Background Targeted sequencing using unique molecular identifiers (UMIs) enables detection of rare variant alleles in challenging applications, such as cell-free DNA analysis from liquid biopsies. Standard bioinformatics pipelines for data processing and variant calling are not adapted for deep-sequencing data containing UMIs, are inflexible, and require multistep workflows or dedicated computing resources. Methods We developed a bioinformatics pipeline using Python and an R package for data analysis and visualization. To validate our pipeline, we analyzed cell-free DNA reference material with known mutant allele frequencies (0%, 0.125%, 0.25%, and 1%) and public data sets. Results We developed UMIErrorCorrect, a bioinformatics pipeline for analyzing sequencing data containing UMIs. UMIErrorCorrect only requires fastq files as inputs and performs alignment, UMI clustering, error correction, and variant calling. We also provide UMIAnalyzer, a graphical user interface, for data mining, visualization, variant interpretation, and report generation. UMIAnalyzer allows the user to adjust analysis parameters and study their effect on variant calling. We demonstrated the flexibility of UMIErrorCorrect by analyzing data from 4 different targeted sequencing protocols. We also show its ability to detect different mutant allele frequencies in standardized cell-free DNA reference material. UMIErrorCorrect outperformed existing pipelines for targeted UMI sequencing data in terms of variant detection sensitivity. Conclusions UMIErrorCorrect and UMIAnalyzer are comprehensive and customizable bioinformatics tools that can be applied to any type of library preparation protocol and enrichment chemistry using UMIs. Access to simple, generic, and open-source bioinformatics tools will facilitate the implementation of UMI-based sequencing approaches in basic research and clinical applications.

中文翻译：

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UMIErrorCorrect 和 UMIAnalyzer：使用唯一分子标识符生成一致性读数、纠错和可视化的软件

背景 使用独特分子标识符 (UMI) 的靶向测序能够在具有挑战性的应用中检测稀有变异等位基因，例如液体活检的无细胞 DNA 分析。用于数据处理和变异调用的标准生物信息学管道不适用于包含 UMI 的深度测序数据，不灵活，并且需要多步工作流程或专用计算资源。方法 我们使用 Python 和 R 包开发了一个生物信息学管道，用于数据分析和可视化。为了验证我们的管道，我们分析了具有已知突变等位基因频率（0%、0.125%、0.25% 和 1%）和公共数据集的无细胞 DNA 参考材料。结果 我们开发了 UMIErrorCorrect，这是一种用于分析包含 UMI 的测序数据的生物信息学管道。UMIErrorCorrect 只需要 fastq 文件作为输入并执行比对、UMI 聚类、纠错和变体调用。我们还提供 UMIAnalyzer，一个图形用户界面，用于数据挖掘、可视化、变异解释和报告生成。UMIAnalyzer 允许用户调整分析参数并研究它们对变异检出的影响。我们通过分析来自 4 种不同靶向测序协议的数据展示了 UMIErrorCorrect 的灵活性。我们还展示了它在标准化无细胞 DNA 参考材料中检测不同突变等位基因频率的能力。UMIErrorCorrect 在变异检测灵敏度方面优于针对目标 UMI 测序数据的现有管道。结论 UMIErrorCorrect 和 UMIAnalyzer 是全面且可定制的生物信息学工具，可应用于任何类型的文库制备方案和使用 UMI 的富集化学。使用简单、通用和开源的生物信息学工具将有助于在基础研究和临床应用中实施基于 UMI 的测序方法。