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Alphagenome Atlas Website Links

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Data and AI
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based VCF-style coordinates), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use when 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. Don't use for computing raw AlphaGenome variant predictions directly without Atlas URLs (use alphagenome-single-variant-analysis or alphagenome-variant-impact-score).
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The AlphaGenome Atlas Deep-Linking skill provides a standardized method for generating precise URLs to the AlphaGenome Atlas web application. It enables users to create deep-links for single-variant exploration, genomic interval views, and comparative track predictions between reference and alternate alleles. The skill includes a Python script to automate the construction of these links, ensuring that complex query parameters—such as multi-modality filters, layout configurations, and pinned tracks—are correctly formatted to prevent performance issues and ensure accurate data visualization.

What it does

  • Generates deep-links for single-variant exploration using 1-based VCF-style coordinates.
  • Constructs URLs for genomic locus views and interval-based data exploration.
  • Creates comparative track prediction links for reference vs. alternate allele analysis.
  • Formats candidate variant records into tables with embedded clickable links.
  • Automates the application of complex URL query parameters for filtering and layout.
  • Ensures mandatory co-plotting of RNA-seq and splicing tracks for variant impact assessment.

When to use it

  • When presenting, discussing, or scoring genetic variants in the AlphaGenome Atlas.
  • When you need to visualize specific genomic loci or variant impacts in a web browser.
  • When comparing functional profiles between reference and alternate alleles.
  • When creating reports or tables that require direct navigation to specific genomic data points.
  • When you need to apply specific filters for biosamples, modalities, or transcription factors.

Advantages

  • Prevents manual URL construction errors and invalid query parameter formatting.
  • Ensures consistent and performant URL generation by automatically bounding genomic intervals.
  • Simplifies the inclusion of complex multi-modality filters and layout configurations.
  • Promotes best practices by enforcing the co-plotting of related data tracks.
  • Provides a programmatic interface for integrating Atlas links into larger analysis workflows.

Usage examples

“Generate a link to explore the variant chr9:128225994:G>A in the K562 cell line with RNA-seq and DNase data.”
The agent uses the script to generate a URL with the correct variant coordinates, biosample, and modality filters applied.
“Create a comparison link for variant chr15:42387805:C>G in the CAPN3 gene for Muscle_Skeletal tissue.”
The agent provides a link to the track-predictions page, correctly configured for the variant, gene, and tissue, including necessary splicing and RNA-seq tracks.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google-deepmind/alphagenome-atlas-website-links/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip alphagenome-atlas-website-links.zip -d .agents/skills

Documentation by Google DeepMind

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

# 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 sequence tracks (ACTIVE_ISM or RAW_SCORES, not REF_SCORES) at base-pair resolution. General section heatmaps do not trigger motif footprint rendering.

Pinned Track Key Schema
pinned:<TrackMetadataName>:<StrandNumber>:<ScorerShortName>:<Source>

Example lItems entry in a raw URL query string:

pinned:EFO:0002067%20TF%20ChIP-seq%20GATA1:3:CHIP_TF:ACTIVE_ISM
  • <TrackMetadataName>: Exact track name from metadata proto (e.g. EFO:0002067 TF ChIP-seq GATA1), percent-encoded (%20 for spaces) in raw URL query strings (or unencoded when passed through urllib.parse.urlencode).

  • <StrandNumber>: 1 (STRAND_POSITIVE), 2 (STRAND_NEGATIVE), 3 (STRAND_UNSTRANDED).

  • <ScorerShortName>: RNA_SEQ, CHIP_TF, DNASE, ATAC, CAGE, PROCAP, CHIP_HISTONE (or RAW_SCORES-only modalities: SPLICE_SITES, SPLICE_SITE_USAGE, SPLICE_JUNCTIONS, POLYADENYLATION, CONTACT_MAPS).

  • <Source>: Heatmap tileset source identifier: ACTIVE_ISM (activity-weighted _TIMES_ACTIVE motif footprints and CWM sequence logos), RAW_SCORES (raw ISM variant-score tracks, also rendering raw ISM motif footprints and CWM sequence logos across all modalities), or REF_SCORES (continuous reference signal line charts without sequence letters or motifs). Calibrated and AVI tracks cannot be pinned, and legacy :heatmap:...:<TilesetId> suffixes fail track lookup and are ignored.

Recipe for Automatic Motif Display on Load
  1. Append entries of the form pinned:<TrackMetadataName>:<StrandNumber>:<ScorerShortName>:<Source> (using ACTIVE_ISM for activity-weighted motifs or RAW_SCORES for raw ISM motifs) directly to lItems for the specific target tracks only.

  2. Set viewport interval i to base-pair resolution ($\ge 2\text{ px/bp}$, 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).

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.

# 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.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Google DeepMind
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Google DeepMind (Official)
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google-deepmind/science-skills · skills/alphagenome_atlas_website_links · commit 4de6786
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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