Viroscope: Plant viral diagnosis from high-throughput sequencing data using biologically-informed genome assembly...

Area of Science:

• Genomics
• Bioinformatics
• Pathogen Detection

Background:

• High-throughput sequencing (HTS) offers powerful pathogen detection but faces challenges in diagnostic certainty, especially with low sequencing coverage.
• Current data analysis pipelines lack standardized metrics and harmonized thresholds, hindering real-world implementation of HTS for disease diagnosis.

Purpose of the Study:

• To develop a robust method for precise viral diagnosis using biologically-informed viral genome assembly coverage.
• To improve diagnostic certainty in high-throughput sequencing (HTS) data analysis, particularly for low-coverage scenarios.

Main Methods:

• Proposed a novel approach defining genome coverage thresholds based on the identification of viral replicases, essential for viral life cycles.
• Developed and validated the Viroscope analysis pipeline using field, synthetic, and published datasets.
• Created Viroscope.io, a web service for on-demand HTS data viral diagnosis.

Main Results:

• Demonstrated that Viroscope provides sensitive and specific viral detection.
• The biologically-informed coverage thresholds improve diagnostic certainty in HTS data.
• Viroscope.io facilitates adoption by phytosanitary agencies for precise viral diagnosis.

Conclusions:

• Biologically-informed viral genome assembly coverage enhances diagnostic certainty for high-throughput sequencing (HTS).
• The Viroscope pipeline and web service offer a sensitive, specific, and accessible solution for pathogen diagnosis.
• This approach supports the broader implementation of HTS in real-world phytosanitary applications.