Alphagenome Atlas Website Links logo

Alphagenome Atlas Website Links

OrganizationPopular
google-deepmind
alphagenome-atlas-website-links

Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based chr:pos:ref>alt), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use whenever visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant.

Overview

Publishergoogle-deepmind
Repositoryscience-skills
Skill namealphagenome-atlas-website-links
Stars
3.1K
Forks
349
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google-deepmind on GitHub. Read the source before you install it.

Installation

Install the Alphagenome Atlas Website Links AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google-deepmind/science-skills.git /tmp/science-skills
mkdir -p .claude/skills
cp -r /tmp/science-skills/skills/alphagenome_atlas_website_links .claude/skills/alphagenome-atlas-website-links
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alphagenome Atlas Website Links in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Alphagenome Atlas Website Links on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Alphagenome Atlas Website Links is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AlphaGenome Atlas Deep-Linking & URL Configuration

Construct and validate deep-links for the AlphaGenome Atlas web application (https://deepmind.google.com/science/alphagenome/atlas).

Base URL: https://deepmind.google.com/science/alphagenome/atlas

[!IMPORTANT] Mandatory Atlas Deep-Linking with Variant Scores: Whenever presenting, discussing, or scoring genetic variants, you MUST always provide clickable deep-links to the AlphaGenome Atlas. Use scripts/alphagenome_atlas_links.py to automate link and table generation.


Prerequisites

bash
# 1. Single Variant Exploration Link:
uv run scripts/alphagenome_atlas_links.py variant "chr9:128225994:G>A" \
  --biosample K562 \
  --modalities RNA_SEQ,DNASE,CHIP_TF

# 2. Genomic Locus / Interval Link:
uv run scripts/alphagenome_atlas_links.py locus "chr11:5288500-5290500" \
  --biosample K562 \
  --modalities RNA_SEQ,DNASE,CHIP_TF

# 3. Format Candidate Variant Records Table (with embedded clickable links):
uv run scripts/alphagenome_atlas_links.py table --input top_variants.json --biosample K562

# 4. Construct Ref vs. Alt Track Predictions Link (/atlas/track-predictions):
uv run scripts/alphagenome_atlas_links.py track-predictions \
  --variant "chr15:42387805:C>G" \
  --gene CAPN3 \
  --biosample "Muscle_Skeletal"

2. URL Query Parameters

  • q (string, Required): Primary search target. Supports 1-based closed intervals (chr11:5288500-5290500), gene symbols (BRCA1), Ensembl IDs (ENSG00000012048), or 1-based variants (chr7:27170000:A>G).
  • m (enum, Optional): View mode. Defaults to entity for genes/variants and locus for coordinate intervals. Use variant for variant queries. (Allowed: locus, entity, variant, motifs).
  • i (string, Optional): Centered viewport zoom interval in 1-based closed chr:start-end format (e.g. chr11:5289310-5289690). Required for automatic motif rendering.
  • f (string, Optional): Comma-separated filter predicates in KEY:VALUE format (e.g. BIOSAMPLE_NAME:K562,SCORER_MODALITY:RNA-seq,ASSAY_TRANSCRIPTOR_FACTOR:GATA1). Controls visible heatmap rows.
  • lItems (string, Optional): Layout item sequence, AVI score track toggle (avi), section heatmaps, and pinned tracks list (e.g. avi,section:RNA_SEQ,section:DNASE,pinned:<TrackKey>).
  • scores (string, Optional): Comma-separated list of ScoreId tokens for the /atlas/track-predictions page comparison (e.g. <ScoreId1>,<ScoreId2>).
  • md (enum, Optional): Active modality tab selector on the track predictions view (RNA_SEQ, SPLICE_JUNCTIONS, SPLICE_SITE_USAGE, DNASE).
  • tpRenames (string, Optional): Custom title overrides for specific score predictions (ScoreId:CustomTitle).
  • tpLegendTitle (string, Optional): Custom legend title for the track predictions chart card (e.g. Predicted Gene Expression).

