METABOLIC PATHWAY RESOURCES AND ANALYSIS
Metabolic pathway analysis plays a crucial role in metabolomics research as it allows for the interpretation of metabolomic data in the context of biochemical pathways and networks. Several bioinformatics resources and tools have been developed to facilitate pathway mapping, visualization, and analysis.
Major Pathway Databases
KEGG (Kyoto Encyclopedia of Genes and Genomes) is one of the most widely used pathway databases in metabolomics research. It provides a comprehensive collection of metabolic pathways, including information on enzymes, reactions, and metabolites.
Reactome is another valuable resource for metabolic pathway analysis. It focuses on human metabolism and provides detailed information on metabolic reactions, enzymes, and their regulation. Reactome also integrates other biological processes such as signaling pathways and gene expression, allowing for a more comprehensive understanding of cellular processes.
Author(s) Details:
Ebenezer Morayo Ale
Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, Taraba State, Nigeria.
Olanrewaju Roland Akinseye
HAT Unit, Nigerian Institute for Trypanosomiasis & Onchocerciasis Research, Ibadan, Nigeria.
Richard-Harris Nsenreuti Boyi
Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, Taraba State, Nigeria.
Victoria Ifeoluwa Ayo
Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, Taraba State, Nigeria.
Mgbede Joy Timothy
Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, Taraba State, Nigeria.
Steve Osagie Asuelimen
Department of Biochemistry, Faculty of Pure and Applied Sciences, Federal University Wukari, Taraba State, Nigeria.
Recent Global Research Developments in Comparing Normalization Strategies for GC-Based Metabolomics
In a recent study, researchers evaluated various normalization strategies for GC-based metabolomics. [1]
Sample Preparation and Derivatization:
- GC-based metabolomics often involves extensive sample preparation, including extraction and derivatization (methoximation and trimethylsilylation).
- Correcting for variations introduced during these steps is crucial for reliable data analysis.
Normalization Strategies:
- Sample Mass Normalization: Commonly used, but not always optimal.
- Total Peak Area (TPA): Another frequently employed method.
- Total Derivatized Peak Area (TDPA): A novel approach, where data are normalized to the intensity of all derivatized compounds. TDPA leverages silylation as a universal derivatization method for GC-based metabolomics studies.
Evaluation:
- Researchers simulated two sample classes with systematically incremented sample mass, differing only in added amino acid concentrations.
- They analyzed the samples using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS).
- Among the five normalization strategies tested (no normalization, sample mass, TPA, total useful peak area (TUPA), and TDPA), TUPA and TDPA were the most effective.
- TUPA requires peak alignment across all samples, while TDPA is alignment-free.
Conclusion:
- These findings enhance the convenient and effective use of data normalization strategies in metabolomics research.
- TDPA, based on derivatized peak areas, shows promise for overcoming current data normalization challenges.
References
- Nam, S.L., Giebelhaus, R.T., Tarazona Carrillo, K.S. et al. Evaluation of normalization strategies for GC-based metabolomics. Metabolomics 20, 22 (2024). https://doi.org/10.1007/s11306-023-02086-8