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MetabSpace

This MetabSpace directory contains codes, data, and instructions for using the "chemical characteristics vector" approach for capturing the chemical space of biomes and mapping them using LC-MS/MS data. The preprint for the main article related to MetabSpace is "Chemistry-based vectors map the chemical space of natural biomes from untargeted mass spectrometry data", https://doi.org/10.1101/2025.01.22.634253

Data for the article is in Zenodo: (https://doi.org/10.5281/zenodo.14506250)

General description

The following workflow describes the general idea or the whole metabolomics data analysis approach, with Step 3-4 including the "chemical characteristics vector" part.

Step 1 is LC-MS/MS peak extraction which can be done using software like MZmine, MS-DIAL or R-based package patRoon.

Step 2 is molecular fingerprint and compound class prediction using SIRIUS software.

Step 3 is our developed approach to describe the ratio of compounds in the sample with specific chemical moiety.

Step 4 illustrates how with this approach we are now able to compare the chemical space of compounds more efficiently.

github_metabspace

Code

CCV_article.R -> Codes for gathering SIRIUS chemical characteristics and calculating averaged CCVs. CCV_article_figures.R -> Code for creating Figures for the article and re-analyzing the data. Uses data from Zenodo.

Functions_SIRIUS_DataAnalysis.R -> gathers functions to get data from SIRIUS calculations. Getting annotation tables and confidence scores into one table.

Data

Most data is available in Zenodo (https://doi.org/10.5281/zenodo.14506250). For additional data, please contact Pilleriin Peets ([email protected], [email protected])

Literature

For the article "Chemistry-based vectors map the chemical space of natural biomes from untargeted mass spectrometry data", metabolomics data from the Earth Microbiome Project was used: (https://earthmicrobiome.org)

Shaffer, J.P., Nothias, LF., Thompson, L.R. et al. Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity. Nat Microbiol 7, 2128–2150 (2022). https://doi.org/10.1038/s41564-022-01266-x

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Metabolomics analysis with LC-HRMS

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