High-performance computing (HPC) environments are crucial for computational research, including quantum chemistry (QC), but pose challenges for non-expert users. Researchers with limited computational knowledge struggle to utilise domain-specific software and access mass spectra prediction for in silico annotation. Here, we provide a robust workflow that leverages interoperable file formats for molecular structures to ensure integration across various QC tools. The quantum chemistry package for mass spectral predictions after electron ionization or collision-induced dissociation has been integrated into the Galaxy platform, enabling automated analysis of fragmentation mechanisms. The extended tight binding quantum chemistry package, chosen for its balance between accuracy and computational efficiency, provides molecular geometry optimisation. A Docker image encapsulates the necessary software stack. We demonstrated the workflow for four molecules, highlighting the scalability and efficiency of our solution via runtime performance analysis. This work shows how non-HPC users can make these predictions effortlessly, using advanced computational tools without needing in-depth expertise.

• Publication type
• Journal Article MeSH

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

• Keywords
• chemical space, chromatography, environmental exposures, exposome, high-resolution mass spectrometry, metabolomics, non-targeted analysis, toxicants,
• MeSH
• Exposome MeSH
• Mass Spectrometry * methods   MeSH
• Humans MeSH
• Metabolomics MeSH
• Proteomics methods   MeSH
• Environmental Exposure MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

The exposome concept encourages holistic consideration of the non-genetic factors (environmental exposures including lifestyle) that influence an individual's health over their life course. However, disconnect between the concept and practical application has promoted divergent interpretations of the exposome across disciplines and reinforced separation of the environmental (emphasizing exposures) and biological (emphasizing responses) research communities. In particular, while knowledge of biological responses can help to distinguish actual (i.e. experienced) from potential exposures, the inclusion of endogenous processes has generated confusion about the position of the exposome in a multi-omics systems biology context. We propose a reattribution of "exposome" to exclusively represent the totality of contact with external factors that a biological entity experiences, and introduce the term "functional exposomics" to denote the systematic study of exposure-phenotype interaction. This reoriented definition of the exposome allows a more readily integrable dataset for multi-omics and systems biology research.

• Keywords
• Environmental health, Exposure assessment, Omics,
• Publication type
• Journal Article MeSH
• Review MeSH