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Updated: Jul 30, 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

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis.

Eftychia E Kontou¹, Axel Walter^2,3, Oliver Alka^2,3

¹The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark.

Journal of Cheminformatics

|May 12, 2023

Summary

This summary is machine-generated.

UmetaFlow offers automated, reproducible untargeted metabolomics data processing. This computational workflow enhances accuracy and speed for analyzing complex metabolomics datasets, aiding secondary metabolite discovery.

Keywords:

Analysis High-throughput workflow Processing Software Untargeted metabolomics

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Last Updated: Jul 30, 2025

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

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Published on: September 20, 2022

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

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Published on: May 20, 2013

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Computational biology
Metabolomics
Bioinformatics

Background:

Metabolomics experiments generate complex data, demanding manual processing that is time-consuming and error-prone.
There is a need for automated, fast, reproducible, and accurate methods for metabolomics data processing and dereplication.

Purpose of the Study:

To present UmetaFlow, a computational workflow for untargeted metabolomics data analysis.
To provide a scalable, reproducible, and user-friendly platform for processing and interpreting complex metabolomics datasets.

Main Methods:

UmetaFlow integrates algorithms for data pre-processing, spectral matching, and molecular formula/structure prediction.
The workflow is implemented in Snakemake and Jupyter notebooks (using pyOpenMS) and offers a web-based GUI.
It integrates with GNPS workflows for downstream analysis, including Feature-Based and Ion Identity Molecular Networking.

Main Results:

UmetaFlow accurately annotated 76% of molecular formulas and 65% of structures in actinomycetes datasets.
Benchmarking on public datasets (MTBLS733, MTBLS736) showed detection of over 90% of ground truth features.
The workflow demonstrated excellent performance in quantification and discriminating marker selection.

Conclusions:

UmetaFlow provides a robust and efficient solution for the automated processing and interpretation of untargeted metabolomics data.
The platform's scalability, reproducibility, and integration capabilities make it valuable for analyzing large-scale metabolomics datasets.
UmetaFlow is expected to accelerate the discovery and annotation of metabolites in complex biological samples.