Recent Advances in Computational Methods for Data Analysis in Untargeted Mass-Spectrometry Based Metabolomics
A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Bioinformatics and Data Analysis".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2631
Special Issue Editors
Interests: systems biology; metabolomics; omics data analysis; kinetic modeling of cellular systems, computational biochemistry
Interests: metabolomics; chemical profile of grapevine and wine; identification of biomarkers of disease resistance through extreme resolution mass spectrometry-based metabolomics
Interests: fourier transform-ion-cyclotron resonance mass spectrometry; protein and PTM characterization; native MS; metabolomics
Special Issue Information
Dear Colleagues,
The emergence of analytical platforms that achieved unprecedent sensitivity, resolution, and mass accuracy projected mass-spectrometry-based metabolomics to a remarkable development in recent years, leading to an increase in reliability and coverage in the analysis of complex samples, ranging from environmental organic matter to biological materials.
In any metabolomics workflow, but particularly applied to untargeted approaches, data analysis is one of the most critical stages, due to the inherent complexity of the methodology. The continuous increase in this complexity, fostered by the evolution of instrumental and analytical techniques, brings new challenges to data analysis, and the development of new methods is paramount to follow up with analytical progress. In this context, the time is right to develop new computational methods of metabolomics data analysis to take full advantage of the increase in analytical performance. Therefore, this Special Issue of Metabolites will be dedicated to current advances in the development of data analysis methods, encompassing various aspects of the metabolomics workflow: analysis of raw instrumental signals, spectral feature processing, hyphenated methods’ annotations, spectra alignment, data pretreatment and preprocessing, deep learning and other machine learning methods, data representation and visualization, information extraction, and multiomics integration.
Prof. Dr. António Ferreira
Dr. Marta Sousa Silva
Dr. Carlos Cordeiro
Guest Editors
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Keywords
- metabolomics data analysis
- mass-spectrometry-based metabolomics
- signal processing
- spectral feature annotation
- machine learning in metabolomics
- multiomics integration
- computational methods
- untargeted metabolomics
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