LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Author:

Yang Qingxia12,Li Bo3ORCID,Wang Panpan4,Xie Jicheng1,Feng Yuhao1,Liu Ziqiang1,Zhu Feng2ORCID

Affiliation:

1. Nanjing University of Posts and Telecommunications Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, , Nanjing, 210023 , China

2. Zhejiang University College of Pharmaceutical Sciences, , Hangzhou, Zhejiang 310058 , China

3. Chongqing Normal University, Chongqing College of Life Sciences, , Chongqing 401331 , China

4. Huanghuai University College of Chemistry and Pharmaceutical Engineering, , Zhumadian 463000 , China

Abstract

Abstract Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Funder

NUPT

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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