Gary Siuzdak

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  Gary Siuzdak handle.invalid · Jan 27
  This @RichardZare paper doi.org/10.1021/acsm... shows spray ionization creates new molecules via microdroplet chemistry, which then undergo ISF, together explaining much of the LC–MS signal explosion. Recognizing source-derived ions is essential to keep metabolomics grounded in biology.
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  Kermit Murray kkmurray.bsky.social · Dec 18, 2025
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  BioMassSpec realbiomassspec.bsky.social · Dec 8, 2025
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  Steffen Neumann sneumann.bsky.social · Dec 8, 2025
  Out now! xcms in Peak Form: Now Anchoring a Complete Metabolomics Data Preprocessing and Analysis Software Ecosystem doi.org/10.1021/acs.... with Phillipine and @jorainer.bsky.social (EURAC), @metabomichael.bsky.social, Hendrik and Norman from @ipbhalle.bsky.social, @janstanstrup.bsky.social, et al.
  

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

  High-quality data preprocessing is essential for untargeted metabolomics experiments, where increasing data set 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 maintains 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 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.

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  Johannes Rainer handle.invalid · Dec 9, 2025
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  Gary Siuzdak handle.invalid · Nov 10, 2025
  Quantitative Systems Pharmacology (QSP) Clear, authoritative overview from PK/PD & mechanistic models to AI and regulatory use. A landmark resource for QSP, PK/PD, and translational modeling Erica Bradshaw & @Mary_Spilker Amazon: a.co/d/fZfZBSW link.springer.com/book/10.1007...
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  Gary Siuzdak handle.invalid · Oct 28, 2025
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  Gary Siuzdak handle.invalid · Oct 30, 2025
  MRM generation using adaptive modeling of tandem MS data. A simple concept that allows for MRM analysis without the need for standards. • Especially useful for preclinical metabolites, that have no standards. Analytical Chemistry (open access article) pubs.acs.org/doi/10.1021/...
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  Gary Siuzdak handle.invalid · Nov 1, 2025
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