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

doi:10.4155/bio-2018-0303

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 journal › Article › peer-review

3 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1139-1155
Number of pages	17
Journal	Bioanalysis
Volume	11
Issue number	12
DOIs	https://doi.org/10.4155/bio-2018-0303
State	Published - 2019
Externally published	Yes

Keywords

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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@article{1725c54e8e794388bead7a69658df894,

title = "Ontology-based metabolomics data integration with quality control",

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.",

keywords = "data integration, diabetes use case, meta-analysis, metabolomics, ontology-based expert system, quality control",

author = "Patricia Buendia and Bradley, {Ray M.} and Taylor, {Thomas J.} and Schymanski, {Emma L.} and Patti, {Gary J.} and Kabuka, {Mansur R.}",

note = "Funding Information: This work was supported by a contract from the National Cancer Institute (SBIR contract 261201600065C) and this article will be deposited in NIHMS system/PMC. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Funding Information: Support for writing and revising the manuscript was provided to GJ Patti by NIH grant ES028365, and EL Schymanski by EC FP7 grant 603437.",

year = "2019",

doi = "10.4155/bio-2018-0303",

language = "English (US)",

volume = "11",

pages = "1139--1155",

journal = "Bioanalysis",

issn = "1757-6180",

publisher = "Future Science",

number = "12",

}

TY - JOUR

T1 - Ontology-based metabolomics data integration with quality control

AU - Buendia, Patricia

AU - Bradley, Ray M.

AU - Taylor, Thomas J.

AU - Schymanski, Emma L.

AU - Patti, Gary J.

AU - Kabuka, Mansur R.

N1 - Funding Information: This work was supported by a contract from the National Cancer Institute (SBIR contract 261201600065C) and this article will be deposited in NIHMS system/PMC. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. Funding Information: Support for writing and revising the manuscript was provided to GJ Patti by NIH grant ES028365, and EL Schymanski by EC FP7 grant 603437.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - data integration

KW - diabetes use case

KW - meta-analysis

KW - metabolomics

KW - ontology-based expert system

KW - quality control

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JO - Bioanalysis

JF - Bioanalysis

SN - 1757-6180

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ER -

Ontology-based metabolomics data integration with quality control

Abstract

Keywords

ASJC Scopus subject areas

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