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

GC-MS和LC-MS数据综合分析用于生物标志物发现 项目摘要 气相色谱-质谱联用（GC-MS）和液相色谱-质谱联用 质谱法（LC-MS）已广泛用于高通量比较的水平， 成千上万的代谢物。使用代谢组学数据， 平台有助于增加生物标志物发现研究的代谢组覆盖率。本行政 补充申请，我们要求支持指导一个高度鼓舞人心的本科生谁会 喜欢从事生物医学研究。候选人将研究整合GC-MS的方法 和LC-MS数据用于生物标志物发现。指导研究将涉及：（1） 预处理先前采集的GC-MS和LC-MS数据以检测和对齐峰;（2）积分 两个平台检测到的峰值，用于基于峰值水平的变化进行特征选择 两个生物学上不同的组之间的强度;（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）疾病相关代谢物的途径分析 通过结合GC-MS和LC-MS数据鉴定。拟议的研究涉及的主要目标， 父母补助金，旨在寻找和验证肝细胞癌（HCC）的代谢物生物标志物 通过使用系统代谢组学。GC-MS和LC-MS数据的整合有助于提高 代谢物组覆盖率，从而增强我们发现HCC代谢物生物标志物的能力。的活动 在此行政补充建议将给予候选人的学习经验定量 生物医学研究除了积极从事拟议的研究，候选人将参加 每周的实验室会议，期刊俱乐部，以及部门研讨会和乔治敦讲习班 大学医学中心。这些活动将扩大和培养候选人在生物医学领域的技能 research.此外，这项指导研究中提出的活动将为以下方面创造机会： 候选人通过与其他实验室成员的互动， 学生在一个高度合作的研究环境。

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.