MS-based metabolite identification

项目摘要 该提议旨在开发一种用于代谢组学的创新的代谢物识别算法，该算法使用液体或 气相色谱-质谱联用(LC/GC-MS) 数据分析：峰值检测和化合物识别。代谢组学有巨大的潜力影响临床 健康实践，因为它能够快速分析组织或生物液样本，几乎不需要准备样本，以及 代谢组学提供了补充患者基因组和蛋白质组特征的信息。然而， 峰检测和化合物鉴定仍然是代谢组学的重大挑战。低质量 信号阻碍了数据分析的每一步，包括但不限于峰值检测和化合物识别。 特别是，代谢物识别的准确性受到高误识率的影响，这可能会误导 下游分析，如网络建设和生物标志物发现。为了缓解这些问题，我们 建议开发一种基于LC/GC-MS的代谢组学新的代谢物鉴定算法 实现两个高度相关的目标：峰值检测和化合物识别 并使用MS相似度和保留时间。拟议的统计/计算 这些方法将在分析LC/GC-MS数据时为化合物鉴定提供新的方法。这个 根据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.