Integrative Analysis of GC-MS and LC-MS Data for Biomarker Discovery

GC-MS 和 LC-MS 数据综合分析以发现生物标志物

• 批准号: 10393981
• 负责人: Habtom W Ressom
• 金额: $ 1.25万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10393981
• 关键词: Academic Medical Centers; Administrative Supplement; Area; Biological; Biological Markers; Biomedical Research; Coupled; Data; Disease; Educational workshop; Environment; Fostering; Gas Chromatography; Goals; Journals; Learning; Liquid Chromatography; Mass Spectrum Analysis; Mentors; Methods; Pathway Analysis; Primary carcinoma of the liver cells; Research; Sampling; Students; System; base; biomarker discovery; career; data integration; experience; feature selection; insight; meetings; member; metabolome; metabolomics; parent grant; skills; undergraduate student

Integrative Analysis of GC-MS and LC-MS Data for Biomarker Discovery PROJECT SUMMARY Gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS) have been extensively used for high-throughput comparison of the levels of thousands of metabolites among biological samples. The use of the metabolomics data acquired by both platforms helps increase the metabolome coverage for biomarker discovery studies. In this administrative supplement application, we request support to mentor a highly inspired undergraduate student who would like to pursue a career in biomedical research. The candidate will investigate methods to integrate GC-MS and LC-MS data for application in biomarker discovery. The mentored research will involve: (1) preprocessing of previously acquired GC-MS and LC-MS data to detect and align peaks; (2) integration of the peaks detected by both platforms for feature selection based on the changes in the levels of the peak intensities between two biologically distinct groups; (3) annotation of analytes detected by both platforms using mass-based search and spectral matching; (4) pathway analysis of disease-associated metabolites identified by combining GC-MS and LC-MS data. The proposed research relates to the main goal of the parent grant, which aims to find and validate metabolite biomarkers for hepatocellular carcinoma (HCC) by using systems metabolomics. Integration of data from GC-MS and LC-MS helps increase the metabolome coverage, thereby enhancing our ability to find metabolite biomarker for HCC. The activities proposed in this administrative supplement will give the candidate learning experience in quantitative biomedical research. In addition to actively working on the proposed research, the candidate will attend weekly lab meetings, journal clubs, as well as departmental seminars and workshops at Georgetown University Medical Center. These activities will expand and foster the skills of the candidate in biomedical research. Furthermore, the activities proposed in this mentored research will create opportunities for the candidate to gain insight into various biomedical research areas by interacting with other lab members and students in a highly collaborative research environment.

生物标志物发现的GC-MS和LC-MS数据的综合分析 项目摘要 气相色谱与质谱法（GC-MS）和液相色谱耦合到质量 光谱法（LC-MS）已广泛用于高通量比较 生物样品中成千上万的代谢产物。两者获得的代谢组学数据的使用 平台有助于增加生物标志物发现研究的代谢覆盖范围。在此管理中 补充申请，我们要求支持一名受到启发的本科生的指导 喜欢从事生物医学研究的职业。候选人将研究整合GC-MS的方法 和用于应用于生物标志物发现的LC-MS数据。指导的研究将涉及：（1） 预处理先前获得的GC-MS和LC-MS数据以检测和对齐峰； （2）集成 两个平台都根据峰值的变化而检测到的特征选择峰 两个生物学上不同的群体之间的强度； （3）两个平台检测到的分析物的注释 使用基于质量的搜索和光谱匹配； （4）与疾病相关代谢物的途径分析 通过组合GC-MS和LC-MS数据来识别。拟议的研究与 旨在寻找和验证肝细胞癌（HCC）的代谢物生物标志物的父母赠款 通过使用系统代谢组学。来自GC-MS和LC-MS的数据集成有助于增加 代谢组的覆盖范围，从而增强了我们找到HCC代谢物生物标志物的能力。活动 在此行政补充中提出的建议将为候选人学习经验 生物医学研究。除了积极从事拟议的研究外，候选人还将参加 每周实验室会议，期刊俱乐部以及乔治敦的部门研讨会和研讨会 大学医学中心。这些活动将扩大和培养生物医学候选人的技能 研究。此外，这项指导研究中提出的活动将为 候选人通过与其他实验室成员互动以及 在高度协作的研究环境中的学生。