Computational Plant Biochemistry

High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing dataset scale and complexity demand adaptable, robust, and reproducible software solutions. Modern preprocessing tools must evolve to integrate seamlessly with downstream analysis platforms, ensuring efficient and streamlined workflows. Since its introduction in 2005, the xcms R package has become one of the most widely used tools for LC-MS data preprocessing. Developed through an open-source, community-driven approach, xcms has maintained long-term stability while continuously expanding its capabilities and accessibility. We present recent advancements that position xcms as a central component of a modular and interoperable software ecosystem for metabolomics data analysis. Key improvements include enhanced scalability, enabling the processing of large-scale experiments with thousands of samples on standard computing hardware. These developments empower users to build comprehensive, customizable, and reproducible workflows tailored to diverse experimental designs and analytical needs. An expanding collection of tutorials, documentation, and teaching materials further supports both new and experienced users in leveraging the broader R and Bioconductor ecosystems. These resources facilitate the integration of statistical modeling, visualization tools, and domain-specific packages, extending the reach and impact of xcms workflows. Together, these enhancements solidify xcms as a cornerstone of modern metabolomics research.

Advances in mass spectrometry (MS) instrumentation, including higher resolution, faster scan speeds, and improved sensitivity, have dramatically increased the data volume and complexity. The adoption of imaging and ion mobility further amplifies these challenges in proteomics, metabolomics, and lipidomics. Current open formats such as mzML and imzML struggle to keep pace due to large file sizes, slow data access, and limited metadata support. Vendor-specific formats offer faster access but lack interoperability and long-term archival guarantees. We here lay the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multidimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human- and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will support complex workflows and regulatory compliance through faster access, improved metadata, and interoperability. For vendors, it offers a streamlined, open alternative to proprietary formats. mzPeak aims to become a cornerstone of MS data management, enabling sustainable, high-performance solutions for future data types and fostering collaboration across the mass spectrometry community.

Results of scientific work in chemistry can usually be obtained in the form of materials and data. A big step towards transparency and reproducibility of the scientific work can be gained if scientists publish their data in research data repositories in a FAIR manner. Nevertheless, in order to make chemistry a sustainable discipline, obtaining FAIR data is insufficient and a comprehensive concept that includes preservation of materials is needed. In order to offer a comprehensive infrastructure to find and access data and materials that were generated in chemistry projects, we combined the infrastructure Chemotion repository with an archive for chemical compounds. Samples play a key role in this concept: we describe how FAIR metadata of a virtual sample representation can be used to refer to a physically available sample in a materials’ archive and to link it with the FAIR research data gained using the said sample. We further describe the measures to make the physically available samples not only FAIR through their metadata but also findable, accessible and reusable.