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experimental testing to those isolates that are most likely to demonstrate assay failure (due to 169 false positives or false negatives). Computational prioritization of testing has the potential to 170 streamline efficiency by providing robust characterization while minimizing the time, cost and 171 sample resources consumed. While this document is not intended to promote specific 172 applications or software, it is intended to describe recommendations and guidelines for modern 173 in silico assay design and evaluation. 174 175 3.0 Assay Development Process: Traditional (Low throughput) vs Modern (High 176 throughput) 177 Traditional and modern assay development processes are illustrated in Figure 4. Apart from 178 the initial assay design step, the traditional approach is centered on laboratory wet lab testing. 179 The key objectives of the modern process are extensive use of in silico analyses of whole 180 genome sequences to 1) guide and minimize the number of experimental iterations, 2) minimize 181 the inclusivity, exclusivity and environmental panel wet lab testing, 3) address limitations on 182 obtaining reference materials, and ultimately 4) cut down cost and time while improving assay 183 performance. Essentially, the modern approach is data-driven and requires (a) establishing well- 184 curated sequence databases, and (b) using state-of-the-art assay design algorithms to evaluate 185 assay designs and rank assays prior to wet lab testing (detailed below). This approach reduces the 186 number of experimental iterations compared to the traditional approach. The following sections 187 compare and contrast the various steps of the two approaches.

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