The transition from raw genomic data to actionable clinical insights is the most complex stage of precision medicine. Genome interpretation involves synthesizing vast amounts of biological evidence to determine the clinical significance of a patient's genetic profile.
Genomic data interpretation is the process of evaluating genetic variants identified during sequencing to assess their role in disease, treatment response, or hereditary risk. This process requires a multi-disciplinary approach, combining bioinformatics, clinical genetics, and functional biology.
As the cost of sequencing continues to drop, the volume of data generated per patient has exploded. The challenge for modern laboratories is no longer generating the data—it's scaling the clinical genome interpretation workflow to handle thousands of samples with high diagnostic rigor.
Effective genome sequencing interpretation is not about looking at a single data point; it's about integrating multiple layers of evidence to build a defensible clinical case.
Curating peer-reviewed publications and clinical databases (like ClinVar) to find previously reported associations between a variant and a phenotype.
Utilizing resources like gnomAD to determine how common a variant is in the general population. Rarity is often a key indicator of pathogenic potential.
Leveraging in-silico algorithms to predict the functional impact of a variant on protein structure, splicing, or gene expression.
"A professional variant interpretation workflow must account for every line of evidence before a final classification is reached."
To ensure clinical consistency, professional societies have established standardized frameworks for variant classification.
Developed by the American College of Medical Genetics and Genomics, this framework uses a 5-tier classification system (Pathogenic to Benign) for inherited variants based on strength-of-evidence criteria (PVS1, PM1, etc.).
A joint effort by AMP, ASCO, and CAP, this framework categorizes variants in cancer based on their clinical evidence of therapeutic, prognostic, or diagnostic significance.
Manual interpretation of genomic data is no longer sustainable for modern laboratories. Professional genome interpretation software is essential for maintaining both speed and accuracy.
Reduction in Turnaround Time
Standardized scoring rigor
VSClinical automates the evidence-gathering process, pulling population frequencies, computational predictions, and literature matches directly into a guided ACMG/AMP scoring interface. This transforms the bioinformatician's role from data collector to clinical validator.
Explore VSClinicalThe VarSeq clinical workflow engine
Access curated data from OMIM, CADD, Mastermind, and HGMD directly in your interpretation workflow.
Turnaround time varies greatly depending on the complexity of the test. A targeted panel might take an hour, while a whole genome for a complex rare disease case could take days of manual review. Professional software like VSClinical can reduce this time by up to 80% through automated evidence gathering.
Variant calling is a computational process (secondary analysis) that identifies where a patient's genome differs from a reference. Variant interpretation (tertiary analysis) is the clinical process of determining which of those differences are medically significant.
Standardization prevents subjective interpretation. Without frameworks like the ACMG or AMP guidelines, different laboratories might classify the same variant differently, leading to inconsistent patient care and diagnostic results.
While much of the evidence-gathering and scoring can be automated, a "human-in-the-loop" approach is still the clinical gold standard. Professional software should surface the evidence and suggest a classification, which is then validated by a certified clinical geneticist or molecular pathologist.
Expert strategies for variant classification, guideline application, and scaling clinical genomics workflows.
Join leading diagnostic laboratories using Golden Helix to standardize interpretation and deliver guideline-driven genomic reports.