CoMIT: A Bioinformatic Pipeline for Risk-based Prediction of COVID-19 Test Inclusivity

Background The global Coronavirus Disease 2019 (COVID-19) pandemic highlighted the need to quickly diagnose infections to identify and prevent viral spread in the population. In response to the pandemic, BioFire Defense leveraged its PCR-based “lab-in-a-pouch” technology for expedited development of the BioFire® COVID-19 Test, a novel in vitro diagnostic detecting SARS-CoV-2 nucleic acid in human samples. Following clearance of an in vitro diagnostic device, regulatory bodies such as the U.S. Food and Drug Administration (FDA) require regular post market surveillance to monitor test performance against viral lineages circulating in the field, using predictive in silico inclusivity evaluations. Exponential increases in the number of sequences deposited in bioinformatic repositories such as GISAID, during the pandemic, impeded progress in meeting these post market requirements. In response, BioFire Defense developed a new bioinformatic tool to overcome scalability problems and the loss of accuracy encountered with the standard inclusivity method. Results The Coronavirus Monitoring for Inclusivity Tool (CoMIT) uses the Variant Sorter Algorithm to sidestep multiple sequence alignments, a significant barrier inherent in the standard inclusivity method. The implementation of CoMIT and its Variant Sorter Algorithm are described. Automated summary tables and visualizations from a typical inclusivity evaluation are presented. We report our approach to filter and display relevant information in the pipeline outputs using risk factors tied to test performance. Conclusions BioFire Defense has developed CoMIT, an automated bioinformatic pipeline for efficient processing and reporting of variant inclusivity from the GISAID EpiCoV™ repository. This tool ensures continuous and comprehensive post market evaluations of BioFire COVID-19 Test performance even from datasets large enough to impede standard inclusivity analyses. CoMIT’s low computational space complexity and modular code allow this tool to be generalized for inclusivity monitoring of multianalyte or single analyte tests with complex assay designs and/or highly variable targets. CoMIT’s databasing capabilities and metadata handling hold the potential for new investigations to improve readiness for future outbreaks.