MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery

Xiaotao Shen; Zheng-Jiang Zhu

doi:10.1093/bioinformatics/bty1066

MetFlow: an interactive and integrated workflow for metabolomics data cleaning and differential metabolite discovery

Bioinformatics. 2019 Aug 15;35(16):2870-2872. doi: 10.1093/bioinformatics/bty1066.

Authors

Xiaotao Shen^{1

2}, Zheng-Jiang Zhu¹

Affiliations

¹ Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
² Univeristy of Chinese Academy of Sciences, Beijing, China.

PMID: 30601938
DOI: 10.1093/bioinformatics/bty1066

Abstract

Summary: Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.

Availability and implementation: The MetFlow is freely available on http://metflow.zhulab.cn/.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Mass Spectrometry
Metabolomics*
Software*
Uncertainty
Workflow