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

Kara D Loos^1,2, Mark Horsman^1,3, Jeff Tuff¹

¹National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, Manitoba, R3E 3R2, Canada.

Microbial Genomics

|January 27, 2026

Summary

Standardized test datasets for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic analysis were developed to ensure reliable public health surveillance. This framework enhances data quality and comparability across different Canadian laboratories and platforms.

Keywords:

SARS-CoV-2 bioinformatics genomics quality control training

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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.