USDA-APHIS-VS-DB National Animal Health Laboratory Network, Ames, IA.
USDA-APHIS-VS-DB National Veterinary Services Laboratories, Ames, IA.
J Vet Diagn Invest. 2021 Mar;33(2):248-252. doi: 10.1177/1040638720937015. Epub 2020 Jul 1.
With the cost of next-generation sequencing (NGS) decreasing, this technology is rapidly being integrated into the workflows of veterinary clinical and diagnostic laboratories nationwide. The mission of the U.S. Department of Agriculture-National Animal Health Laboratory Network (NAHLN) is in part to evaluate new technologies and develop standardized processes for deploying these technologies to network laboratories for improving detection and response to emerging and foreign animal diseases. Thus, in 2018, the NAHLN identified the integration of NGS into the network as a top priority. In order to assess the current state of preparedness across NAHLN laboratories and to identify which have the capability for performing NGS, a questionnaire was developed by the NAHLN Methods Technical Working Group and submitted to all NAHLN laboratories in December 2018. Thirty of 59 laboratories completed the questionnaire, of which 18 (60%) reported having some sequencing capability. Multiple sequencing platforms and reagents were identified, and limited standardized quality control parameters were reported. Our results confirm that NGS capacity is available within the NAHLN, but several gaps remain. Gaps include not having sufficient personnel trained in bioinformatics and data interpretation, lack of standardized methods and equipment, and maintenance of sufficient computing capacity to meet the growing demand for this technology.
随着下一代测序(NGS)成本的降低,这项技术正在迅速融入全美兽医临床和诊断实验室的工作流程中。美国农业部-国家动物健康实验室网络(NAHLN)的使命之一是评估新技术,并开发将这些技术部署到网络实验室的标准化流程,以提高对新发和外来动物疾病的检测和应对能力。因此,2018 年,NAHLN 将 NGS 纳入网络作为优先事项。为了评估 NAHLN 实验室的当前准备情况,并确定哪些实验室有能力进行 NGS,NAHLN 方法技术工作组开发了一份问卷,并于 2018 年 12 月提交给所有 NAHLN 实验室。59 个实验室中有 30 个完成了问卷,其中 18 个(60%)报告说具有一定的测序能力。确定了多种测序平台和试剂,并报告了有限的标准化质量控制参数。我们的结果证实,NGS 能力在 NAHLN 中是可用的,但仍存在一些差距。这些差距包括没有足够的人员接受生物信息学和数据分析方面的培训、缺乏标准化的方法和设备,以及维护足够的计算能力以满足对这项技术不断增长的需求。