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.
How do I install this agent skill?
npx skills add https://github.com/google-deepmind/science-skills --skill alphagenome-atlas-website-linksIs this agent skill safe to install?
- Gen Agent Trust Hubpass
The skill constructs deep-links and track prediction URLs for the AlphaGenome Atlas. It includes a utility script that resolves genomic coordinates by fetching metadata from a trusted Google storage bucket and integrates with the AlphaGenome API. The skill uses secure practices for API key management and standard URL validation.
- Socketpass
No alerts
- Snykpass
Risk: LOW · No issues
What does this agent skill do?
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 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 (%20for 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 (17354278441953531756for current production).
Recipe for Automatic Motif Display on Load
- Append
pinned:<PinnedKey>entries tolItemsfor the specific target tracks only. - Set viewport interval
ito base-pair resolution ($\le 1\text{ bp/px}$, 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-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.
# 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 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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/google-deepmind/science-skills/alphagenome-atlas-website-links">View alphagenome-atlas-website-links on skillZs</a>