Inter-lab comparison of unknowns in polar metabolomics data

极性代谢组学数据中未知数的实验室间比较

• 批准号: 10397327
• 负责人: CHARLES ROBERT EVANS
• 金额: $ 39.17万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10397327
• 关键词: Address; Archives; Biological; California; Chemical Structure; Chromatography; Classification; Colorado; Computer software; Consensus; Data; Data Analyses; Data Set; Databases; Development; Funding; Future; Hand; Health; Ions; Laboratories; Light; Lipids; Liquid substance; Manuscripts; Methods; Michigan; Pacific Northwest; Participant; Pathway interactions; Preparation; Procedures; Process; Publications; Reproducibility; Research; Research Personnel; Sampling; Software Tools; Source; Time; Tissues; United States National Institutes of Health; Washington; Work; analytical method; base; comparative; computerized data processing; data acquisition; data harmonization; feature detection; human disease; improved; insight; instrumentation; interoperability; lipidomics; metabolomics; small molecule; working group

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.

在非靶向中识别和注释未知特征的障碍 代谢组学数据是分析方法和所用仪器之间的不一致 不同的实验室。为了应对这一挑战，我们将与NIH的其他实验室合作， 代谢组学联盟使用共识极性生成实验室间数据集 代谢组学方法和常见样品。这些数据将促进对齐， 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（基于现有的ULWG和ISWG，但也欢迎其他 与会者）将开会讨论评估实验室间未知重叠的策略 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 样品、内标物（来自ISWG的努力）、色谱法、质谱法 参数和数据处理方法。目标2：我们将分发一个样本集， 各参与实验室将根据目标1中定义的 方法.出于比较目的，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品进行数据采集， 非靶向极性代谢组学目标3：来自参与实验室的数据将通过 公共特征检测和对齐流水线以对齐已知和未知特征。数据 将贡献给目前正在开发的未知化合物数据库， 未知的脂质组学数据

项目成果

Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.

• DOI: 10.1021/acs.analchem.1c02149
• 发表时间: 2021-12-07
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Anderson, Brady G.;Raskind, Alexander;Habra, Hani;Kennedy, Robert T.;Evans, Charles R.
• 通讯作者: Evans, Charles R.

Implementing the MSFragger Search Engine as a Node in Proteome Discoverer.

将 MSFragger 搜索引擎实现为 Proteome Discoverer 中的节点。

• DOI: 10.1021/acs.jproteome.2c00485
• 发表时间: 2023
• 期刊: Journal of proteome research
• 影响因子: 4.4
• 作者: Chang,Hui-Yin;Haynes,SarahE;Yu,Fengchao;Nesvizhskii,AlexeyI
• 通讯作者: Nesvizhskii,AlexeyI

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.

基于质谱的代谢组学和药物代谢组学中的化合物鉴定策略。

• DOI: 10.1007/164_2022_617
• 发表时间: 2023
• 期刊: Handbook of experimental pharmacology
• 作者: Hissong,Rylan;Evans,KendraR;Evans,CharlesR
• 通讯作者: Evans,CharlesR