TADA

TADA prioritizes pathogenic copy number variants (CNVs) using functional annotations and classifier-derived calibrated pathogenicity scores to distinguish benign from potentially harmful CNVs.

Key Features:

• Functional annotation integration: Integrates an extensive catalogue of functional annotations for CNVs.
• Manual filtering: Supports manual filtering of CNVs alongside automated methods.
• Automated classification: Implements automated classification to assign pathogenicity labels to CNVs.
• Enrichment analysis: Performs enrichment analysis of functional annotations to evaluate annotation relevance.
• Classifier construction: Constructs classifiers to predict CNV pathogenicity.
• Performance and calibration: Produces well-calibrated pathogenicity scores and has been demonstrated to outperform alternative methods in accuracy and reliability.

Scientific Applications:

• Clinical diagnostics: Aids clinical diagnostics by prioritizing CNVs based on functional annotations and pathogenicity scores.
• Mechanistic research: Enables investigation of mechanisms underlying the disease impact of larger genomic alterations by linking CNVs to functional annotations.
• Genetic disorder research: Supports research into genetic disorders by helping to elucidate the roles of specific CNVs in disease etiology.

Methodology:

Integrates a catalogue of functional annotations, performs enrichment analysis, applies manual filtering and automated classification, and constructs classifiers that output calibrated pathogenicity scores for CNVs.