One of the major challenges in liquid chromatography coupled to mass spectrometry data is converting many metabolic feature entries to biological function information, such as metabolite annotation and pathway enrichment, which are based on the compound and pathway databases. Multiple online databases have been developed. However, no tool has been developed for operating all these databases for biological analysis. Therefore, we developed massDatabase, an R package that operates the online public databases and combines with other tools for streamlined compound annotation and pathway enrichment. massDatabase is a flexible, simple and powerful tool that can be installed on all platforms, allowing the users to leverage all the online public databases for biological function mining. A detailed tutorial and a case study are provided in the Supplementary Material.
Conventional environmental health studies have primarily focused on limited environmental stressors at the population level, which lacks the power to dissect the complexity and heterogeneity of individualized environmental exposures. Here, as a pilot case study, we integrated deep-profiled longitudinal personal exposome and internal multi-omics to systematically investigate how the exposome shapes a single individual's phenome. We annotated thousands of chemical and biological components in the personal exposome cloud and found they were significantly correlated with thousands of internal biomolecules, which was further cross-validated using corresponding clinical data. Our results showed that agrochemicals and fungi predominated in the highly diverse and dynamic personal exposome, and the biomolecules and pathways related to the individual's immune system, kidney, and liver were highly associated with the personal external exposome. Overall, this data-driven longitudinal monitoring study shows the potential dynamic interactions between the personal exposome and internal multi-omics, as well as the impact of the exposome on precision health by producing abundant testable hypotheses.
Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.