MS-based metabolite identification

项目摘要 该提案旨在开发一种创新的代谢物鉴定算法，用于代谢组学， 气相色谱-质谱联用（LC/GC-MS），通过解决两个重要组成部分， 数据分析：峰检测和化合物鉴定。代谢组学有很大的潜力影响临床 由于其能够快速分析组织或生物流体样品，且样品制备很少， 代谢组学提供补充患者的基因组和蛋白质组谱的信息。然而，在这方面， 峰检测和化合物鉴定仍然是代谢组学的重要挑战。低质量 信号阻碍了数据分析的每一步，包括但不限于峰检测和化合物识别。 特别地，代谢物鉴定准确性受到高错误鉴定率的影响，该错误鉴定率可能误导鉴定者。 下游分析，如网络构建和生物标志物发现。为了解决这些问题，我们 建议为基于代谢组学的LC/GC-MS开发创新的代谢物鉴定算法， 实现两个高度相互关联的目标：峰值检测和化合物识别， 增强的信号，并使用MS相似性和保留时间。统计/计算 这些方法将导致在分析LC/GC-MS数据中用于化合物鉴定的新方法。的 从该项目开发的代谢识别算法将通过以下方式实现准确的代谢物识别： 同时考虑MS相似性和保留时间。

Project Summary This proposal aims to develop an innovative metabolite identification algorithm for metabolomics using liquid or gas chromatography coupled with mass spectrometry (LC/GC-MS) by addressing two important components of data analysis: peak detection and compound identification. Metabolomics has great potential to impact clinical health practices due to its ability to rapidly analyze tissue or biofluid samples with little sample preparation, and metabolomics provides information that complements the genomic and proteomic profile of a patient. However, peak detection and compound identification remain as significant challenges for metabolomics. Low quality signal hampers every step of data analyses including, but not limited, peak detection and compound identification. In particular, metabolite identification accuracy suffers from a high rate of false identification that can mislead the downstream analysis such as network construction and biomarker discovery. To alleviate these issues, we propose to develop an innovative metabolite identification algorithm for LC/GC-MS based metabolomics, by accomplishing two highly interconnected goals: peak detection and compound identification by generating augmented signals and using both MS similarity and retention times. The proposed statistical/computational approaches will lead to novel methodology for compound identification in analyzing LC/GC-MS data. The metabolic identification algorithms developed from this project will enable accurate metabolite identification by simultaneously considering MS similarity and retention time.