OpenNAU: An Open-source Platform for Normalizing, Analyzing, and Visualizing Cancer Untargeted Metabolomics Data

Overview

Journal Chin J Cancer Res

Specialty Oncology

Date 2023 Nov 16

PMID 37969962

Authors

Qingrong Sun

Qingqing Xu

Majie Wang

Yongcheng Wang

Dandan Zhang

Maode Lai

Affiliations

Soon will be listed here.

Abstract

Objective: As 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 poses 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.

Methods: We 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.

Results: 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.

Conclusions: 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. The source code can be downloaded from https://github.com/zjuRong/openNAU.

Citing Articles

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Cui X, Anatolevna S, Wang Y Metabolites. 2024; 14(8).

PMID: 39195529 PMC: 11356577. DOI: 10.3390/metabo14080433.

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