MS-based metabolite identification

项目总结

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.