ArticleMethods in molecular biology (Clifton, N.J.)2026
Computational Tools for LC-IMS-MS Data Processing in Metabolomics.
Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
5 authors.
Funding
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Abstract
This chapter provides resources and step-by-step processing guidelines for analyzing liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data in metabolomics. The methods described here are based on open-source software and freely available executables developed at Pacific Northwest National Laboratory (PNNL), including PNNL-PreProcessor, MZA, mzapy, LipidOz, PeakQC, and IonToolPack. Importantly, the same software ecosystem is broadly applicable to IMS-MS workflows both with and without LC and includes algorithms that support other modalities such as proteomics, making it suitable for a wide range of experimental designs. The chapter is written for scientists seeking to establish reproducible workflows to analyze multidimensional metabolomics data, regardless of prior experience with IMS. Demonstrations for both Python-based programmatic data processing and graphical user interface (GUI) workflows are provided to facilitate implementation by users with different levels of computational expertise. Following these procedures, researchers can successfully process, visualize, and interpret LC-IMS-MS data using freely available software and data resources.
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Identifiers
42680970What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.