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Related Concept Videos

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Updated: Nov 7, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Fast Healthcare Interoperability Resources (FHIR)-Based Quality Information Exchange for Clinical Next-Generation

Donghyeong Seong1, Sungwon Jung1, Sungchul Bae2

  • 1Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.

Journal of Medical Internet Research
|April 28, 2021
PubMed
Summary
This summary is machine-generated.

This study developed the NGS-QR web app to standardize and manage quality information for next-generation sequencing (NGS) genomic tests. The tool enhances the reliability of clinical genomic data by enabling standardized quality reporting and verification.

Keywords:
FHIRclinical NGS genomic testingclinical massive parallel sequencinggenomic reportingquality control

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Area of Science:

  • Genomics
  • Bioinformatics
  • Clinical Diagnostics

Background:

  • Next-generation sequencing (NGS) is crucial in clinical practice for diagnosis, classification, and treatment planning.
  • Increasing NGS test requests necessitate clear and unambiguous reporting of genomic testing results.
  • Current reports often lack sufficient quality information, hindering the assessment of genomic testing reliability due to NGS process complexity.

Purpose of the Study:

  • To develop a Fast Healthcare Interoperability Resources (FHIR)-based web application, NGS Quality Reporting (NGS-QR).
  • To standardize the reporting and management of quality information derived from clinical NGS genomic tests.
  • To facilitate the exchange of quality data for improved reliability in clinical genomics.

Main Methods:

  • Defined data elements for exchanging quality information from clinical NGS genomic tests.
  • Profiled a FHIR genomic resource for standardized information exchange.
  • Developed a FHIR-based web app and server for quality information exchange, incorporating R Shiny for statistical analysis.

Main Results:

  • Validated the NGS-QR app using approximately 1000 experimental data entries from Samsung Medical Center's CancerSCAN pipeline.
  • Demonstrated real-world data application for sharing and verifying NGS genomic testing quality status.
  • Enabled users to assess individual sample quality within the overall distribution.

Conclusions:

  • Successfully demonstrated standardized exchange of clinical NGS genomic testing quality information.
  • Quality information serves as crucial reference material for improving testing quality as genomic data accumulate.
  • The NGS-QR app can motivate laboratories to enhance diagnostic test quality and provide high-quality genomic data.