ABI Innovation: A Systematic Computational Approach to Creating Libraries of High-Quality Mass Spectra for Unidentified Metabolites

ABI Innovation：一种为未知代谢物创建高质量质谱库的系统计算方法

• 批准号: 1262416
• 负责人: Xiuxia Du
• 金额: $ 58.06万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262416&HistoricalAwards=false
• 关键词: ABI; Innovation; Systematic; Computational; Approach

An award is made to the University of North Carolina at Charlotte to develop a computational workflowthat will create libraries of high-quality mass spectra for unknown metabolites that are observedrepeatedly by mass spectrometers in metabolomics studies. Metabolomics is a rapidly developing field of?omics? research concerned with the high-throughput identification and quantitation of small moleculemetabolites in the metabolome. Since the metabolome constitutes a wide array of compound classes thatare crucial for the normal functioning of a biological system, the metabolomics approach promises to offernew insights in many areas of biological investigation. Recent metabolomics research benefited greatlyfrom advances in mass spectrometry and chromatography. These advances allow researchers to detectmany metabolites that could not be detected previously. However, a sizable fraction of these compoundsare unknown and a new computational infrastructure is required for processing the complex massspectral data and identifying and characterizing these metabolites. This project addresses this need bydeveloping a computational workflow that will create libraries of high-quality mass spectra for unknowncompounds from many samples. These resulting libraries will enable the identification of many currentlyunidentified, but commonly observed components by their spectra. Equally important, the workflow willallow more precise quantitation of metabolites and subsequent differential analysis of metabolic profiles.The most biologically interesting unknown compounds in the library can then be subjected to furtherattempts at structure elucidation.The project will contribute to the training of postdoctoral fellows and graduate students in bioinformaticsmethods. The PI will develop modules covering metabolomics bioinformatics methods for a graduatecourse. Materials developed for the class will also be made available online and presented at abioinformatics workshop hosted at UNC-Charlotte.

北卡罗来纳大学夏洛特分校（University of North Carolina at Charlotte）获得了一项奖励，以开发一种计算工作流程，该流程将为代谢组学研究中通过质谱仪反复观察的未知代谢物创建高质量质谱库。代谢组学是一个快速发展的组学领域。代谢组中小分子代谢物的高通量鉴定和定量研究。由于代谢组学构成了一系列对生物系统正常功能至关重要的化合物类别，因此代谢组学方法有望为许多生物研究领域提供新的见解。最近的代谢组学研究很大程度上得益于质谱法和色谱法的进步。这些进步使研究人员能够检测到许多以前无法检测到的代谢物。然而，这些化合物中有相当大一部分是未知的，需要新的计算基础设施来处理复杂的质谱数据并识别和表征这些代谢物。该项目通过开发一个计算工作流程来满足这一需求，该工作流程将为许多样品中的未知化合物创建高质量的质谱库。这些生成的文库将能够通过光谱识别许多目前尚未确定但通常观察到的成分。同样重要的是，该工作流程将允许更精确的代谢物定量和随后的代谢谱差异分析。然后，文库中生物学上最有趣的未知化合物可以进行进一步的结构解析。该项目将有助于培养生物信息学方法方面的博士后和研究生。PI将为研究生课程开发涵盖代谢组学生物信息学方法的模块。为课程开发的材料也将在网上提供，并在北卡罗来纳大学夏洛特分校主办的生物信息学研讨会上展示。