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Publication Detail

Title: Ontology-based metabolomics data integration with quality control.

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

Published In Bioanalysis, (2019 Jun)

Abstract: 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.

PubMed ID: 31179719 Exiting the NIEHS site

MeSH Terms: Biological Ontologies*; Data Analysis*; Diabetes Mellitus/metabolism; Humans; Metabolomics/methods*; Quality Control

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