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Updated: Aug 11, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.

Xinsong Du¹, Farhad Dastmalchi¹, Hao Ye²

¹Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, Gainesville, FL, USA.

Metabolomics : Official Journal of the Metabolomic Society

|February 6, 2023

Summary

This summary is machine-generated.

Metabolomics software for LC-HRMS data processing shows low adherence to FAIR4RS principles. Improvements are needed in semantic annotation, DOI registration, containerization, and documentation for better research software reusability.

Keywords:

FAIR principles Liquid chromatography-mass spectrometry Metabolomics Open science Reproducibility Research software

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Last Updated: Aug 11, 2025

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

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Published on: September 20, 2022

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

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Published on: May 20, 2013

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Metabolomics
Computational Biology
Scientific Software Engineering

Background:

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is crucial for metabolomics data acquisition.
The FAIR Principles (Findability, Accessibility, Interoperability, Reusability) promote open science and data reuse.
FAIR principles have been extended to research software (FAIR4RS) to enhance software reusability.

Approach:

Systematically reviewed 124 LC-HRMS metabolomics data processing software.
Evaluated 61 selected software against FAIR4RS-related criteria.
Assigned criteria to FAIR4RS categories through literature review and expert discussion.

Key Points:

Software FAIRness ranged from 21.6% to 71.8%, with no significant improvement over time.
Low fulfillment (<17%) in semantic annotation, DOI registration (Zenodo), containerization, and code documentation.
Identified critical areas for improving FAIRness in research software.

Conclusions:

The study provides an implementation solution for adopting FAIR4RS in metabolomics software.
Evaluation guidelines and results can enhance the FAIRness of research software.
Discussed strategies and future directions for improving FAIR4RS adherence in scientific software.