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The Power of Agnostic Detection

Global Health Surveillance Human Health
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An Early Warning System from Zoonotic Disease to Antimicrobial Resistance

In cases of infectious disease outbreaks or emerging antimicrobial resistance, time is critical and waiting to identify a pathogen using traditional target specific methods could mean losing valuable response time and efficacy. Further, agnostic detection methods such as metagenomic sequencing analyze all of the genetic material in a sample, enabling the detection of unknown or unexpected pathogens. This makes them particularly powerful for the detection of emerging infectious diseases or engineered biothreats that may not yet be on the threat radar. Such an approach provides a broad safety net, ensuring that even unexpected or novel pathogens are identified early, improving response efforts and saving lives. Read more about the importance of using agnostic detection methods to detect these threats in “Agnostic Detection Methods for Pathogens.” 

Agnostic methods can also be incorporated into multilayered infectious disease surveillance by complimenting targeted approaches like PCR, which is fast and accurate for known threats. Together, they create a more comprehensive system where targeted methods handle routine diagnostics, while agnostic approaches act as a safeguard for emerging or unexpected pathogens. This integration enables public health systems to balance speed, cost and preparedness, ensuring we can respond effectively to both known and unknown infectious threats. 

Further, agnostic detection methods can play a pivotal role in biothreat surveillance by providing the ability to monitor and identify a wide array of pathogens from environmental samples, human samples, or even animal reservoirs without prior knowledge about the target. Use of this technology can help us detect engineered pathogens or bioweapons early, even if they’re designed to mimic naturally occurring organisms. By continuously monitoring high risk areas and populations, we can stay ahead of potential biothreats and intervene before they lead to large–scale outbreaks or attacks. 

 

Interested in learning more about the power of agnostic detection? Listen to the “Agnostic Detection” episode of the Science Diction podcast!

The Role of Bioinformatics
Use of the correct anomaly detection computational algorithms effectively is the principal task of bioinformatics in the agnostic detection space. While database methods can be effective at identification of specific known targets, the ability to identify potentially never seen or sequenced biological signals is critical for the biosurveillance space. Algorithms like DeepSVDD and autoencoders can leverage deep learning to construct a profile of what a typical or expected biological signature might look like, which can then be used to flag anomalous detections as sufficiently far enough away from the typical profile to have concern. Using a variety of feature encodings of the raw data like k–mer frequency, taxonomic labels, gene frequencies, and protein abundance in ensemble cast a broad net and improve the confidence in agnostic detection. 

Additionally, clustering based algorithms can leverage the intrinsic distance metrics used to calculate clusters and similarities to make assertions about possible detection events. For example, if a sample were to fail to cluster within a known group that, would suggest it is not like the previously sequenced and analyzed data. In the case of zoonotic disease, this could represent a new strain. Additionally, clustering could be done for antimicrobial analysis, identifying groups as either resistant or non-resistant. The location of a novel sample on the clustering embedding can provide information about whether antimicrobial resistance is present or not.

While the opportunities for the use of agnostic detection methods are significant, use of this technology is not without its challenges. Read more in “Agnostic Detection Challenges and Opportunities.”  

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