[!IMPORTANT] Variant Query Format: Variants in q must strictly use chr:pos_1_based:ref>alt format (e.g. chr7:27170000:A>G or URL-encoded chr7:27170000:A%3EG, where the position is 1-based). Do not use colon-separated alleles (A:G) or dbSNP rsIDs (rsIDs are unsupported).


3. Multi-Modality Filtering & The Assay Group Gotcha (f)

Filter Groups & Boolean Evaluation

Filters in f map to three primary evaluation groups:

  • Biosample Group (BIOSAMPLE_NAME, BIOSAMPLE_TYPE): Evaluated with AND logic.
  • Assay Group (SCORER_MODALITY, ASSAY_TRANSCRIPTOR_FACTOR, ASSAY_HISTONE_MARK): Evaluated with OR logic.
  • Gene Group (GENE_NAME): Evaluated with OR logic.

⚠️ Mandatory Multi-Modality Filter Rule

RNA-seq and DNase tracks have no transcription factor code (transcriptionFactorCode === ""). If f contains only ASSAY_TRANSCRIPTOR_FACTOR filters under the Assay group, RNA-seq and DNase tracks fail the Assay evaluation and are hidden from the heatmap.

To display RNA-seq and DNase tracks alongside specific ChIP-seq transcription factors, explicitly include SCORER_MODALITY:RNA-seq and SCORER_MODALITY:DNase in f (handled automatically by scripts/alphagenome_atlas_links.py):

f=BIOSAMPLE_NAME:<CellLine>,SCORER_MODALITY:RNA-seq,SCORER_MODALITY:DNase,ASSAY_TRANSCRIPTOR_FACTOR:<TF1>,ASSAY_TRANSCRIPTOR_FACTOR:<TF2>

4. Layout Configuration, AVI Scores, & Pinned Tracks (lItems)

Plotting AVI Scores and Modality Sections

  • AVI Variant Impact Track (avi): Including avi in lItems renders the top-level AlphaGenome Variant Impact score track for the interval or variant.
  • Database Modality Sections (section:<MODALITY>): Sections render full unpinned heatmaps across all matching tracks for that modality (e.g. section:RNA_SEQ, section:DNASE, section:CHIP_TF, section:ATAC, section:CAGE).

Pinned Tracks & Motif Instances

[!NOTE] Track-Specific Motif Guideline: Pinned Active-ISM tracks with motif instances and Contribution Weight Matrix (CWM) logos should only be added when specifically requested for individual tracks. Only a limited subset of tracks (such as key ChIP-TF or RNA-seq tracks relevant to the locus) support and benefit from pinned motif overlays. For standard exploration links, default section heatmaps (avi,section:RNA_SEQ,section:DNASE,section:CHIP_TF) without pinned tracks are preferred.

Motif instances and CWM logos render exclusively on pinned tracks at base-pair resolution. General section heatmaps do not trigger motif footprint rendering.

Pinned Track Key Schema

pinned:<TrackMetadataName>:<StrandNumber>:<ScorerShortName>:heatmap:HEATMAP_TILESET_SOURCE_ACTIVE_ISM_SCORES:<TilesetId>
  • <TrackMetadataName>: Exact track name from production metadata proto, URL-encoded (%20 for spaces).
  • <StrandNumber>: 1 (STRAND_POSITIVE), 2 (STRAND_NEGATIVE), 3 (STRAND_UNSTRANDED).
  • <ScorerShortName>: RNA_SEQ, CHIP_TF, DNASE, ATAC, CAGE, PROCAP, CHIP_HISTONE.
  • HEATMAP_TILESET_SOURCE_ACTIVE_ISM_SCORES: Required source identifier for Active-ISM motif layers.
  • <TilesetId>: Server-assigned tileset identifier (17354278441953531756 for current production).

Recipe for Automatic Motif Display on Load

  1. Append pinned:<PinnedKey> entries to lItems for the specific target tracks only.
  2. Set viewport interval i to base-pair resolution ($\le 1\text{ bp/px}$, window $\le 380\text{ bp}$).
  3. Configure f with cell line and transcription factors.

