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Tools for the functional interpretation of metabolomic experiments
Monica Chagoyen1, Florencio Pazos
1National Center for Biotechnology (CNB-CSIC), Darwin 3. 28049 Madrid, Spain. pazos@cnb.csic.es.
Briefings in Bioinformatics
|October 16, 2012
Summary
Modern metabolomics requires automated analysis tools. New methods, adapted from other
Area of Science:
- Biological Sciences
- Biochemistry
- Systems Biology
Background:
- 'Omics' approaches characterize molecular profiles of biological systems.
- Metabolomics, a recent 'omics' field, analyzes the complete set of metabolites.
- Increasing data volume necessitates automated analysis for biological insights.
Purpose of the Study:
- To highlight the need for automated analysis in metabolomics.
- To introduce emerging computational tools for metabolomic data interpretation.
Main Methods:
- Review of existing computational approaches in 'omics' fields.
- Adaptation of annotation enrichment analysis for metabolomics.
- Utilizing generic metabolic analysis and visualization tools.
Main Results:
- Specialized metabolomic analysis tools are emerging.
- Existing 'omics' and generic tools can be applied to metabolomic data.
- Automated analysis is crucial for extracting biological meaning from large metabolomic datasets.
Conclusions:
- Automated analysis tools are essential for the future of metabolomics research.
- The integration of various computational methods will advance the field.
