Department of Pathology (SoRelle, Wachsmann), University of Texas Southwestern Medical Center, Dallas.
Bioinformatics Core Facility (Cantarel), University of Texas Southwestern Medical Center, Dallas.
Arch Pathol Lab Med. 2020 Sep 1;144(9):1118-1130. doi: 10.5858/arpa.2019-0476-RA.
CONTEXT.—: Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.
OBJECTIVE.—: To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.
DATA SOURCES.—: This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.
CONCLUSIONS.—: This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.
临床下一代测序（NGS）正在被迅速采用，但对大型数据集的分析和解释为临床实验室环境带来了新的挑战。临床 NGS 结果严重依赖于生物信息学管道来识别复杂样本中的遗传变异。生物信息学算法、基因组组装和遗传注释数据库的选择对于确定与疾病相关的遗传改变非常重要。分析方法通常针对检测方法进行调整，以最大限度地提高准确性。一旦开发了一个管道，就必须对其进行验证，以确定与实际病例相似的样本的准确性和可重复性。基于计算机的能力验证或机构数据交换将确保临床实验室之间的一致性。
为分子病理学家提供生物信息学分析和验证设计的分步指南，以便在实施生物信息学管道作为新的临床 NGS 检测一部分时，了解监管和验证标准。
本指南使用了关于基因组分析、生物信息学方法和方法比较研究的已发表研究，使读者了解可用于检测和解释遗传变异的资源，包括开源软件工具和数据库。
这篇综述涵盖了 4 个关键概念：（1）用于检测遗传变异的生物信息学分析设计，（2）用于评估遗传影响的资源，（3）分析验证评估实验和数据集，包括一组多样化的样本，以模拟评估准确性和重现性的实际挑战，以及（4）如果设计用于测试生物信息学管道的能力验证能够提高临床实验室之间的一致性。