5. Track Predictions & Ref vs. Alt Comparisons (/atlas/track-predictions)

The dedicated /atlas/track-predictions page compares predicted functional profiles between the Reference and Alternate alleles for selected scores across genomic windows:

  • Route: https://deepmind.google.com/science/alphagenome/atlas/track-predictions
  • Visualizations: Expanded line plots (expression, chromatin accessibility, TF binding) and Sashimi arc charts (splice junctions).

Automated Prediction Link Generation (scripts/alphagenome_atlas_links.py track-predictions)

Always construct track prediction URLs using scripts/alphagenome_atlas_links.py track-predictions. Manual ScoreId string formatting is error-prone due to donor/acceptor skipping coordinates, strand orientation (+/-), and genic vs. non-genic suffix rules. The script automatically handles coordinate extraction from GENCODE v46, track catalog resolution, and URL synthesis.

bash
# Variant & Gene:
uv run scripts/alphagenome_atlas_links.py track-predictions \
  --variant "chr15:42387805:C>G" \
  --gene CAPN3 \
  --biosample "Muscle_Skeletal" \
  --modalities SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TF \
  --tf CTCF

# Interval/Locus query:
uv run scripts/alphagenome_atlas_links.py track-predictions \
  --variant "chr15:42387805:C>G" \
  --interval "chr15:41869312-42917888" \
  --biosample "Muscle_Skeletal" \
  --modalities SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TF

Supported CLI Options for track-predictions

  • --variant, -v (string, default: None): Variant string in chr:pos_1_based:ref>alt format.
  • --gene, -g (string, default: None): Target gene symbol (bounds i= viewport and computes splice junctions).
  • --gene_id (string, default: None): Target Ensembl gene ID (e.g. ENSG00000092529.26).
  • --interval, -i (string, default: None): Genomic interval viewport in chr:start-end format.
  • --biosample, -b (string, default: Muscle_Skeletal): Target biosample or tissue query (e.g. Muscle_Skeletal, K562, Whole_Blood).
  • --modalities, -m (string, default: SPLICE_JUNCTIONS,RNA_SEQ,DNASE,CHIP_TF): Comma-separated list of modalities (SPLICE_JUNCTIONS, RNA_SEQ, DNASE, ATAC, CHIP_TF).
  • --tf (string, default: CTCF): Transcription factor name for ChIP-TF tracks (e.g. CTCF, GATA1).
  • --rename (string, default: None): Custom track rename overrides in the chart card.
  • --legend_title (string, default: None): Custom legend header for the chart card.
  • --format (enum, default: table): Output format (table, url, json).

[!IMPORTANT] Mandatory Splicing & RNA-seq Co-Plotting Rule: When generating /atlas/track-predictions deep-links, plotting, or visualizing variant impact data for splicing variants, always plot continuous RNA-seq expression alongside splicing tracks (SPLICE_JUNCTIONS, SPLICE_SITE_USAGE, SPLICE_SITES). Splicing mutations frequently activate cryptic splice junctions and trigger nonsense-mediated decay (NMD) or alter total transcript output; assessing splice junctions (sashimi arcs) together with continuous RNA-seq read coverage is required to observe both the structural splice defect and the resulting change in overall transcript abundance.

[!IMPORTANT] Always Provide Bounded i= in Track Prediction URLs: Omitting scores= or leaving the genomic interval (i=) unbounded causes the web application to attempt querying all matching tracks across the broader locus, leading to severe latency or page hanging. alphagenome_atlas_links.py track-predictions automatically bounds i= to the target gene or requested interval.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Alphagenome Atlas Website Links AI skill do?

Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based chr:pos:ref>alt), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use whenever visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant.

Why use Alphagenome Atlas Website Links on TypingMind?

Because you install it once and use it with any model. Alphagenome Atlas Website Links is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Alphagenome Atlas Website Links in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google-deepmind/science-skills/tree/main/skills/alphagenome_atlas_website_links. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Alphagenome Atlas Website Links?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Alphagenome Atlas Website Links?

As many as you like. As long as a model supports skills, you can use Alphagenome Atlas Website Links with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Alphagenome Atlas Website Links AI skill free?

Yes. It is published on GitHub by google-deepmind under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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