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Precision medicine requires precise knowledge of the patient's disease.

Currently, more than 6,000 single gene disorders are listed in the OMIM (Online Mendelian Inheritance in Man,  Amberger et al., 2019) catalogue of human genetic disorders. Most of them are very rare, affecting fewer than 100 patients in Germany, and only few physicians are aware of them. So even finding the correct diagnosis in patients with rare disorders is cumbersome and takes more than 5 years on average (https://www.uniklinika.de/themen-die-bewegen/seltene-erkrankungen-waisen-der-medizin/seltene-erkrankungen-in-zahlen-fakten/). Detailed recording of patients’ clinical signs provides a concise description of their phenotypes without having to know the name of the disease. Using clinical signs and symptoms from the Human Phenotype Ontology (Robinson, PN. et al., 2008) and diagnoses from OMIM (Amberger et al., 2019) and the rare disease database Orphanet (Maiella et al., 2018), we have developed the deep phenotyping tool SAMS (Symptom Annotation Made Simple, https://www.genecascade.org/SAMS/) (Steinhaus, R. et al., 2022). SAMS allows physicians to enter clinical signs in a user-friendly web interface and record their presence or absence over time. It also suggests a diagnosis based on the patient's signs  and symptoms. Phenotyping data can be exported as GA4GH (Global Alliance for Genomics and Health) Phenopackets (Jacobsen, JO et al., 2022) and easily shared with collaborators. 

By implementing deep phenotyping across the CADS cohorts, our goal is to thoroughly characterise patients not only on the DNA but also on the phenotype level. Additionally, the system will allow matching patients based on shared clinical signs and provide an aggregated overview of all patients included in CADS. We will make this publicly available to foster collaborations between groups working on similar disorders and eventually between patients suffering from similar disorders.

Participating Clinics and Infrastructures

Project lead

  • Dominik Seelow

    Prof. Dr. Dominik Seelow

    Professor of Bioinformatics and Translational Genetics

    Contact information
    Address:Postal address: Charitéplatz 1, 10117 Berlin
    Office: Platz vor dem Neuen Tor 4, 10115 Berlin

    Phone:+49 30 450 543684
    E-mail:dominik.seelow@bih-charite.de
  • Janina Schönberger

    PhD-Student

    Contact information
    Address:Bioinformatik und translationale Genetik
    Charitéplatz 1, 10117 Berlin


    Phone:+49 30 450 543 698
    E-mail:janina.schoenberger@bih-charite.de