ArticleNature communications2025
MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Lipidomic profiling reveals age-dependent changes in plasma membrane lipids that affect neural stem cell aging.Science advances · 2026Article
- Color-associated metabolic divergence and nutritional variation in colored cauliflower curds.Food chemistry: X · 2026Article
- Decoding the Metabolic Signatures of Neurodegeneration Diseases: Advances in Mass Spectrometry-Based Metabolomics.Metabolites · 2026Review
- Reduced glutathione levels inmSystems · 2026Article
- Serum Metabolomic Signatures of Iron Deficiency Anemia: An Untargeted LC-HRMS-Based Case-Control Study.Anemia · 2026Article
- Review
- The Metabolome Atlas of 22 Tissues in Aging Mice Reveals a Switch in Thermogenesis from Brown Fat to Skeletal Muscle.Research square · 2025Article
- Structural annotation of full-scan MS data: A unified solution for LC-MS and MS imaging analyses.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
6 authors.
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Abstract
Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by both identity- and fuzzy searches. Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. MassCube outperforms MS-DIAL, MZmine3 or XCMS for speed, isomer detection, and accuracy. It supports diverse numerical routines for MS data analysis while maintaining efficiency, capable for handling 105 GB of Astral MS data on a laptop within 64 min, while other programs took 8-24 times longer. MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.
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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.