SPADA Draft Documents

Moreover, publicly available sequence database (e.g., GenBank) typically only contain a 79 small fraction of naturally occurring sequence diversity. As a result, detection assays are 80 vulnerable to “overfitting”: correctly differentiating known (i.e. sequenced) targets and non- 81 targets but failing to detect novel target variants or falsely detecting novel non-targets. 82 Knowledge of the true genetic diversity is limited for some biodefense agents and their near 83 neighbors, as often only several geographical and temporal representatives are fully 84 characterized while other geographic locations have been ignored or significantly under-sampled 85 and hence under-represented. In addition, while some agents such as the bacterium Bacillus 86 anthracis , are monomorphic (i.e. highly conserved), other agents, especially RNA viruses, are 87 very diverse (e.g., Lymphocytic choriomeningitis virus (LCMV), Lassa virus, and Crimean- 88 Congo hemorrhagic fever virus (CCHFV)). In general, detection assays targeting highly 89 conserved targets tend to fail due to unsequenced near-neighbor cross-reactivity, while assays 90 targeting diverse targets tend to fail due to false negatives against unsequenced target variants. 91 While the recent revolution in Next Generation sequencing technologies combined with 92 decreasing sequencing costs has increased knowledge of population genomic structure, the 93 capability for laboratory-based evaluation of newly sequenced strains has not kept pace. In this 94 scenario, replacing or redesigning old assays to incorporate new knowledge of the target 95 genomic landscape is critical. However, wet lab testing may not be feasible due to limitations on 96 the timely availability of samples/strains. This problem is further exacerbated by policy 97 decisions, such as the 2015 DoD moratorium that decreased access to live/inactivated biodefense 98 pathogens for various applications including assay development and validation (1). 99 100

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