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    Resources: MET-COFEA + MET-XAlign: Tools that Enable LC/MS based Comparative Metabolomics
    Submitted by WENCHAO ZHANG; posted on Thursday, October 08, 2020

    Comparative metabolomics is aiming to find the biological meaningful common metabolites (biomarkers) from large-scale, high throughout metabolomics profiling data. Thus, approaches and tools towards detecting and aligning the potential same known and unknown metabolites across different samples, different biological experiments and even different instrument runs are desired.

    Inspired by the aims in LC/MS based comparative metabolomics, a series of approaches are developed and two tools entitled as MET-COFEA (bioinfo.noble.org/manu[...]ofea/) and MET-XAlign (bioinfo.noble.org/manu[...]lign/) are implemented.

    MET-COFEA, being an analysis tool, can be used to extract and annotate each meaningful metabolite' associated chromatograph features from each LC-MS sample. For the extracted metabolite compound group with multiple fragment peaks, the neutral molecular mass can be deduced and the compound's representative retention time can be estimated, which can be considered as the common thing for the same metabolite across different samples and different experiment configurations, although the fragmentation pattern can vary from sample to sample, from experiment to experiment. MET-COFEA has already been successfully implemented as a pipeline tool with visualization.

    MET-XAlign, being as an alignment tool, has been dedicatedly developed based on the analysis results from MET-COFEA. It mainly includes an algorithm core and user interface. The identified compounds from MET-COFEA are characterized by compound retention time and neural molecular mass deduced by multiple associated fragments' m/z value, which are represented as its unique Compound_ID.

    Adopting this approach that combined MET-COFEA and MET-XAlign, the LC-MS based comparative metabolomics analysis including metabolite feature extraction and annotation for each sample and alignment across samples can be separated. Our previous experimental results show that the strategy combing MET-COFEA and MET-XAlign can efficiently realize the aim to find the biological meaningful biomarkers from LC/MS based comparative metabolomics data.

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