Metadata-driven comparative analysis tool for sequences (meta-CATS): an automated process for identifying significant...

Area of Science:

• Virology
• Bioinformatics
• Genomics

Background:

• The Virus Pathogen Resource (ViPR) and Influenza Research Database (IRD) provide extensive viral sequence data.
• Identifying correlations between viral sequence variations and phenotypic attributes is crucial for understanding virus evolution and developing countermeasures.
• Existing tools may lack the integrated statistical power to directly link sequence data with metadata attributes.

Purpose of the Study:

• To develop and introduce a metadata-driven tool, meta-CATS, for comparative sequence analysis.
• To enable researchers to statistically identify significant sequence variations associated with specific virus metadata.
• To facilitate the discovery of genotype-phenotype correlations in viral pathogens.

Main Methods:

• Meta-CATS utilizes a metadata-driven approach to analyze nucleotide and amino acid sequences.
• Users select sequence datasets and group them based on metadata attributes (e.g., isolation location, host).
• The tool performs statistical tests at each aligned sequence position to identify significant differences between groups.

Main Results:

• Meta-CATS successfully identified sequence biomarkers associated with dengue viruses from different hemispheres.
• The tool differentiated unique variations in the NS1 protein across the four dengue serotypes.
• Demonstrated the capability to link specific sequence residues to distinct virus attributes.

Conclusions:

• Meta-CATS is a valuable, freely available resource for virology researchers.
• The tool enhances the ability to uncover genotype-phenotype correlations.
• Findings support the development of improved vaccines, diagnostics, and therapeutics through sequence-based insights.