Abstract
Metabolomics data analysis depends on the utilization of bioinformatics tools. To meet the evolving needs of metabolomics research, several integrated platforms have been developed. Our group has developed a desktop platform IP4M (integrated Platform for Metabolomics Data Analysis) which allows users to perform a nearly complete metabolomics data analysis in one-stop. With the extensive usage of IP4M, more and more demands were raised from users worldwide for a web version and a more customized workflow. Thus, iMAP (integrated Metabolomics Analysis Platform) was developed with extended functions, improved performances, and redesigned structures. Compared with existing platforms, iMAP has more methods and usage modes. A new module was developed with an automatic pipeline for train-test set separation, feature selection, and predictive model construction and validation. A new module was incorporated with sufficient editable parameters for network construction, visualization, and analysis. Moreover, plenty of plotting tools have been upgraded for highly customized publication-ready figures. Overall, iMAP is a good alternative tool with complementary functions to existing metabolomics data analysis platforms. iMAP is freely available for academic usage at https://imap.metaboprofile.cloud/ (License MPL 2.0).
Highlights
Metabolomics data analysis depends on specialized bioinformatics tools
We have developed IP4M (Liang et al, 2020) for metabolomics data mining which covers all the key steps from pre-processing to data-interpretation and can be freely accessed via https://github.com/IP4M
Workflow with the predictive model building was performed on 234 clinical samples (Zhu et al, 2014) from three groups (66 colorectal cancer patients, 76 polyp patients, and 92 healthy controls)
Summary
Metabolomics data analysis depends on specialized bioinformatics tools. After raw data preprocessing (peak finding, matching, and quantifying), metabolomic data analysis can be summarized as three steps. The predefined workflows are simpler with complete (or nearly complete) functions and recommended parameters. It is more preferred for batch analysis or. Given the new trends of metabolomics data analysis and to meet the evolving needs of IP4M users, we improved the modules and workflows and developed a webserver named integrated Metabolomics Analysis Platform (iMAP). Modules belong to steps 1–3 can be incorporated into a thread-like or tree-like workflow freely by users according to their study aims and data characteristics. The user-defined workflows and relevant parameters can be saved for later analysis This mode is suitable for batch analysis or studies with special requirements. We present the functions and applications of iMAP with the focuses on newly-added modules and workflows
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