alphagenome-single-variant-analysis

You can predict the impact of a single genetic variant on gene expression and splicing by using AlphaGenome to generate REF versus ALT comparisons for regulatory modalities. This skill analyzes chromatin accessibility, transcription factor binding, and splicing effects for your specific variant.

In-silico mutagenesis for variant interpretation is a technique used to identify likely disrupted transcription factor motifs at a variant locus. This skill applies ISM using AlphaGenome predictions to diagnose regulatory motif disruptions caused by your genetic variant.

To analyze a non-coding variant across disease-relevant tissues, you can run a discovery scan with AlphaGenome and apply ontology-aware tissue resolution. This skill ranks disease-relevant tissues using UBERON and CL ontology terms to guide your final interpretation.

Yes, you need an AlphaGenome API key configured in a .env file to run genetic variant analysis. The skill uses this key to access AlphaGenome predictions and requires uv to run Python code with its dependencies, including pandas and scipy.

The best way to resolve tissue and cell-type ontologies for chromatin accessibility predictions is to use ontology-aware resolution within AlphaGenome analysis. This skill parses UBERON and CL terms to accurately map predictions to specific tissues and cell types for variant interpretation.

No, you cannot use external gene lookup APIs with AlphaGenome variant analysis because this skill explicitly avoids them. You must follow the built-in report template workflow to generate interpretable plots and biological mechanism reports without external gene lookup dependencies.