Inter-lab comparison of unknowns in polar metabolomics data

非目标语言中未知特征识别和标注的障碍 代谢组学数据是分析方法和所用仪器之间的不一致 由不同的实验室提供。为了应对这一挑战，我们将与美国国立卫生研究院的其他实验室合作 代谢组学联盟将使用Consensus Polal生成实验室间数据集 代谢组学方法和常见样品。这些数据将有助于调整和 识别当前和未来非靶向代谢组学数据中的未知特征。目标1：A 工作组(以现有的ULWG和ISWG为基础，但也欢迎增加 与会者)将开会讨论评估未知的实验室间重叠的战略 非靶向代谢组学数据中的极性代谢物。我们将讨论并商定共同的 样品、内标(取自国际工作小组的工作)、层析、质谱分析 参数和数据处理方法。目标2：我们将分发决定的样本集 在目标1中，每个参与实验室将根据定义的 方法：研究方法。为便于比较，还将要求实验室进行第二轮 使用自己的标准操作程序对相同样本进行数据采集 非靶向极地代谢组学。目标3：来自参与实验室的数据将通过 通用特征检测和比对流水线，用于比对已知和未知特征。数据 将被捐献给一个目前正在开发的未知化合物数据库 未知的脂质组学数据。

An impediment to identification and annotation of the unknown features in untargeted metabolomics data is the inconsistency between analytical methods and instrumentation used by different labs. To address this challenge, we will work with other labs in the NIH metabolomics consortium to generate an inter-laboratory dataset using consensus polar metabolomics methods and common samples. These data will facilitate alignment and identification of unknown features in current and future untargeted metabolomics data. Aim 1: A working group (based on the existing ULWG and ISWG, but also welcoming additional participants) will convene to discuss strategies to assess inter-laboratory overlap of unknown polar metabolites in untargeted metabolomics data. We will discuss and agree upon common samples, internal standards (to be derived from of ISWG efforts), chromatography, mass spec parameters, and data processing methods. Aim 2: We will distribute a sample set decided upon in Aim 1 and each participating laboratory will perform sample analysis according to the defined methods. For comparative purposes, labs will also be requested to perform a second round of data acquisition on the same samples using their own standard operating procedures for untargeted polar metabolomics. Aim 3: Data from participating labs will be processed through a common feature detection and alignment pipeline to align known and unknown features. Data will be contributed to an unknown compound database currently under development to house the unknown lipidomics data.