SUsPECT

SUsPECT predicts the impact of genetic variants on custom long-read transcriptomes to improve clinical variant annotation by revealing effects on novel transcripts and open reading frames.

Key Features:

• Custom long-read transcriptome support: Operates on transcript sets generated by long-read RNA-sequencing.
• Ensembl VEP integration: Builds upon the Ensembl Variant Effect Predictor (VEP) framework to predict variant consequences on custom transcripts.
• Novel open reading frame analysis: Considers novel open reading frames absent from Ensembl/GENCODE and RefSeq when assessing variant impact.
• Missense variant evaluation: Evaluates missense variants within custom transcript contexts and predicts their functional consequences.
• Deleteriousness scoring: Assigns deleteriousness scores to variants in the context of custom transcripts.
• ClinVar reannotation capability: Identifies potential mutational mechanisms for ClinVar variants that appear non-pathogenic when annotated with standard references.
• Context-specific immune analysis: Reveals immune-related variants with more severe molecular consequences when analyzed against a transcriptome from stimulated immune cells.
• Variant prioritization: Produces annotations to prioritize potentially disease-causing variants across diseases based on predicted molecular consequences.

Scientific Applications:

• Clinical variant annotation: Improves clinical variant annotation by incorporating custom long-read transcriptomes into consequence prediction.
• Variant reinterpretation: Reinterprets ClinVar and other catalogued variants to uncover missed pathogenic mechanisms.
• Context-specific pathogenicity assessment: Detects condition-specific transcript effects, exemplified by stimulated immune cell transcriptomes, to refine molecular consequence estimates.
• Research and diagnostic prioritization: Prioritizes candidate variants for genetic research and clinical diagnostics based on predicted effects on novel transcripts and ORFs.

Methodology:

Uses the Ensembl Variant Effect Predictor (VEP) on custom transcript sets generated from long-read RNA-sequencing to predict variant consequences, evaluate missense variants, and assign deleteriousness scores including effects on novel open reading frames.