Optimizing data-driven excellence: Canada's approach to using pathogen test datasets for quality control, pipeline...

Area of Science:

• Genomic surveillance
• Public health
• Bioinformatics

Background:

• Pathogen genomic surveillance is crucial for public health, especially after the COVID-19 pandemic.
• Ensuring quality and comparability of genomic data across diverse settings is an urgent challenge.
• No universal framework currently exists to standardize genomic analysis results.

Purpose of the Study:

• To develop standardized test datasets for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic analysis in Canada.
• To establish a framework for validating sequencing workflows and bioinformatics pipelines.
• To enhance the accuracy, comparability, and reliability of genomic surveillance data.

Main Methods:

• Utilized curated genomic sequences from COVID-19 patients in Saskatchewan, Canada.
• Generated data using Illumina and Nanopore sequencing with well-characterized protocols.
• Included clean controls, variable lineages, and low-quality data; datasets are publicly available on Zenodo.

Main Results:

• Developed standardized test datasets for SARS-CoV-2 genomic analysis.
• Created a customized R script for comparing Illumina sequencing data.
• Provided data summary outputs and pipeline environment files to facilitate reproducibility.

Conclusions:

• The study provides essential bioinformatic pipeline validation tools and protocols for reliable genomic surveillance.
• Established a structured framework for computational validation, enhancing genomic data accuracy and comparability.
• This work supports effective outbreak response in the face of evolving viral strains and testing strategies.