PathoSeq-QC: a decision support bioinformatics workflow for robust genomic surveillance

Area of Science:

• Bioinformatics
• Genomic Surveillance
• Pathogen Evolution

Background:

• High-throughput genomic sequencing is crucial for combating global health threats.
• Genomics, combined with bioinformatics and epidemiological data, provides insights into pathogen evolution and spread.
• Ensuring the quality of genomic surveillance data is essential for informed policy decisions.

Purpose of the Study:

• To develop a bioinformatics decision support workflow, PathoSeq-QC, to enhance the trustworthiness of genomic surveillance analyses.
• To provide a robust tool for assessing genomic data quality and variant calling for viral pathogens.
• To support public health policy by ensuring reliable evidence from pathogen surveillance.

Main Methods:

• PathoSeq-QC is a command-line tool developed using Python and R.
• The workflow integrates multiple quality assessment tools and performs multi-tool comparison for robust variant calling.
• It is designed to be modular for easy extension of functionalities.

Main Results:

• PathoSeq-QC evaluates raw genomic data quality.
• It assesses samples for single or multiple pathogen lineages.
• The tool provides robust variant calling and identifies recombinant viruses, novel, or known lineages.

Conclusions:

• PathoSeq-QC improves the reliability of genomic surveillance for SARS-CoV-2 and other viral threats.
• The workflow aids in generating trustworthy conclusions from genomic data for public health.
• Its modular design allows for future adaptability to emerging health threats.