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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
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A complementary approach for detecting biological signals through a semi-automated feature selection tool
Gabriel Santos Arini1,2,3, Luiz Gabriel Souza Mencucini1,2, Rafael de Felício4
1Department of Biomolecular Sciences, Computational Chemical Biology Laboratory, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil.
Frontiers in Chemistry
|November 11, 2024
Summary
This study introduces RegFilter, a dynamic method to improve untargeted metabolomics by capturing ions missed in data-dependent acquisition (DDA) mode. This enhances biological signal detection and allows for custom spectral library creation.
Area of Science:
- Metabolomics
- Mass Spectrometry
- Bioinformatics
Background:
- Untargeted metabolomics commonly uses data-dependent acquisition (DDA).
- DDA has limitations including incomplete ion fragmentation and signal specificity issues.
- There is a need to extend biological signal detection beyond DDA capabilities.
Purpose of the Study:
- To enhance biological signal detection in metabolomics.
- To overcome fragmentation limitations inherent in DDA mode.
- To develop a dynamic procedure combining experimental and in silico approaches.
Main Methods:
- Liquid chromatography coupled with mass spectrometry (LC-MS) was used.
- Metabolomic analysis was performed on three actinomycete species.
- Data preprocessing involved MZmine software and custom RegFilter package.
Main Results:
- RegFilter enabled fragmentation of precursor ions missed in DDA mode.
- Most selected ions were annotated, identifying biologically relevant candidates.
- The workflow facilitated detection optimization and curation of potential biological signals.
Conclusions:
- RegFilter is a valuable complementary approach to DDA for untargeted metabolomics.
- The dynamic analysis flow improves detection and allows for custom spectral library creation.
- This method extends the coverage of biological signals in metabolomic studies.
