AlignDx: Enabling Automated, Cloud-Based Workflows for Streamlined Bioinformatic-Focused Pathogen Surveillance

Abstract

The rising trends in infectious disease burden, alongside the recent COVID-19 pandemic, underline the need for effective public health disease mitigation strategies like pathogen surveillance. Improvements to surveillance systems can be realized by incorporating a variety of surveillance data sources such as comprehensive genomics and simpler point-of-care approaches. In this thesis, a novel bioinformatic-focused surveillance platform is presented for executing scientific workflows in cloud-based environments. The platform in question, AlignDx, addresses gaps in available surveillance systems via its modular component-based design providing security, workflow management, summary reports and data archiving. Two workflows were created and tested using this platform. First, a metagenomics next-generation sequencing workflow was developed for human pathogenic virus surveillance. Using a clinical nasopharyngeal RNA-seq test dataset, the workflow performed well in classification of severe acute respiratory syndrome coronavirus 2. Also, a lateral flow assay workflow was developed for mass automated point-of-care pathogen surveillance. Using an original test dataset of serially diluted LFA images, under controlled lighting, the workflow performed well in correctly classifying tests according to their manually curated results. Overall, the AlignDx platform is an effective system for automated surveillance applications and its constituent workflows are flexible and primed for further development.