The Metabolomics Workbench File Status Website: A Metadata Repository Promoting FAIR Principles of Metabolomics Data

Abstract

Motivation: An updated version of the mwtab Python package for programmatic access to the Metabolomics Workbench (MetabolomicsWB) data repository was released at the beginning of 2021. Along with updating the package to match the changes to MetabolomicsWB's 'mwTab' file format and to enhance the package's functionality, the package included format validation facilities which were used to detect and catalog file inconsistencies and errors across all publically available datasets in MetabolomicsWB. Results: The Metabolomics Workbench File Status website was developed to provide continuous validation of MetabolomicsWB data files and a useful interface to find all inconsistencies and errors that are found. This list of detectable issues/errors include format parsing errors, format compliance issues, access problems via MetabolomicsWB's REST interface, and other small inconsistencies that can hinder reusability. The website uses the mwtab Python package to pull down and validate each available analysis file and then generates an html report. The website is updated on a weekly basis. Moreover, the Python website design utilizes GitHub and GitHub.io, providing an easy to replicate template for implementing other metadata, virtual, and meta- repositories. Availability: Metabolomics Workbench file status website can be accessed at: https://moseleybioinformaticslab.github.io/mwFileStatusWebsite/. The mwtab Python library is available on GitHub: https://github.com/MoseleyBioinformaticsLab/mwtab, PyPI: https://pypi.org/project/mwtab/, and documentation is available on ReadTheDocs: https://mwtab.readthedocs.io/. Metabolomics Workbench analysis data files used for analysis presented here along with the generated HTML files of the website are available on FigShare: https://doi.org/10.6084/m9.figshare.19221159.

A FAIR approach for detecting and sharing PFAS hot-spot areas and water systems

“…The result of this research was verified by the sampling result generated by the Kentucky Department of Environmental Protection Agency (KDEP) [28]. KDEP sampled eighty-one (81) public drinking water systems, of which forty-three (43) are surface water sources and thirty-eight (38) are groundwater sources. In 2019, KDEP directly tested for eight (8) PFAS, including Perfluorobutane sulfonate (PFBS), Hexafluoropropylene oxide-dimer(HFDO-DA or Gen-X), perfluoroheptanoic acid (PFHpA), Perfluorohexane sulfonate (PFHxS), Ammonium 4,8-dioxa-3Hperfluorononanoate (ADONA), PFOA, PFOS, and Perfluorononanoic acid (PFNA) [29].…”

“…Even with minimum standards, deposition quality is a constant issue that hinders FAIRness [41][42][43]. For research data types that have no procedures or minimum standards, these problems are far worse.…”

“…The associated procedures describe: i) generating a dataset on ArcGIS, ii) organizing the dataset under a consistent directory structure , iii) creation of adequate metadata and description in draft MIAGIS JSON format, iv) uploading the dataset to a general open data repository like Figshare [45] or Zenodo [46], v) generating a Digital Object Identifier (DOI), and vi) and cross-linking the public ArcGIS dataset with the public open repository. Our goal is to kickstart the development of a FAIR deposition standard for GIS datasets as well as a communityserviceable meta repository [43] for GIS datasets, which could be trivially adapted for QGIS generated datasets as well. This grass roots approach has reached a level of success for other research communities [47].…”

CloMet: A Novel Open-Source and Modular Software Platform That Connects Established Metabolomics Repositories and Data Analysis Resources

“…As far as CloMet FAIRness is concerned, CloMet inherits the FAIR principles of the data coming from Metabolights and Metabolomics Workbench. These two repositories already promote FAIR principles. , Thus, CloMet does not add any new indexing scheme nor modifies any file format, but it just adopts the data properties of its sources. Our custom data format is an internal standard that harmonizes the different formats found in public repositories.…”

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

“…Given the lack of need for proprietary software to export pixel-by-pixel data, an individual feature list provides MALDI-MS instrument/application flexibility. Additionally, considering each pixel data as a replicate from a defined ROI, exported pixel data can also be used for multivariate spatial metabolomics data analysis using either vendor-specific or non-proprietary metabolomics data software platforms [27][28][29][30][31][32][33][34][35][36][37][38][39] . Figure S1 shows an exemplary conversion of ROI pixel data to formats compatible with import into MetaboAnlayst 27 and Agilent Mass Hunter Professional, allowing for multivariate statistical analysis and biomarker discovery.…”