Designing sensitive viral diagnostics with machine learning
Name
s41587-022-01213-5.pdf
Description
Published version
Size
6.45 MB
Format
Adobe PDF
Checksum (MD5)
0a1ae8c2221d992cb301653403490b18
Author(s)
Metsky, Hayden C
Welch, Nicole L
Pillai, Priya P
Haradhvala, Nicholas J
Rumker, Laurie
Mantena, Sreekar
Zhang, Yibin B
Yang, David K
Ackerman, Cheri M
Weller, Juliane
Date Issued
2022
Journal
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Citation
Metsky, Hayden C, Welch, Nicole L, Pillai, Priya P, Haradhvala, Nicholas J, Rumker, Laurie et al. 2022. "Designing sensitive viral diagnostics with machine learning." Nature Biotechnology, 40 (7).
Version
Final published version
Abstract
AbstractDesign of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome’s conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic–target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus’s genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT’s designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus’s variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41587-022-01213-5
