Espindola Andres S
Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK 74078, USA.
Biology (Basel). 2024 Sep 6;13(9):700. doi: 10.3390/biology13090700.
The validation of diagnostic assays in plant pathogen detection is a critical area of research. It requires the use of both negative and positive controls containing a known quantity of the target pathogen, which are crucial elements when calculating analytical sensitivity and specificity, among other diagnostic performance metrics. High Throughput Sequencing (HTS) is a method that allows the simultaneous detection of a theoretically unlimited number of plant pathogens. However, accurately identifying the pathogen from HTS data is directly related to the bioinformatic pipeline utilized and its effectiveness at correctly assigning reads to their associated taxa. To this day, there is no consensus about the pipeline that should be used to detect the pathogens in HTS data, and results often undergo review and scientific evaluation. It is, therefore, imperative to establish HTS resources tailored for evaluating the performance of bioinformatic pipelines utilized in plant pathogen detection. Standardized artificial HTS datasets can be used as a benchmark by allowing users to test their pipelines for various pathogen infection scenarios, some of the most prevalent being multiple infections, low titer pathogens, mutations, and new strains, among others. Having these artificial HTS datasets in the hands of HTS diagnostic assay validators can help resolve challenges encountered when implementing bioinformatics pipelines for routine pathogen detection. Offering these purely artificial HTS datasets as benchmarking tools will significantly advance research on plant pathogen detection using HTS and enable a more robust and standardized evaluation of the bioinformatic methods, thereby enhancing the field of plant pathogen detection.
植物病原体检测中诊断分析方法的验证是一个关键的研究领域。这需要使用含有已知数量目标病原体的阴性和阳性对照，在计算分析灵敏度和特异性以及其他诊断性能指标时，这些是关键要素。高通量测序（HTS）是一种能够同时检测理论上数量不受限的植物病原体的方法。然而，从HTS数据中准确识别病原体直接关系到所使用的生物信息学流程及其将读数正确分配到相关分类群的有效性。时至今日，对于用于检测HTS数据中病原体的流程尚无共识，其结果往往需要经过审查和科学评估。因此，必须建立专门用于评估植物病原体检测中所使用生物信息学流程性能的HTS资源。标准化的人工HTS数据集可作为基准，通过允许用户针对各种病原体感染情况测试其流程，其中一些最常见的情况包括多重感染、低滴度病原体、突变和新菌株等。让HTS诊断分析验证人员掌握这些人工HTS数据集有助于解决在实施用于常规病原体检测的生物信息学流程时遇到的挑战。提供这些纯人工HTS数据集作为基准工具将显著推进利用HTS进行植物病原体检测的研究，并能对生物信息学方法进行更稳健和标准化的评估，从而推动植物病原体检测领域的发展。
