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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics.

Sebastian Köhler1,2,3, N Christine Øien4, Orion J Buske5

  • 1Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.

Current Protocols in Human Genetics
|September 4, 2019
PubMed
Summary

The Human Phenotype Ontology (HPO) aids rare disease diagnosis by standardizing clinical abnormalities. This enables computational comparison of patient phenotypes to HPO disease profiles for differential diagnosis.

Keywords:
HPOHuman Phenotype Ontologydifferential diagnosisexomephenotype

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Area of Science:

  • Medical Informatics
  • Genetics
  • Computational Biology

Background:

  • The Human Phenotype Ontology (HPO) provides a standardized vocabulary for describing human phenotypic abnormalities.
  • HPO is crucial for computational analysis in diagnosing rare genetic diseases.
  • Accurate phenotypic descriptions are essential for identifying potential genetic disorders.

Purpose of the Study:

  • To guide the selection and input of optimal Human Phenotype Ontology (HPO) terms for computational differential diagnosis.
  • To demonstrate the application of Phenomizer and Exomiser tools for generating differential diagnoses.
  • To enhance the utility of HPO in rare disease diagnostics.

Main Methods:

  • Utilizing the Human Phenotype Ontology (HPO) to create standardized phenotypic profiles for individuals.
  • Comparing patient HPO profiles against the HPO database for disease similarity.
  • Coupling phenotypic analysis with whole-exome or whole-genome sequencing data.
  • Employing software like PhenoTips, PatientArchive, Phenomizer, and Exomiser for data entry and analysis.

Main Results:

  • Demonstration of a workflow for selecting and entering HPO terms.
  • Successful generation of computational differential diagnoses using Phenomizer and Exomiser.
  • Highlighting the integration of HPO with genomic data analysis.

Conclusions:

  • Optimal selection and input of HPO terms are critical for accurate computational differential diagnosis.
  • Phenomizer and Exomiser are effective tools for leveraging HPO data in clinical settings.
  • HPO facilitates the identification of rare genetic diseases through computational phenotype analysis.