Ontology-based metabolomics data integration with quality control

Patricia Buendia, Ray M. Bradley, Thomas J. Taylor, Emma L. Schymanski, Gary J. Patti, Mansur R. Kabuka

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1139-1155
Number of pages17
Issue number12
StatePublished - 2019


  • data integration
  • diabetes use case
  • meta-analysis
  • metabolomics
  • ontology-based expert system
  • quality control

ASJC Scopus subject areas

  • Analytical Chemistry
  • Pharmacology, Toxicology and Pharmaceutics(all)
  • Clinical Biochemistry
  • Medical Laboratory Technology


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