MET-COFEA:
A Novel Liquid Chromatography-Mass Spectrometry Data Processing Platform for Metabolite Compound Feature Extraction and Annotationn
 
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MET-COFEA: A Novel Liquid Chromatography-Mass Spectrometry Data Processing Platform for Metabolite Compound Feature Extraction and Annotation

Wenchao Zhang, Junil Chang, Zhentian Lei, David Huhman, Lloyd W. Sumner, and Patrick X. Zhao

Plant Biology Division, The Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA

Accurate and automated metabolite-associated chromatograph peak feature extraction, annotation, alignment, and quantification are critical yet challenging analysis steps in large-scale liquid chromatography/mass spectrometry (LC/MS)-based untargeted metabolomics and metabolite identification.

We developed a novel LC/MS data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and clusters chromatograph peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak’s observed m/z value with the corresponding metabolite compound’s molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak annotation algorithms. We have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different ESI modes, different instruments, and different experimental conditions, which results in more confident and comprehensive compound-associated peak information for downstream analyses. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. MET-COFEA provides the highest performance in detected chromatographic peaks and additional metabolite compound information that is not available from existing open-source software. MET-COFEA support batch and parallel mode to run large scale LC-MS data set.

Additionally, MET-COFEA, can be extended into LC-MS based comparative metabolomics study by combining with our another Tool entitiled as MET-XAlign.

Regarding GC-MS based metabolomics data, our new developed tool MET-COFEI is aiming to analize large scale GC-MS data, and can realize the pure compound spectrum extraction, identification and alignment.