MetSign: A Computational Platform for High-Resolution Mass Spectrometry-Based Metabolomics

Abstract

Data analysis in metabolomics is currently a major challenge, particularly when large sample sets are analyzed. Herein, we present a novel computational platform entitled MetSign for high-resolution mass spectrometry-based metabolomics. By converting the instrument raw data into mzXML format as its input data, MetSign provides a suite of bioinformatics tools to perform raw data deconvolution, metabolite putative assignment, peak list alignment, normalization, statistical significance tests, unsupervised pattern recognition, and time course analysis. MetSign uses a modular design and an interactive visual data mining approach to enable efficient extraction of useful patterns from data sets. Analysis steps, designed as containers, are presented with a wizard for the user to follow analyses. Each analysis step might contain multiple analysis procedures and/or methods, and serves as a pausing point where users can interact with the system to review the results, to shape the next steps, and to return to previous steps to repeat them with different methods or parameter settings. Analysis of metabolite extract of mouse liver with spiked-in acid standards shows that MetSign outperforms the existing publically available software packages. MetSign has also been successfully applied to investigate the regulation and time course trajectory of metabolites in hepatic liver.

Biomarker discovery of liver diseases using mass spectrometry-based metabolomics.

“…The FT-MS data were processed using the software package MetSign [76]. For metabolite peak quantification, the raw instrument data were first reduced into a peak list using second-order polynomial fitting (SPF) and Gaussian mixture model (GMM).…”

“…The experimental data were processed using software package MetSign [108]. After peak alignment, a contrast based method was employed for normalization [76,77].…”

“…After peak alignment, a contrast based method was employed for normalization [76,77]. Both the Fisher's exact test and the pairwise two-tail t-test were used to study the concentration change of each metabolite between the two physiological conditions.…”

Chemoselective reactions and their applications in metabolite isolation and analysis.

“…While chromatographic purification approaches are effective for thiol metabolite identification as well as for determination of GSH/GSSG concentrations, the reliance on separation techniques can be tedious for high-throughput applications, and in some cases, unnecessary, particularly with the advent of ultra-high resolution MS instruments [133]. Thio-alkyaltions for these processes are usually conducted at high pH (8)(9), which compromises the chemoselectivity of the reagents causing alkylations of cellular amines, phenols and carboxylate salts leading to false positives in the identification of cellular thiols [134]. For applications in metabolomics, this can be a severe constraint due to interference across the many other metabolite classes that must also be analyzed for metabolic networks.…”

“…Such indiscriminate analyses result in highly complex and large data sets that require specialized software for analysis [9,10]. Although the data collection and analysis can be cumbersome for untargeted metabolomics, this approach leads to discovery of uncharacterized molecules and discovery of new pathways.…”