Grothendieck, Kevin Keating, Tyler Marshall, Anton Persikov, Helen Scott, Roy Siegelmann, Mona Singh, Allison Taggart, Benjamin Toll, Kenneth H. Wan, Daniel Wyschogrod, Fusun YamanEric M. Young, Susan E. Celniker, Nicholas Roehner

Research output: Contribution to journalArticlepeer-review

Abstract

Synthetic biology is creating genetically engineered organisms at an increasing rate for many potentially valuable applications, but this potential comes with the risk of misuse or accidental release. To begin to address this issue, we have developed a system called GUARDIAN that can automatically detect signatures of engineering in DNA sequencing data, and we have conducted a blinded test of this system using a curated Test and Evaluation (T&E) data set. GUARDIAN uses an ensemble approach based on the guiding principle that no single approach is likely to be able to detect engineering with perfect accuracy. Critically, ensembling enables GUARDIAN to detect sequence inserts in 13 target organisms with a high degree of specificity that requires no subject matter expert (SME) review.

Original languageEnglish (US)
Pages (from-to)1105-1115
Number of pages11
JournalACS Synthetic Biology
Volume13
Issue number4
DOIs
StatePublished - Apr 19 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)

Keywords

  • artificial intelligence
  • bioinformatics
  • biosecurity
  • engineering detection
  • machine learning

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