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bioRxiv · 10.1101/2022.08.31.506116

openNAU: An open-source platform for normalizing, analyzing, and visualizing untargeted metabolomics data

Sun, Q.·Xu, Q.·Wang, M.·Wang, Y.·Zhang, D.·Lai, M.
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Abstract

MotivationAs an important part of metabolomics analysis, untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also brings great challenges to data analysis from the extraction of raw data to the identification of differential metabolites. To date, a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research. The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data. Our goal is to establish an easily operated platform and obtain a repeatable analysis result. ResultsWe used the R language basic environment to construct the preprocessing system of the original data and the LAMP (Linux + Apache + MySQL + PHP) architecture to build a cloud mass spectrum data analysis system. An open-source analysis software for untargeted metabolomics data (openNAU) was constructed. It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks. A reference metabolomics database based on public databases was also constructed. Finally, a complete analysis system platform for untargeted metabolomics was established. This platform provides a complete template interface for the addition and updating of the analysis process, so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions. Availability and ImplementationThe source code can be downloaded from https://github.com/zjuRong/openNAU. Contactsunqingrong@zju.edu.cn

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Sun, Q., Xu, Q., Wang, M., Wang, Y., Zhang, D., Lai, M.. 2022-09-03. openNAU: An open-source platform for normalizing, analyzing, and visualizing untargeted metabolomics data. https://doi.org/10.1101/2022.08.31.506116

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