Abstract
Background
Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles – Findability, Accessibility, Interoperability, and Reusability – were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).
Aim of review
This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.
Key scientific concepts of review
We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.
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Acknowledgements
The authors thank Biswapriya Misra, Ph.D., for his constructive remarks and useful suggestions for the study. The authors also sincerely thank Bailey Ballard and Jianming (Jennifer) Wang for their help in the process of title-abstract screening. We would like to express a special thank to all software authors that took the time out of their busy schedule to respond our emails and provide thoughtful feedback regarding the annotation of software functions.
Funding
Research reported in this publication was supported by the University of Florida Informatics Institute Fellowship Program. Research reported in this publication was also supported by Southeast Center for Integrated Metabolomics at the University of Florida, the National Institute of Diabetes and Digestive and Kidney Diseases (K01DK115632), the University of Florida Clinical and Translational Science Institute (UL1TR001427). The content is solely the responsibility of the authors and does not necessarily represent the official views the University of Florida Informatics Institute, Southeast Center for Integrated Metabolomics at the University of Florida, University of Florida Clinical and Translational Science Institute, or the National Institutes of Health.
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XD conceived the idea, designed the study, participated in the paper review, participated in software evaluation, participated in designing software evaluation criteria and assigning criteria to corresponding FAIR4RS categories as a major contributor, drafted the original version of the categorization and description of LC-HRMS metabolomics data processing steps, performed and programmed for all analysis and visualization, interpreted results, prepared the original draft, and revised the manuscript. FD participated in the paper review and software evaluation. HY provided literature search terms and databases, participated in designing software evaluation criteria and assigned criteria to corresponding FAIR4RS categories. TJL provided expertise regarding study design, revised the categorization and description of LC-HRMS metabolomics data processing steps, and revised the manuscript. MAD participated in designing software evaluation criteria and assigning criteria to corresponding FAIR4RS categories. ML provided expertise regarding statistical analysis. WRH revised the manuscript. MB revised the manuscript. DJL conceived the idea, provided expertise regarding study design, provided expertise regarding data analysis and visualization, interpreted results, and revised the manuscript.
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Du, X., Dastmalchi, F., Ye, H. et al. Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software. Metabolomics 19, 11 (2023). https://doi.org/10.1007/s11306-023-01974-3
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DOI: https://doi.org/10.1007/s11306-023-01974-3