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 Yaman; Eric M. Young; Susan E. Celniker; Nicholas Roehner

doi:10.1021/acssynbio.3c00398

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 journal › Article › peer-review

1 Scopus citations

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 language	English (US)
Pages (from-to)	1105-1115
Number of pages	11
Journal	ACS Synthetic Biology
Volume	13
Issue number	4
DOIs	https://doi.org/10.1021/acssynbio.3c00398
State	Published - Apr 19 2024

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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10.1021/acssynbio.3c00398

Cite this

Adler, A., Bader, J. S., Basnight, B., Booth, B. W., Cai, J., Cho, E., Collins, J. H., Ge, Y., Grothendieck, J., Keating, K., Marshall, T., Persikov, A., Scott, H., Siegelmann, R., Singh, M., Taggart, A., Toll, B., Wan, K. H., Wyschogrod, D., ... Roehner, N. (2024). Ensemble Detection of DNA Engineering Signatures. ACS Synthetic Biology, 13(4), 1105-1115. https://doi.org/10.1021/acssynbio.3c00398

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title = "Ensemble Detection of DNA Engineering Signatures",

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.",

keywords = "artificial intelligence, bioinformatics, biosecurity, engineering detection, machine learning",

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

note = "Publisher Copyright: {\textcopyright} 2024 American Chemical Society.",

year = "2024",

month = apr,

day = "19",

doi = "10.1021/acssynbio.3c00398",

language = "English (US)",

volume = "13",

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Adler, A, Bader, JS, Basnight, B, Booth, BW, Cai, J, Cho, E, Collins, JH, Ge, Y, Grothendieck, J, Keating, K, Marshall, T, Persikov, A, Scott, H, Siegelmann, R, Singh, M, Taggart, A, Toll, B, Wan, KH, Wyschogrod, D, Yaman, F, Young, EM, Celniker, SE & Roehner, N 2024, 'Ensemble Detection of DNA Engineering Signatures', ACS Synthetic Biology, vol. 13, no. 4, pp. 1105-1115. https://doi.org/10.1021/acssynbio.3c00398

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AU - Adler, Aaron

AU - Bader, Joel S.

AU - Basnight, Brian

AU - Booth, Benjamin W.

AU - Cai, Jitong

AU - Cho, Elizabeth

AU - Collins, Joseph H.

AU - Ge, Yuchen

AU - Grothendieck, John

AU - Keating, Kevin

AU - Marshall, Tyler

AU - Persikov, Anton

AU - Scott, Helen

AU - Siegelmann, Roy

AU - Singh, Mona

AU - Taggart, Allison

AU - Toll, Benjamin

AU - Wan, Kenneth H.

AU - Wyschogrod, Daniel

AU - Yaman, Fusun

AU - Young, Eric M.

AU - Celniker, Susan E.

AU - Roehner, Nicholas

PY - 2024/4/19

Y1 - 2024/4/19

N2 - 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.

AB - 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.

KW - artificial intelligence

KW - bioinformatics

KW - biosecurity

KW - engineering detection

KW - machine learning

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JF - ACS Synthetic Biology

IS - 4

ER -

Ensemble Detection of DNA Engineering Signatures

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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