Abstract
Untargeted metabolomics experiments for characterizing complex biological samples, conducted with chromatography/mass spectrometry technology, generate large datasets containing very complex and highly variable information. Many data-processing options are available, however, both commercial and open-source solutions for data processing have limitations, such as vendor platform exclusivity and/or requiring familiarity with diverse programming languages. Data processing of untargeted metabolite data is a particular problem for laboratories that specialize in non-routine mass spectrometry analysis of diverse sample types across humans, animals, plants, fungi, and microorganisms. Here, we present MStractor, an R workflow package developed to streamline and enhance pre-processing of metabolomics mass spectrometry data and visualization. MStractor combines functions for molecular feature extraction with user-friendly dedicated GUIs for chromatographic and mass spectromerty (MS) parameter input, graphical quality-control outputs, and descriptive statistics. MStractor performance was evaluated through a detailed comparison with XCMS Online. The MStractor package is freely available on GitHub at the MetabolomicsSA repository.
Highlights
Over the last few decades, mass spectrometry (MS) has become the technique of choice to profile metabolites in biological systems, when untargeted strategies are required for characterizing the complexity of biological samples
The MStractor workflow translates raw mass spectrometry data into a base peak table output that can be used for statistical analysis
It is freely available on Github and combines a range of tools into a seamless workflow for processing large batches of metabolomics datasets quickly and
Summary
Over the last few decades, mass spectrometry (MS) has become the technique of choice to profile metabolites in biological systems, when untargeted strategies are required for characterizing the complexity of biological samples. Data processing has become a critical step that limits throughput, productivity, and potentially the quality of interpretation of raw mass spectrometry data. Availability of bioinformatics workflows and software that enable reliable data pre-processing, rapid throughput, and production of reliable information is essential for the quality of the analytical results and biological interpretation [1,2,3,4]. Software for processing metabolomics data is in either a proprietary or open-source format [1,5]. There are many solutions available, with various degrees of sophistication; some software supports the entire metabolomics data-processing pipeline, while other programs are specialized for specific tasks such as feature detection, statistical analysis, or metabolite identification. Many tools offer processing solutions limited to single technology platforms such as GC/MS [6] or LC/MS
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