Department of Surgery, Emory University, United States.
Department of Pathology, Emory University, United States.
Hum Immunol. 2022 Mar;83(3):248-255. doi: 10.1016/j.humimm.2022.01.007. Epub 2022 Jan 31.
Eplet mismatch load, both overall and at the single molecule level, correlates with transplant recipient outcomes. However, precise eplet assessment requires high-resolution HLA typing of both the donor and recipient. Anything less than high-resolution typing requires imputation of HLA types. The currently available methods to identify eplet mismatch are both tedious and demanding. Therefore, we developed a software package and user-friendly web application (hlaR), that simplifies the workflow of eplet analysis, provides functions to impute high-resolution from low-resolution data and calculates both overall and single molecule eplet mismatch for single or multiple donor recipient pairs. Compared to manual assessments using currently available tools (namely, HLAMatchMaker), hlaR resulted in only minimal discrepancy in eplet mismatches (mean absolute difference of 0.56 for class I and 0.86 for class II for unique sum across loci). Additionally, output of the single molecule eplet function compared well to manual calculation, with an average single antigen count increase of 0.19. Importantly, the hlaR tool permits rapid and reproducible imputation and eplet mismatch including comparison between eplet reference tables (e.g. HLAMatchMaker version 2 or 3). Users can import data from a spreadsheet rather than relying on keystroke entry of individual donor and recipient data, thus reducing the risk of data entry errors. The resulting improved scalability of the hlaR tool is highlighted by plotting analysis time against the size of the input dataset. The new hlaR tool can provide eplet mismatch data with a streamlined workflow. With decreased effort from the end user, eplet matching and mismatch load data can be further incorporated into both research and clinical use.
错配负荷，无论是整体水平还是单分子水平，都与移植受者的结局相关。然而，精确的错配评估需要对供体和受者进行高分辨率 HLA 分型。任何低于高分辨率的分型都需要 HLA 类型的推断。目前用于识别错配的方法既繁琐又费力。因此，我们开发了一个软件包和用户友好的网络应用程序（hlaR），简化了错配分析的工作流程，提供了从低分辨率数据推断高分辨率的功能，并计算了单个或多个供受者对的整体和单分子错配。与使用当前可用工具（即 HLAMatchMaker）进行手动评估相比，hlaR 在错配方面的差异仅最小（在独特的基因座总和方面，I 类的平均绝对差异为 0.56，II 类为 0.86）。此外，单分子错配功能的输出与手动计算结果相当，平均单个抗原计数增加了 0.19。重要的是，hlaR 工具允许快速和可重复的推断和错配，包括在错配参考表之间进行比较（例如，HLAMatchMaker 版本 2 或 3）。用户可以从电子表格导入数据，而不必依赖单个供者和受者数据的键输入，从而降低了数据输入错误的风险。hlaR 工具的改进可扩展性通过绘制分析时间与输入数据集大小的关系来突出显示。新的 hlaR 工具可以提供简化的工作流程的错配数据。通过减少最终用户的工作量，可以进一步将错配匹配和错配负荷数据纳入研究和临床应用中。
