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Updated: Aug 4, 2025

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

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

Published on: September 20, 2022

Generalized Tree Structure to Annotate Untargeted Metabolomics and Stable Isotope Tracing Data.

Shuzhao Li¹, Shujian Zheng¹

¹Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06032, United States.

Analytical Chemistry

|April 5, 2023

Summary

This study introduces khipu, a novel Python package for metabolomics data analysis. It organizes complex ion data using a generalized tree structure, improving the interpretation of untargeted metabolomics and stable isotope tracing experiments.

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

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Published on: May 20, 2013

Area of Science:

Computational Biology
Metabolomics
Bioinformatics

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 has limitations in interpreting complex ion data.

Purpose of the Study:

To develop a generalized tree structure for annotating ions and inferring neutral mass.
To present an algorithm for converting mass distance networks to this tree structure.
To provide a computational tool for enhanced metabolomics data interpretation.

Main Methods:

Developed a generalized tree structure for ion annotation.
Created an algorithm to convert mass distance networks to the proposed tree structure.
Implemented the method as a Python package named khipu.

Main Results:

The khipu package successfully annotates ions and infers neutral mass.
The algorithm achieves high fidelity in converting mass distance networks to tree structures.
khipu supports both untargeted metabolomics and stable isotope tracing experiments.

Conclusions:

The generalized tree structure provides a robust method for organizing and interpreting complex metabolomics ion data.
khipu facilitates data exchange and interoperability through its JSON format.
This approach enhances the connection of metabolomics data with data science tools and supports flexible experimental designs.