A Practical Guide to Metabolomics Software Development
Author(s) -
Hui-Yin Chang,
Sean Colby,
Xiuxia Du,
Javier D. Gomez,
Maximilian J. Helf,
Katerina Kechris,
Christine R. Kirkpatrick,
Shuzhao Li,
Gary J. Patti,
Ryan Renslow,
Shankar Subramaniam,
Mukesh Verma,
Jianguo Xia,
Jamey D. Young
Publication year - 2021
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.0c03581
Subject(s) - metabolomics , software , software development , data science , software analytics , computer science , coding (social sciences) , software engineering , software development process , chemistry , statistics , mathematics , chromatography , programming language
A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.
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