Abstract
Abstract
Background
Two decades after the Metabolomics Standards Initiative, public deposition of raw LC-MS/MS data into archives such as MetaboLights, Metabolomics Workbench, GNPS and MassBank has matured, but the analyses that turn raw files into reported feature tables remain difficult to share, audit and reuse. The FAIR principles, originally formulated for data, later extended to research software and to workflows, make a strong case that this gap matters; we adopt that view and ask how an educational resource can help researchers cross it.
Aim of review
This is an educational review where we describe the principles of FAIR and reproducible analysis as they apply to untargeted LC-MS metabolomics, and we show how those principles are realised in
Metabonaut
, an open educational resource built on top of the R for Mass Spectrometry/Bioconductor ecosystem.
Metabonaut
provides executable example workflows from raw mzML files to annotated feature tables, demonstrates interoperability across programming languages, and is built with and for the community.
Key scientific concepts of review
After a brief tour of the underlying software ecosystem and of literate programming, the review discusses three topics, each through one or more
Metabonaut
vignettes: visual inspection and quality control, reuse of public archives at scale, and cross-language interoperability. Together, they teach how to carry FAIR practice from data to analysis: making the analysis itself, and not only the data it consumes, shareable, auditable and reusable. The source code of Metabonaut and links to pre-rendered workflow documents are available at
https://github.com/RforMassSpectrometry/Metabonaut
.