원클릭으로 Manus에서 모든 스킬 실행

alphagenome-variant-effects

스타31

포크8

업데이트2026년 6월 18일 18:53

Use Google DeepMind AlphaGenome to predict tissue-aware regulatory effects of DNA sequence variants across expression, splicing, chromatin, and contact-map outputs. Use when prioritizing noncoding variants, comparing reference and alternate alleles, visualizing predicted regulatory changes, or designing focused AlphaGenome API analyses.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

mdbabumiamssm

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

AlphaGenome Variant Effects

Use AlphaGenome for mechanistic prioritization of regulatory variants. Do not convert its predictions directly into clinical classifications.

Workflow

Confirm the biological question, supported species and reference assembly, coordinate convention, and normalized reference/alternate allele.
Select a sequence interval that contains the relevant regulatory context without exceeding the model limit of one million base pairs.
Select tissue or cell context with ontology terms. Document why each term is biologically relevant.
Request only the output modalities needed for the hypothesis, such as RNA expression, splicing, chromatin accessibility, histone marks, transcription-factor binding, or chromatin contacts.
Use predict_variant for reference-versus-alternate comparisons and preserve both outputs.
Derive interpretable summaries:
- direction and magnitude of predicted change;
- affected tracks, genes, splice junctions, or regulatory elements;
- consistency across related tissues and nearby windows.
Visualize reference and alternate tracks on the same axes and annotate the variant position.
Compare predictions with population frequency, ClinVar or disease evidence, QTLs, epigenomic tracks, and functional assays.

Scale and Access

Obtain an AlphaGenome API key and install the official client.
Use the API for focused small-to-medium analyses. The official documentation states it is not intended for analyses requiring more than roughly one million predictions.
Check the current terms before commercial use. The public API is offered for non-commercial use subject to Google DeepMind terms.
Use the repository's BioMCP integration only when MCP orchestration is useful; use the official client for direct control and reproducible notebooks.

Guardrails

Do not infer causality from a predicted track change alone.
Do not treat absent predicted effects as proof of benignity.
Separate model outputs from external annotations in reports.
Preserve API/client version, interval, ontology terms, requested outputs, and visualization settings.
Require orthogonal evidence for clinical, therapeutic, or genome-editing decisions.

Output Contract

Return a variant-centered report containing normalized input, biological context, requested modalities, reference-versus-alternate effects, supporting and conflicting evidence, limitations, and recommended validation.

Read references/operations.md for installation, a minimal API pattern, interpretation guidance, and canonical sources.

이 저장소의 다른 Skills

같은 저장소

medsam2-3d-segmentation

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate MedSAM2 for promptable segmentation of 3D medical images and medical videos, including CT lesion propagation, MRI volumes, RECIST-guided prompts, efficient CPU-oriented variants, training, and 3D Slicer integration. Use when generating or validating volumetric masks from sparse prompts or propagating masks through image slices or video frames.

2026-06-1831

monai-medical-imaging

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.

2026-06-1831

txgemma-therapeutics

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.

2026-06-1831

opencrispr-gene-editors

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.

2026-06-1831

transcriptformer-cell-embeddings

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.

2026-06-1831

protenix-structure-prediction

mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-

Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.

2026-06-1831

name	alphagenome-variant-effects
description	Use Google DeepMind AlphaGenome to predict tissue-aware regulatory effects of DNA sequence variants across expression, splicing, chromatin, and contact-map outputs. Use when prioritizing noncoding variants, comparing reference and alternate alleles, visualizing predicted regulatory changes, or designing focused AlphaGenome API analyses.

AlphaGenome Variant Effects

Use AlphaGenome for mechanistic prioritization of regulatory variants. Do not convert its predictions directly into clinical classifications.

Workflow

Confirm the biological question, supported species and reference assembly, coordinate convention, and normalized reference/alternate allele.
Select a sequence interval that contains the relevant regulatory context without exceeding the model limit of one million base pairs.
Select tissue or cell context with ontology terms. Document why each term is biologically relevant.
Request only the output modalities needed for the hypothesis, such as RNA expression, splicing, chromatin accessibility, histone marks, transcription-factor binding, or chromatin contacts.
Use predict_variant for reference-versus-alternate comparisons and preserve both outputs.
Derive interpretable summaries:
- direction and magnitude of predicted change;
- affected tracks, genes, splice junctions, or regulatory elements;
- consistency across related tissues and nearby windows.
Visualize reference and alternate tracks on the same axes and annotate the variant position.
Compare predictions with population frequency, ClinVar or disease evidence, QTLs, epigenomic tracks, and functional assays.

Scale and Access

Obtain an AlphaGenome API key and install the official client.
Use the API for focused small-to-medium analyses. The official documentation states it is not intended for analyses requiring more than roughly one million predictions.
Check the current terms before commercial use. The public API is offered for non-commercial use subject to Google DeepMind terms.
Use the repository's BioMCP integration only when MCP orchestration is useful; use the official client for direct control and reproducible notebooks.

Guardrails

Do not infer causality from a predicted track change alone.
Do not treat absent predicted effects as proof of benignity.
Separate model outputs from external annotations in reports.
Preserve API/client version, interval, ontology terms, requested outputs, and visualization settings.
Require orthogonal evidence for clinical, therapeutic, or genome-editing decisions.

Output Contract

Read references/operations.md for installation, a minimal API pattern, interpretation guidance, and canonical sources.