Rare disease diagnosis can be a complex process. Patients may undergo multiple tests, specialist consultations, and repeated evaluations before the underlying genetic cause is identified. While sequencing generates valuable genomic data, interpreting that data efficiently remains a major challenge.
Modern rare disease genomics workflows combine NGS analysis, phenotype information, variant interpretation, and AI-assisted evidence review to help clinical teams identify potentially relevant variants more efficiently. Research has also shown that genome sequencing can identify disease-causing variants that may remain undetected through earlier testing approaches.
ClairLabs’ Impactomics approach supports this workflow through automated NGS secondary analysis, phenotype-based gene prioritization, variant ranking, and ACMG-based classification. The platform can also incorporate family-based analysis to evaluate inheritance patterns and help analysts focus on the most clinically relevant candidates.
A key component is phenotype-first analysis. Instead of beginning only with a large list of genomic variants, clinical symptoms can be mapped to established phenotype and disease resources to prioritize relevant genes. Evidence from genomic databases and scientific literature can then support variant interpretation.
For laboratories, diagnostics organizations, and research teams, these capabilities can help create more consistent and traceable genomic interpretation workflows. By connecting sequencing data with clinical phenotypes and evidence, AI-enabled platforms can help move rare disease diagnosis toward faster and more informed clinical decision-making.
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