Search

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in GC-MS still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RI were matched to library RI with a generous window of +/-35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive, true negative or unknown. Metabolites with at least 30 true positive identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, etc.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical, metabolite-specific distributions improved the true positive and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimizes the effort to complete manual verification.

BackgroundMicrobiomes are now recognized as main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e. metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking.ResultsHere, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line based pipeline for the analysis, visualization, and presentation of metabolomics data by direct injection FTICR-MS after molecular formula assignment has been performed. When compared to all other available FTICR software, MetaboDirect is superior with respect to its compute time as it only requires a single line of code that launches a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences that provide a comprehensive experimental assessment of metabolite connectives within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of the microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses.ConclusionApplication of MetaboDirect to FTICR-MS-based metabolomics datasets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the FTICR-MS research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. Source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/) respectively.