Untargeted LC–MS Data Preprocessing in Metabolomics

Liquid chromatography–mass spectrometry (LC–MS) has enabled the detection of thousands of metabolite features from a single biological sample that produces large and complex datasets. One of the key issues in LC–MS-based metabolomics is comprehensive and accurate analysis of enormous amount of data. Many free data preprocessing tools, such as XCMS, MZmine, MAVEN, and MetaboAnalyst, as well as commercial software, have been developed to facilitate data processing. However, researchers are challenged by the inevitable and unconquerable yields of numerous false-positive peaks, and human errors while manually removing such false peaks. Even with continuous improvements of data processing tools, there can still be many mistakes generated during data preprocessing. In addition, many data preprocessing software exist, and every tool has its own advantages and disadvantages. Thereby, a researcher needs to judge what kind of software or tools to choose that most suit their vendor proprietary formats and goal of downstream analysis. Here, we provided a brief introduction of the general steps of raw MS data processing, and properties of automated data processing tools. Then, characteristics of mainly free data preprocessing software were summarized for researchers’ consideration in conducting metabolomics study.