Diagnostic targETEd seQuencing adjudicaTion (DETEQT): Algorithms for Adjudicating Targeted Infectious Disease Next-Generation Sequencing Panels

Abstract

Next-generation sequencing (NGS) for infectious disease diagnostics is a relatively new and underdeveloped concept. If this technology is to become a regulatory-grade clinical diagnostic, standardization in the form of locked-down assays and firmly established underlying processes is necessary. Targeted sequencing, specifically by amplification of genomic signatures, has the potential to bridge the gap between PCR- and NGS-based diagnostics; however, existing NGS assay panels lack validated analytical techniques to adjudicate high background and error-prone NGS data. Herein, we present the Diagnostic targETEd seQuencing adjudicaTion (DETEQT) software, consisting of an intuitive bioinformatics pipeline entailing a set of algorithms to translate raw sequencing data into positive, negative, and indeterminate diagnostic determinations. After basic read filtering and mapping, the software compares abundance and quality metrics against heuristic and fixed thresholds. A novel generalized quality function provides an amalgamated quality score for the match between sequence reads of an assay and panel targets, rather than considering each component factor independently. When evaluated against numerous assay samples and parameters (mock clinical, human, and nonhuman primate clinical data sets; diverse amplification strategies; downstream applications; and sequence platforms), DETEQT demonstrated improved rejection of false positives and accuracies >95%. Finally, DETEQT was implemented in the user-friendly Empowering the Development of Genomics Expertise (EDGE) bioinformatics platform, providing a complete, end-to-end solution that can be operated by nonexperts in a clinical laboratory setting.