Improving viral annotation with artificial intelligence.

Area of Science:

• Microbiology and Virology
• Bioinformatics and Computational Biology

Background:

• Bacteriophages (phages) are essential but poorly understood components of microbial communities.
• Phages act as sensitive indicators of ecosystem conditions due to their host-dependent replication.
• Metagenomic sequencing reveals phage diversity, but most viral genomes remain unannotated due to extreme diversity.

Purpose of the Study:

• To explore the potential and limitations of large language models (LLMs) for annotating viral sequences.
• To address the need for novel methods to organize and annotate diverse viral sequences in metagenomic data.
• To facilitate biological discovery by identifying similarities among sequence-diverse viruses and viral-like elements.

Main Methods:

• Reviewing the fundamentals of protein language models (PLMs) and their application in sequence annotation.
• Leveraging self-supervised representation learning for remote viral protein homology detection.
• Analyzing the strengths and weaknesses of LLMs in the context of viral sequence annotation.

Main Results:

• Self-supervised learning methods can enhance homology detection for viral proteins, even in diverse datasets like the ocean virome.
• Functional content analysis aids in identifying similarities among sequence-divergent viruses.
• LLMs show promise for improving the annotation of uncharacterized viral sequences.

Conclusions:

• New computational approaches, particularly LLMs, are crucial for annotating the vast diversity of viral sequences in metagenomes.
• Understanding phage functional content is key to unlocking their potential in human and environmental health.
• Further development of LLMs is needed to overcome current limitations and broadly improve viral annotation.