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.

GC-MS和LC-MS数据在生物标志物发现中的整合分析