Maximal viral information recovery from sequence data using VirMAP.

Area of Science:

• Virology
• Bioinformatics
• Metagenomics

Background:

• Accurate classification of the human virome is critical for understanding viral roles in health and disease.
• Current viral taxonomic characterization methods using metagenomic data are limited by high background noise and signal crosstalk.
• There is a need for sensitive, specific, and practical pipelines for precise viral classification and post hoc analysis.

Purpose of the Study:

• To develop a novel computational pipeline, VirMAP, for accurate taxonomic classification of viral reconstructions from metagenomic data.
• To overcome limitations of existing methods by merging nucleotide and protein information.
• To enable viral classification independent of genome coverage or read overlap.

Main Methods:

• Development of VirMAP, a pipeline that integrates nucleotide and protein sequence information.
• Classification of viral reconstructions using a hybrid approach.
• Validation of VirMAP using published datasets and diverse viral mock communities (RNA viruses, DNA viruses, bacteriophages).

Main Results:

• VirMAP successfully classifies viral reconstructions by merging nucleotide and protein data.
• The method overcomes limitations of high background noise and signal crosstalk inherent in metagenomic data.
• Validation demonstrated VirMAP's effectiveness across various viral types and data complexities.

Conclusions:

• VirMAP provides a robust and accurate method for human virome classification.
• The pipeline enhances metagenomic studies for virome-host interactions and biosurveillance.
• VirMAP strengthens capabilities in molecular epidemiology reporting and viral diagnostics.