Pathogen genomic surveillance and the AI revolution

Area of Science:

• Genomics
• Bioinformatics
• Artificial Intelligence

Background:

• The COVID-19 pandemic accelerated viral sequencing, establishing genomic surveillance as crucial for tracking virus evolution.
• Analyzing vast amounts of pathogen genomic data presents significant computational challenges.

Purpose of the Study:

• To explore the application of protein language models (pLMs) in analyzing pathogen genomic data.
• To demonstrate how pLMs can predict viral properties and evolutionary trajectories.
• To propose a framework for incorporating pLMs into existing genomic surveillance systems.

Main Methods:

• Utilizing state-of-the-art artificial intelligence, specifically protein language models (pLMs).
• Applying pLMs to analyze genomic sequences from circulating viruses.
• Developing a framework for integrating pLM analysis into genomic surveillance workflows.

Main Results:

• pLMs show significant potential for effective analysis of pathogen genomic data.
• Examples demonstrate pLM capabilities in predicting viral characteristics and evolutionary patterns.
• A framework for pLM integration into genomic surveillance is outlined.

Conclusions:

• Protein language models represent a significant advancement in analyzing pathogen genomic data.
• pLMs can enhance the predictive power and efficiency of genomic surveillance for infectious diseases.
• Integrating pLMs into surveillance pipelines will improve our ability to monitor and respond to viral evolution.