Chemistry-based vectors map the chemical space of natural biomes from untargeted mass spectrometry data
Peets, P.; Litos, A.; Duehrkop, K.; Garza, D. R.; van der Hooft, J. J. J.; Boecker, S.; Dutilh, B. E.
Show abstract
Untargeted metabolomics can comprehensively map the chemical space of a biome, but is limited by low annotation rates (<10%). We used chemistry-based vectors, consisting of molecular fingerprints or chemical compound classes, predicted from mass spectrometry data, to characterize compounds and samples. These chemical characteristics vectors (CCVs) estimate the fraction of compounds with specific chemical properties in a sample. Unlike the aligned MS1 data with intensity information, CCVs incorporate actual chemical properties of compounds, offering deeper insights into sample comparisons. Thus, we identified key compound classes differentiating biomes, such as ethers which are enriched in environmental biomes, while steroids enriched in animal host-related biomes. In biomes with greater variability, CCVs revealed key clustering compound classes, such as organonitrogen compounds in animal distal gut and lipids in animal secretions. CCVs thus enhance the interpretation of untargeted metabolomic data, providing a quantifiable and generalizable understanding of the chemical space of natural biomes.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- MS-CleanR: A feature-filtering approach to improve annotation rate in untargeted LC-MS based metabolomics 96%
- SMART: an approach for accurate formula assignment in spatially-resolved metabolomics 96%
- Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies 96%
Similar papers in this journal
Similar papers in this journal
- Biological Function Assignment Across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm 95%
- A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases 95%
- Public LC-Orbitrap-MS/MS Spectral Library for Metabolite Identification 94%
Similar papers in this journal
- Rapid detection of Staphylococcus aureus and Streptococcus pneumoniae by real-time analysis of volatile metabolites 94%
- Spatial proteomics reveals subcellular reorganization in human keratinocytes exposed to UVA light 91%
- Shotgun lipidomics and mass spectrometry imaging unveil diversity and dynamics in lipid composition in Gammarus fossarum 91%
Similar papers in this journal
- High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins 95%
- Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets 95%
- Native Triboelectric Nanogenerator Ion Mobility-Mass Spectrometry of Egg Proteins Relevant to Objects of Cultural Heritage at Picoliter and Nanomolar Quantities. 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.