Predict functional effects of genetic variants using the AlphaGenome API.
npx skills add https://github.com/bigbio/hvantk --skill alphagenome
Please help me install this Agent Skill. Skill: alphagenome Source: https://github.com/bigbio/hvantk/tree/main/hvantk/skills/alphagenome Command: npx skills add https://github.com/bigbio/hvantk --skill alphagenome
This skill addresses the challenge of predicting the functional impact of genetic variants on molecular phenotypes, such as gene expression and chromatin accessibility, by automating the interaction with the AlphaGenome deep learning model.
Use the alphagenome skill to process your variant table by running the reprocess command with your specific configuration file path.
High-intent search queries and answers about installing and using this skill.
To predict functional effects of genetic variants on molecular phenotypes, you can use the alphagenome skill to automate AlphaGenome API calls and convert outputs into structured Hail Tables for analysis.
The best way to annotate thousands of variants for multiomics analysis is using an automated pipeline with batching, checkpointing, and retry logic to process VCF or TSV data into standardized annotation tables.
Yes, alphagenome works with Hail Tables by seamlessly converting AlphaGenome API outputs into structured Hail Tables, enabling immediate downstream multiomics analysis of variant functional scores.
To handle rate-limiting during large-scale variant predictions, the pipeline implements automated batching, checkpointing, and retry logic to ensure robust API interaction across large genomic datasets.
Yes, you can use TSV files as input for variant effect prediction; the skill integrates variant data from both TSV files and Hail Tables into standardized annotation tables for processing.
A limitation of using deep learning models for variant annotation is the need for automated retry logic to handle API rate-limiting, requiring robust pipelines to process large-scale genomic datasets effectively.
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