Washington University in St. Louis

The Patti Lab
Metabolomics to elucidate novel biochemical mechanisms of disease
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Ontology-Based Metabolomics Data Integration with Quality Control

Buendia P, Bradley RM, Taylor TJ, Schymanski EL, Patti GJ, and Kabuka MR
Ontology-Based Metabolomics Data Integration with Quality Control
Bioanalysis, 11(12), 1139-1155, 2019
doi:10.4155/bio-2018-0303

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results/methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.

Washington University, Departments of Chemistry, Genetics, and Medicine. Saint Louis, Missouri 63110 USA