Alphagenome Atlas Website Links
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Data and AI
- Apache-2.0
- Low risk
- Has scripts
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
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google-deepmind/alphagenome-atlas-website-links/downloadUnzip 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.pyto 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 toentityfor genes/variants andlocusfor coordinate intervals. Usevariantfor variant queries. (Allowed:locus,entity,variant,motifs).i(string, Optional): Centered viewport zoom interval in 1-based closedchr:start-endformat (e.g.chr11:5289310-5289690). Required for automatic motif rendering.f(string, Optional): Comma-separated filter predicates inKEY:VALUEformat (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 ofScoreIdtokens for the/atlas/track-predictionspage 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
qmust strictly usechr:pos_1_based:ref>altformat (e.g.chr7:27170000:A>Gor URL-encodedchr7:27170000: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:
BiosampleGroup (BIOSAMPLE_NAME,BIOSAMPLE_TYPE): Evaluated with AND logic.AssayGroup (SCORER_MODALITY,ASSAY_TRANSCRIPTOR_FACTOR,ASSAY_HISTONE_MARK): Evaluated with OR logic.GeneGroup (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): IncludingaviinlItemsrenders 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 (%20for spaces) in raw URL query strings (or unencoded when passed throughurllib.parse.urlencode).<StrandNumber>:1(STRAND_POSITIVE),2(STRAND_NEGATIVE),3(STRAND_UNSTRANDED).<ScorerShortName>:RNA_SEQ,CHIP_TF,DNASE,ATAC,CAGE,PROCAP,CHIP_HISTONE(orRAW_SCORES-only modalities:SPLICE_SITES,SPLICE_SITE_USAGE,SPLICE_JUNCTIONS,POLYADENYLATION,CONTACT_MAPS).<Source>: Heatmap tileset source identifier:ACTIVE_ISM(activity-weighted_TIMES_ACTIVEmotif 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), orREF_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
Append entries of the form
pinned:<TrackMetadataName>:<StrandNumber>:<ScorerShortName>:<Source>(usingACTIVE_ISMfor activity-weighted motifs orRAW_SCORESfor raw ISM motifs) directly tolItemsfor the specific target tracks only.Set viewport interval
ito base-pair resolution ($\ge 2\text{ px/bp}$, window $\le 380\text{ bp}$).Configure
fwith 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-predictionsVisualizations: 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.
# 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_TFSupported CLI Options for track-predictions
--variant,-v(string, default:None): Variant string inchr:pos_1_based:ref>altformat.--gene,-g(string, default:None): Target gene symbol (boundsi=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 inchr:start-endformat.--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-predictionsdeep-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: Omittingscores=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-predictionsautomatically boundsi=to the target gene or requested interval.
Files
- ATTRIBUTION.mdAdded by AutoGineer944 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md12.3 KiB
- pyproject.toml215 B
- scripts/alphagenome_atlas_links.pyScript36.0 KiB
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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
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Needs: Needs Python
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Attribution and licence
- Published by
- Google DeepMind
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- Google DeepMind (Official)
- Source
- google-deepmind/science-skills · skills/alphagenome_atlas_website_links · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
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