IP4M: an integrated platform for mass spectrometry-based metabolomics data mining
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Open Access
SOFTWARE
IP4M: an integrated platform for mass spectrometry‑based metabolomics data mining Dandan Liang1, Quan Liu2, Kejun Zhou2, Wei Jia1*, Guoxiang Xie2* and Tianlu Chen1*
*Correspondence: [email protected]; xieguoxiang@hmibiotech. com; [email protected] 1 Shanghai Key Laboratory of Diabetes Mellitus and Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China 2 Human Metabolomics Institute, Inc., Shenzhen 518109, Guangdong, China
Abstract Background: Metabolomics data analyses rely on the use of bioinformatics tools. Many integrated multi-functional tools have been developed for untargeted metabolomics data processing and have been widely used. More alternative platforms are expected for both basic and advanced users. Results: Integrated mass spectrometry-based untargeted metabolomics data mining (IP4M) software was designed and developed. The IP4M, has 62 functions categorized into 8 modules, covering all the steps of metabolomics data mining, including raw data preprocessing (alignment, peak de-convolution, peak picking, and isotope filtering), peak annotation, peak table preprocessing, basic statistical description, classification and biomarker detection, correlation analysis, cluster and sub-cluster analysis, regression analysis, ROC analysis, pathway and enrichment analysis, and sample size and power analysis. Additionally, a KEGG-derived metabolic reaction database was embedded and a series of ratio variables (product/substrate) can be generated with enlarged information on enzyme activity. A new method, GRaMM, for correlation analysis between metabolome and microbiome data was also provided. IP4M provides both a number of parameters for customized and refined analysis (for expert users), as well as 4 simplified workflows with few key parameters (for beginners who are unfamiliar with computational metabolomics). The performance of IP4M was evaluated and compared with existing computational platforms using 2 data sets derived from standards mixture and 2 data sets derived from serum samples, from GC–MS and LC–MS respectively. Conclusion: IP4M is powerful, modularized, customizable and easy-to-use. It is a good choice for metabolomics data processing and analysis. Free versions for Windows, MAC OS, and Linux systems are provided. Keywords: Metabolomics, Data analysis, Workflow, Software
Background Gas and liquid chromatography coupled with mass spectrometry (GC/LC-MS), among others, are the main technical approaches for metabolomics studies, as they are able to detect and quantify a large variety of metabolite molecules from cells, tissues and biological fluids [1]. However, it is challenging to get accurate and reproducible data processing results due to the complexity of mass spectra (MS) data. To extract information and knowledge from metabolomics data, several non-commercial © The Author(s) 2020. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use
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