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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

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Generalized tree structure to annotate untargeted metabolomics and stable isotope tracing data.

Shuzhao Li¹, Shujian Zheng¹

¹Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.

Biorxiv : the Preprint Server for Biology

|January 30, 2023

Summary

This study introduces khipu, a novel Python package for metabolomics data analysis. It organizes complex metabolite ions using a generalized tree structure, improving data interpretation and interoperability for various experimental designs.

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Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Published on: May 20, 2013

Area of Science:

Metabolomics
Bioinformatics
Computational Chemistry

Background:

Untargeted metabolomics generates multiple ions per metabolite (isotopes, adducts, fragments).
Computational organization of these ions is challenging without prior chemical knowledge.
Existing network-based software tools have limitations in ion interpretation.

Conclusions:

The khipu package offers a robust solution for organizing and interpreting complex metabolomics data.
Generalized pre-annotation facilitates integration with data science tools and supports flexible experimental designs.
This approach enhances the connection between metabolomics data and downstream analysis.