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Genomic Common Data Model for Biomedical Data in Clinical Practice.

Seo Jeong Shin1, Seng Chan You2, Jin Roh3

  • 1Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea.

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|August 24, 2019
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Summary
This summary is machine-generated.

A new data model standardizes clinical next-generation sequencing (NGS) panel data. This enables large-scale research networks to generate evidence for better patient care using real-world clinical NGS data.

Keywords:
Data AnalysisHigh-Throughput Nucleotide SequencingObservational Study

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Area of Science:

  • Bioinformatics
  • Genomic Medicine
  • Data Science

Background:

  • Clinical next-generation sequencing (NGS) generates vast amounts of data.
  • Standardized data models are needed for large-scale analysis in distributed research networks.
  • Current data structures hinder the integration of clinical NGS data for patient care improvement.

Purpose of the Study:

  • To develop a common data model extension for next-generation sequencing (NGS) panel data.
  • To validate the feasibility of this data model within a distributed research network.
  • To facilitate the use of clinical NGS data for improving patient care.

Main Methods:

  • Developed an extension of the Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for NGS panel data.
  • Compared databases generated for research purposes versus clinical practice to assess model feasibility.
  • Utilized a distributed research network approach.

Main Results:

  • Successfully developed and implemented an OMOP-CDM extension for clinical NGS panel data.
  • Demonstrated the model's feasibility by identifying differences between research and clinical databases.
  • Confirmed the potential for large-scale data aggregation and analysis.

Conclusions:

  • The developed OMOP-CDM extension is feasible for standardizing clinical NGS panel data.
  • This data model supports distributed research networks and enhances the utility of clinical NGS data.
  • The model facilitates evidence generation for improving patient care through integrated genomic data analysis.