xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem

Area of Science:

• Metabolomics
• Bioinformatics
• Computational Biology

Background:

• High-quality data preprocessing is critical for untargeted metabolomics.
• Increasing data scale and complexity necessitate robust software solutions.
• The xcms R package has been a widely used tool for LC-MS data preprocessing since 2005.

Purpose of the Study:

• To present recent advancements in the xcms R package.
• To position xcms as a central component of a modular metabolomics software ecosystem.
• To highlight improvements in scalability and interoperability.

Main Methods:

• Utilizing the R programming language and Bioconductor framework.
• Implementing open-source, community-driven development.
• Enhancing scalability for large-scale experiments.

Main Results:

• xcms now offers enhanced scalability for processing thousands of samples.
• Improved interoperability with downstream analysis platforms and R/Bioconductor packages.
• Expanded resources including tutorials and documentation for user support.

Conclusions:

• xcms advancements solidify its role in modern metabolomics research.
• The package empowers users to build customizable and reproducible workflows.
• Integration with the broader R ecosystem extends the utility of xcms.