# Computational Methods for Designing Optimal Genomics-guided Viral Diagnostics

> **NIH NIH K01** · BROAD INSTITUTE, INC. · 2021 · $129,165

## Abstract

Project Summary/Abstract
Viral genome sequencing is growing exponentially and cutting-edge molecular technologies, guided by
genomic data, show great promise in detecting and responding to viruses. Yet we lack a computational
framework that efficiently leverages viral data to design the nucleic or amino acid sequences applied by these
technologies. The proposal provides a career development plan to (i) build computational techniques —
algorithms, models, and software — that yield highly accurate diagnostic assays, with potential to outperform
existing ones, and (ii) use the techniques to proactively design assays for detecting 1,000s of viruses.
The project will first develop methods for designing optimal viral genome-informed diagnostics. The study will
formulate objective functions that evaluate an assay’s performance across a distribution of anticipated viral
targets. Combinatorial optimization algorithms and generative models, constructed in the study, will optimize
the functions. The project will also develop datasets for training predictive models of assay performance, which
are used in the objective functions, focusing on CRISPR-, amplification-, and antigen-based diagnostics.
Preliminary experimental results suggest such models can render assays with exquisite sensitivity and
specificity. The study will compare the algorithmically-designed assays to state-of-the-art tests for four viruses.
With these methods, the project will design diagnostic assays that are species-specific and broadly effective
across genomic diversity for all viruses known to infect vertebrates. The study will build a system to monitor the
assays’ effectiveness against emerging viral genomic diversity and to continually update them as needed. To
enable the broad adoption of these methods, the project will implement them efficiently in accessible software.
The proposal aligns with a NIAID goal of improving diagnostics via data science. The methods developed here
may also aid therapy and vaccine design, and will leave the world better prepared to combat viral outbreaks.
The career development award will provide training for the candidate in applied areas of long-term interest to
his career. The candidate has previous experience in developing computational methods and analyzing viral
genomes. Through the award, he will gain new knowledge and skills in diagnostic applications, alongside
formal and informal training in immunology, bioengineering, and related laboratory techniques. This training will
help the candidate progress toward therapy and vaccine applications that could benefit from advanced
computational methods. The Broad Institute provides a supportive environment for the candidate’s
development, including career development workshops, research seminars aligned with the proposed plan,
and opportunities to initiate collaborations with scientists having expertise complementary to the candidate’s.
The research and training will help him form an independent research ...

## Key facts

- **NIH application ID:** 10284445
- **Project number:** 1K01AI163498-01
- **Recipient organization:** BROAD INSTITUTE, INC.
- **Principal Investigator:** Hayden C Metsky
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $129,165
- **Award type:** 1
- **Project period:** 2021-06-09 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10284445

## Citation

> US National Institutes of Health, RePORTER application 10284445, Computational Methods for Designing Optimal Genomics-guided Viral Diagnostics (1K01AI163498-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10284445. Licensed CC0.

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