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MetaNetter 2: A Cytoscape plugin for ab initio network analysis and metabolite feature classification.

K E V Burgess¹, Y Borutzki¹, N Rankin²

¹Glasgow Polyomics, University of Glasgow, Glasgow, United Kingdom; Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow, United Kingdom.

Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences

|October 15, 2017

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This summary is machine-generated.

MetaNetter 2 enhances metabolomics by creating biochemical networks from mass spectrometry data. This tool aids in identifying compound relationships and analyzing adduct patterns for biological insights.

Area of Science:

Metabolomics and Bioinformatics
Computational Chemistry
Systems Biology

Background:

Metabolomics generates complex high-resolution mass spectrometry data.
Data analysis is challenging due to artefacts, noise, adducts, and fragmentation.
Understanding compound relationships is crucial for biological interpretation.

Purpose of the Study:

Introduce MetaNetter 2, an improved biochemical networking tool.
Enhance data analysis by generating adduct networks and cross-sample pattern tables.
Facilitate the study of compound relationships in complex metabolomic datasets.

Main Methods:

Utilized Cytoscape plugin for ab initio network generation.
Developed adduct network creation capabilities.

Keywords:

Ab-initio network Mass spectrometry MetaNetter Metabolomics

Related Experiment Videos

Implemented tables for mapping adduct and transformation patterns across samples.

Applied the tool to analyze adduct patterns under different chromatographic conditions.

Performed chemical transformation analysis on single and all-ions fragmentation datasets.

Main Results:

Demonstrated MetaNetter 2's ability to generate adduct networks.
Showcased the creation of tables mapping adduct/transformation patterns.
Analyzed the impact of buffer conditions on adduct detection.
Applied chemical transformation analysis to fragmentation data.
Successfully analyzed a Staphylococcus aureus growth dataset.

Conclusions:

MetaNetter 2 provides significant improvements for metabolomic data analysis.
The tool effectively reveals compound relationships through adduct and transformation networks.
MetaNetter 2 is valuable for inferring biochemical processes and biological differences from metabolomic data.