SPADA Meeting Book

designs and also guide validation studies. The proposed computational approaches also result in 46 higher performing assays with better sensitivity, specificity and lower limit of detection and 47 reduce the possibility of assay failure due to signature erosion. To provide clarity, an extensive 48 glossary of defined terms is provided.

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2.0 Background and Rationale

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Nucleic acid-based assays, such as real time polymerase chain reaction (PCR), are the 52 mainstay of clinical diagnostics and biosurveillance. A typical PCR assay design begins with 53 computational (“ in silico ”) identification of a unique region (signature) that can support the 54 binding of primer and probe sequences for target-specific amplification as a means of detecting 55 the presence of the target organism. This step is followed by wet-lab testing of the primers and 56 probes using genomic deoxyribonucleic acid (DNA) or reverse transcribed ribonucleic acid 57 (RNA) and performance-optimization of selected assays. In addition, extensive testing of the 58 assay in the intended clinical matrix is required to evaluate assay parameters, such as: limit of 59 detection, sensitivity (probability of detection) and specificity (see glossary for definitions). The 60 sensitivity and specificity of the assay are experimentally determined using a set of target 61 (inclusivity) strains, near neighbor (exclusivity) strains, and matrix relevant (background) 62 organisms. Assay performance also needs to be measured in assay-specific matrices (i.e. blood, 63 stool, water, soil, etc.). Often, assays are computationally designed using a set of available 64 genomic/gene sequences at that time and then experimentally validated for signature presence in 65 all available samples of the target organism (the inclusivity panel) and validated for signature 66 absence in many other samples that do not contain the target (the exclusivity panel and the 67

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