Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data.
Document Type
Article
Publication Date
4-18-2023
Original Citation
Li S,
Zheng S.
Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data. Anal Chem. 2023;95(15):6212-7.
Keywords
JGM, Metabolomics, Algorithms, Software, Isotopes, Ions
JAX Source
Anal Chem. 2023;95(15):6212-7.
ISSN
1520-6882
PMID
37018697
DOI
https://doi.org/10.1021/acs.analchem.2c05810
Grant
This work was in parted funded by NIH grants (to SL) U01 CA235493 (NCI) and R01 AI149746 (NIAID).
Abstract
In untargeted metabolomics, multiple ions are often measured for each original metabolite, including isotopic forms and in-source modifications, such as adducts and fragments. Without prior knowledge of the chemical identity or formula, computational organization and interpretation of these ions is challenging, which is the deficit of previous software tools that perform the task using network algorithms. We propose here a generalized tree structure to annotate ions in relationships to the original compound and infer neutral mass. An algorithm is presented to convert mass distance networks to this tree structure with high fidelity. This method is useful for both regular untargeted metabolomics and stable isotope tracing experiments. It is implemented as a Python package (khipu) and provides a JSON format for easy data exchange and software interoperability. By generalized preannotation, khipu makes it feasible to connect metabolomics data with common data science tools and supports flexible experimental designs.