google-deepmind/science-skills

alphagenome-atlas-website-links

- Constructs deep-links and URLs for the AlphaGenome Atlas website.

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使い方

  1. インストールコマンドをコピーし、自分の環境へ導入する前にソースを確認します。
  2. 必要なツール、認証情報、対応環境を原文で確認します。
  3. 入力と成果物を指定し、出力を資料と照合します。
npx skills add https://github.com/google-deepmind/science-skills --skill alphagenome-atlas-website-links

開始前の確認

  • 導入にはモデル契約、API枠、ファイルへのアクセス許可は含まれません。
  • スター数や導入数は活動の指標であり、独立した品質・安全性試験ではありません。
リポジトリの原文

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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](https://deepmind.google.com/science/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.

  1. Set viewport interval i to base-pair resolution ($\le 1\text{ bp/px}$,

window $\le 380\text{ bp}$).

  1. 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.

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