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Updated: Nov 20, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

A Practical Guide to Metabolomics Software Development.

Hui-Yin Chang^1,2, Sean M Colby³, Xiuxia Du⁴

¹Department of Pathology, University of Michigan, 1301 Catherine Street, Ann Arbor, Michigan 48109, United States.

Analytical Chemistry

|January 20, 2021

Summary

This summary is machine-generated.

This perspective offers guidelines for developing impactful metabolomics software tools. It covers preparation, development, and distribution to aid researchers with varying programming expertise.

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Area of Science:

Metabolomics
Bioinformatics
Scientific Software Development

Background:

Numerous software tools are available for metabolomics data processing and analysis.
Tools are often developed by practitioners with limited software engineering experience.
Users of these tools possess a wide range of technical skills, from basic data analysis to advanced programming.

Purpose of the Study:

To provide essential considerations for maximizing the impact of publicly available metabolomics software tools.
To offer best practices for metabolomics tool development.
To guide both metabolomics software users and developers.

Main Methods:

The recommendations are based on the collective experience of a working group from the NIH-sponsored Metabolomics Consortium.
The guidelines are structured into three key stages: preparation, tool development, and distribution and maintenance.
The perspective is aimed at metabolomics researchers, including those with limited formal programming backgrounds.

Main Results:

The perspective outlines critical factors influencing the success and adoption of metabolomics software.
It provides a framework for developing user-friendly and robust metabolomics tools.
The recommendations address the entire lifecycle of a software tool, from conception to ongoing support.

Conclusions:

Adhering to best practices in software development can significantly enhance the utility and impact of metabolomics tools.
Clear guidelines are needed to support researchers in creating and utilizing metabolomics software effectively.
Collaboration and shared knowledge are crucial for advancing the field of metabolomics through improved software resources.