Addressing big data challenges in mass spectrometry-based metabolomics

Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub ( https://www.github.com/HuanLab ), along with revised and regularly updated content. This work elaborates on a suite of bioinformatics solutions developed in the Huan lab to address big-data challenges in metabolomics.