Back

GenPK Suite: An Integrated Digital Platform for Phenotypic Data Collection, 3D Facial Imaging, and Research Workflow Management in Rare Diseases

Akbar, S. F.; Rashid, A.; Anwar, I.; Reymond, A.; Santoni, F.; Ansar, M.

2025-11-22 genetic and genomic medicine
10.1101/2025.11.18.25340411 medRxiv
Show abstract

Accurate and secure collection of genetic samples and associated phenotypic data is essential for advancing rare disease research, yet existing workflows often remain fragmented across paper records, electronic questionnaires, and laboratory information systems. To address these limitations, we developed the GenPK Suite, an offline-capable digital platform that integrates family-wise recruitment, disorder-specific questionnaires, digital consent, pedigree capture, barcoded biospecimen tracking, and three-dimensional (3D) facial imaging. The system was deployed in field settings in Pakistan. During pilot implementation, the platform enabled the recruitment of 121 families encompassing more than 150 individuals, resulting in 150 barcoded biospecimens and 50 high-resolution 3D craniofacial scans. Data completeness across mandatory fields exceeded 90%, while offline-to-cloud synchronization succeeded in >95% of encounters within 24 hours. Laboratory accession confirmed end-to-end traceability, with DNA quality and quantity metrics returned for samples. User feedback highlighted reduced paperwork burden and greater procedural consistency, with staff reporting fewer transcription mistakes and fewer manual linkage steps compared with their previous paper-based workflow. By embedding technical safeguards aligned with ISO/IEC 27001 and 27701 and enforcing role-based access, the GenPK Suite demonstrated secure and practical feasibility for international rare disease research. These results show that integrated digital infrastructures can enable scalable recruitment and phenotyping across both high-resource and remote field environments.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.