What problem does it solve? Prioritizing and mechanistically interpreting non-coding, regulatory, and splicing variants from VCFs or credible sets requires running large sequence-to-function models; this Skill provides direct access to DeepMind's AlphaGenome Atlas (precomputed effects for ~9 billion GRCh38 SNVs) and the on-demand AlphaGenome model without writing client code. ## Core Features & Use Cases - Atlas AVI Ranking: Look up the AlphaGenome Variant Impact (AVI) score, Phred-scaled genome-wide percentile, and 18 SHAP feature attributions for any GRCh38 SNV, VCF, or window up to ~1 kb. - Track-Level Mechanism Resolution: Retrieve raw and quantile scores across RNA-seq, DNase, ATAC, ChIP-TF, ChIP-histone, CAGE, PRO-cap, splicing, polyadenylation, and contact-map tracks filtered by tissue ontology CURIE or gene. - On-Demand Model Scoring: Score indels, mouse (mm10) variants, custom windows, or run in silico mutagenesis and REF-versus-ALT track prediction with the AlphaGenome model. - Portal Deep Links: Build AlphaGenome Atlas website URLs for any variant, locus, or gene so readers can inspect prediction tracks. - Use Case: Given a GWAS credible set in a VCF, rank every variant by AVI Phred, identify whether splicing or TF-binding drives the top hit, pull colon RNA-seq scores for the lead variant, and attach a portal link to the report. ## Quick Start Ask the AI to look up the AlphaGenome Atlas AVI score and feature attributions for the variant chr22:36201698:A>C and explain which molecular mechanism drives it.