Enabling Large Scale Metabolomics Meta-Analyses and Data Integration of Metabolomics Workbench

PROJECT SUMMARY (30 lines) The practical reuse of genomics and transcriptomics datasets is well-demonstrated due to the use of universal gene identifiers that facilitate the matching of features across these datasets, high feature coverage, standardized metadata and data deposition formats, and a maturity in deposition quality and consistency. However, metabolomics datasets are much harder to reuse due to the lack of standardized metabolite feature identification, heterogeneity in feature coverage, and high variability in deposition quality and consistency. Therefore, it is much harder to both find relevant metabolomics datasets from repositories like Metabolomics Workbench (MWbench) and effectively reuse these datasets to generate and/or test hypotheses. To address these difficulties in reusing metabolomics datasets, the quality of deposited datasets must be improved. Furthermore, methods that enable the effective harmonization and utilization of MWbench datasets are needed, especially for large meta-analyses and integrative multi-omics analyses. We are the developers of the only set of available open-source tools for parsing, generating, validating, and depositing mwTab formatted repository files. Our experience developing the open-source mwtab and MESSES Python packages makes us uniquely qualified to develop methods to improve the FAIRness, especially Reusability, of MWbench datasets. Also, we have provided periodic feedback to MWbench based on systematic evaluations of the repository to enable the improvement of this growing public resource (2). Therefore, we propose to implement new methods and open-source tools that will improve the FAIRness of MWbench datasets through the following specific aims: Aim 1: Implement comprehensive repair, cleaning, and data harmonization tools for Metabolomics Workbench; Aim 2: Disseminate repaired, cleaned, and harmonized Metabolomics Workbench datasets to the broader biomedical community. The major innovations from this proposal are: i) advanced data repair and cleaning methods to recover unusable MWbench datasets, ii) effective data harmonization that enable multi- dataset analyses, iii) advanced search methods for finding relevant MWbench datasets, iv) effective dissemination of open-source tools with application programming interfaces (APIs) and command line interfaces (CLIs), implemented at high industrial coding and package management and delivery standards, and v) a light- weight, easy to maintain, website (meta-repository) for cleaned dataset dissemination through a web user interface (WebUI). The significance and overall impact of this proposal comes from implementing effective open-source tools that: a) comprehensively repair, clean, and harmonize MWbench datasets, greatly improving the quality of MWbench datasets, especially for large-scale reuse and b) broadly disseminate these greatly improved datasets through a combination of API, CLI, and WebUI methods. This combination of open- source tools and open web infrastructure will enhance the overall utility and FAIRness of Metabolomics Workbench, especially reuse, enabling large-scale meta-analyses and multi-omics data integrations.