Ribovirus classification by a polymerase barcode sequence.

Area of Science:

• Virology
• Bioinformatics
• Molecular Biology

Background:

• RNA viruses, specifically riboviruses, are abundant in eukaryotic viromes.
• High-throughput sequencing yields many novel riboviruses identified solely by sequence.
• Current classification methods struggle with sequence-only data, lacking standardized approaches for polymerase-based classification.

Purpose of the Study:

• To develop standardized methods for classifying RNA viruses based on polymerase sequences.
• To introduce a novel classification feature, the polymerase palmprint, for high-throughput viral characterization.

Main Methods:

• Defined the polymerase palmprint as a conserved segment within the polymerase palm sub-domain, identified by catalytic motifs.
• Developed Palmscan, an algorithm for identifying palmprints in nucleotide and amino acid sequences.
• Created PALMdb, a database of palmprints from public sequence data, and palmID, a web tool for palmprint analysis.

Main Results:

• Successfully delineated the polymerase palmprint using conserved catalytic motifs.
• Demonstrated the efficacy of Palmscan in identifying palmprints across diverse sequences.
• Established a functional workflow (Palmscan, PALMdb, palmID) for high-throughput RNA virus characterization.

Conclusions:

• The polymerase palmprint offers a robust method for RNA virus classification from sequence data.
• The developed computational tools provide a proof-of-concept for rapid, large-scale RNA virus discovery.
• These advancements are crucial for managing the accelerating discovery of novel RNA viruses.