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Mass Spectrometry-Based Proteomics Analyses Using the OpenProt Database to Unveil Novel Proteins Translated from Non-Canonical Open Reading Frames
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The openEHR Genomics Project.

Cecilia Mascia1, Francesca Frexia1, Paolo Uva1

  • 1CRS4: Center for Advanced Studies, Research and Development in Sardinia.

Studies in Health Technology and Informatics
|June 24, 2020
PubMed
Summary
This summary is machine-generated.

High-throughput sequencing generates vast genomic data. We developed openEHR genomic information models to improve data interpretation, quality, and interoperability for clinical decisions.

Keywords:
genomic modelsmutationsopenEHRstructured datavariations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Health Informatics

Background:

  • High-throughput sequencing generates large genomic datasets, increasing genetic testing capabilities.
  • Genomic data interpretation for clinical decisions remains challenging due to data complexity and size.
  • Current workflows struggle with the granular nature and computational dependencies of genomic data.

Purpose of the Study:

  • To introduce the openEHR Genomic Project.
  • To present novel genomic information models for managing complex genomic data.
  • To enhance the machine-readability and provenance of genomic data.

Main Methods:

  • Development of openEHR-compliant genomic information models.
  • Focus on capturing complex genomic data structures.
  • Implementation of efficient data provenance preservation.

Main Results:

  • Creation of a set of genomic information models.
  • Models designed for machine-readability and efficient provenance tracking.
  • Foundation laid for improved data quality and interoperability.

Conclusions:

  • The developed models facilitate clinical actionability of genomic data.
  • Improved data quality, interoperability, and re-usability are supported.
  • Addresses technological challenges in genomic data management for clinical use.