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outlined in this document by no means obviate the need for wet lab testing, they can reduce the 45 amount of effort wasted on empirical optimization and iterative re-designs and also guide 46 validation studies. The proposed computational approaches also result in higher performing 47 assays with better sensitivity, specificity and lower limit of detection and reduce the possibility 48 of assay failure due to signature erosion. To provide clarity, an extensive glossary of defined 49 terms is provided. 53 mainstay of clinical diagnostics and biosurveillance. A typical PCR assay design begins with 54 computational (“ in silico ”) identification of a unique region (signature) that can support the 55 binding of primer and probe sequences for target-specific amplification as a means of detecting 56 the presence of the target organism. This step is followed by wet-lab testing of the primers and 57 probes using genomic deoxyribonucleic acid (DNA) or reverse transcribed ribonucleic acid 58 (RNA) and performance-optimization of selected assays. In addition, extensive testing of the 59 assay in the intended clinical matrix is required to evaluate assay parameters, such as: limit of 60 detection, sensitivity (probability of detection) and specificity (see glossary for definitions). The 61 sensitivity and specificity of the assay are experimentally determined using a set of target 62 (inclusivity) strains, near neighbor (exclusivity) strains, and matrix relevant (background) 63 organisms. Assay performance also needs to be measured in assay-specific matrices (i.e. blood, 64 stool, water, soil, etc.). Often, assays are computationally designed using a set of available 65 genomic/gene sequences at that time and then experimentally validated for signature presence in 66 all available samples of the target organism (the inclusivity panel) and validated for signature 67 50 51 2.0 Background and Rationale 52 Nucleic acid-based assays, such as real time polymerase chain reaction (PCR), are the

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