Patel Hamel, Lee Sang-Hyuck, Breen Gerome, Menzel Stephen, Ojewunmi Oyesola, Dobson Richard J B
Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, UK.
NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, UK.
Curr Protoc. 2022 Apr;2(4):e373. doi: 10.1002/cpz1.373.
The Illumina genotyping microarrays generate data in image format, which is processed by the platform-specific software GenomeStudio, followed by an array of complex bioinformatics analyses that rely on various software, different programming languages, and numerous dependencies to be installed and configured correctly. The entire process can be time-consuming, can lead to reproducibility errors, and can be a daunting task for bioinformaticians. To address this, we introduce the COPILOT protocol, which has been successfully used to transform raw Illumina genotype intensity data into high-quality analysis-ready data on tens of thousands of human patient samples that have been genotyped on a variety of Illumina genotyping arrays. This includes processing both mainstream and custom content genotyping chips with over 4 million markers per sample. The COPILOT QC protocol consists of two distinct tandem procedures to process raw Illumina genotyping data. The first protocol is an up-to-date process to systematically QC raw Illumina microarray genotyping data using the Illumina-specific GenomeStudio software. The second protocol takes the output from the first protocol and further processes the data through the COPILOT (Containerised wOrkflow for Processing ILlumina genOtyping daTa) containerized QC pipeline, to automate an array of complex bioinformatics analyses to improve data quality through a secondary clustering algorithm and to automatically identify typical Genome-Wide Association Study (GWAS) data issues, including gender discrepancies, heterozygosity outliers, related individuals, and population outliers, through ancestry estimation. The data is returned to the user in analysis-ready PLINK binary format and is accompanied by a comprehensive and interactive HTML summary report file which quickly helps the user understand the data and guides the user for further data analyses. The COPILOT protocol and containerized pipeline are also available at https://khp-informatics.github.io/COPILOT/index.html. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Processing raw Illumina genotyping data using GenomeStudio Basic Protocol 2: COPILOT: A containerised workflow for processing Illumina genotyping data.
Illumina基因分型微阵列以图像格式生成数据，该数据由特定平台的软件GenomeStudio进行处理，随后是一系列复杂的生物信息学分析，这些分析依赖于各种软件、不同的编程语言以及众多需要正确安装和配置的依赖项。整个过程可能很耗时，可能导致可重复性错误，并且对生物信息学家来说可能是一项艰巨的任务。为了解决这个问题，我们引入了COPILOT协议，该协议已成功用于将原始Illumina基因型强度数据转换为高质量的、可供分析的数据，这些数据来自数万名在各种Illumina基因分型阵列上进行基因分型的人类患者样本。这包括处理每个样本有超过400万个标记的主流和定制内容基因分型芯片。COPILOT质量控制协议由两个不同的串联程序组成，用于处理原始Illumina基因分型数据。第一个协议是一个最新的过程，使用Illumina特定的GenomeStudio软件系统地对原始Illumina微阵列基因分型数据进行质量控制。第二个协议获取第一个协议的输出，并通过COPILOT（用于处理Illumina基因分型数据的容器化工作流程）容器化质量控制管道进一步处理数据，以自动化一系列复杂的生物信息学分析，通过二次聚类算法提高数据质量，并通过祖先估计自动识别典型的全基因组关联研究（GWAS）数据问题，包括性别差异、杂合性异常值、相关个体和群体异常值。数据以可供分析的PLINK二进制格式返回给用户，并附带一个全面且交互式的HTML摘要报告文件，该文件可快速帮助用户理解数据并指导用户进行进一步的数据分析。COPILOT协议和容器化管道也可在https://khp-informatics.github.io/COPILOT/index.html上获取。© 2022作者。由Wiley Periodicals LLC出版的《当前协议》。基本协议1：使用GenomeStudio处理原始Illumina基因分型数据 基本协议2：COPILOT：用于处理Illumina基因分型数据的容器化工作流程。
