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Updated: Feb 1, 2026

A Web-Based Workflow for Selecting Gene- and Tissue-Specific Enhancers
Published on: July 18, 2025
Development and user evaluation of a rare disease gene prioritization workflow based on cognitive ergonomics
Jessica J Y Lee1, Clara D M van Karnebeek1,2,3, Wyeth W Wasserman1,4
1Centre for Molecular Medicine and Therapeutics, BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
Objective:
The clinical diagnosis of genetic disorders is undergoing transformation, driven by whole exome sequencing and whole genome sequencing (WES/WGS). However, such nucleotide-level resolution technologies create an interpretive challenge. Prior literature suggests that clinicians may employ characteristic cognitive processes during WES/WGS investigations to identify disruptions in genes causal for the observed disease. Based on cognitive ergonomics, we designed and evaluated a gene prioritization workflow that supported these cognitive processes.
Materials And Methods:
We designed a novel workflow in which clinicians recalled known genetic diseases with similarity to patient phenotypes to inform WES/WGS data interpretation. This prototype-based workflow was evaluated against the common computational approach based on physician-specified sets of individual patient phenotypes. The evaluation was conducted as a web-based user study, in which 18 clinicians analyzed 2 simulated patient scenarios using a randomly assigned workflow. Data analysis compared the 2 workflows with respect to accuracy and efficiency in diagnostic interpretation, efficacy in collecting detailed phenotypic information, and user satisfaction.
Results:
Participants interpreted genetic diagnoses faster using prototype-based workflows. The 2 workflows did not differ in other evaluated aspects.
Discussion:
The user study findings indicate that prototype-based approaches, which are designed to model experts' cognitive processes, can expedite gene prioritization and provide utility in synergy with common phenotype-driven variant/gene prioritization approaches. However, further research of the extent of this effect across diverse genetic diseases is required.
Conclusion:
The findings demonstrate potential for prototype-based phenotype description to accelerate computer-assisted variant/gene prioritization through complementation of skills and knowledge of clinical experts via human-computer interaction.
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