Ensemble Detection of DNA Engineering Signatures.
Aaron AdlerJoel S BaderBrian BasnightBenjamin W BoothJitong CaiElizabeth ChoJoseph H CollinsYuchen GeJohn GrothendieckKevin W KeatingTyler MarshallAnton PersikovHelen ScottRoy SiegelmannMona SinghAllison TaggartBenjamin TollKenneth H WanDaniel WyschogrodFusun YamanEric M YoungSusan E CelnikerNicholas RoehnerPublished in: ACS synthetic biology (2024)
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.
Keyphrases
- circulating tumor
- electronic health record
- cell free
- single molecule
- big data
- gram negative
- genome wide
- convolutional neural network
- chronic pain
- single cell
- study protocol
- randomized controlled trial
- clinical trial
- multidrug resistant
- data analysis
- risk assessment
- gene expression
- clinical practice
- climate change
- deep learning
- amino acid
- placebo controlled