跳到正文
cccskills
免费在 GitHub 公开

alphagenome-atlas-website-links

Create links that open specific genetic variants or genomic regions in AlphaGenome Atlas. Set tissue and display filters, build linked candidate tables, and open charts comparing predictions for reference and alternate DNA sequences.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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).

实测评分 未实测

查看安装方法

适用场景

  • Open a specific genetic variant in AlphaGenome Atlas
  • Explore a genomic region with selected display filters
  • Build a candidate variant table with clickable links
  • Compare predictions for reference and alternate sequences

包含的文件(3)

  • SKILL.md12.3 KB
  • pyproject.toml215 B
  • scripts/alphagenome_atlas_links.py36.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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).

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 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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Retrieve AlphaFold protein structures using a UniProt ID and summarize prediction confidence, boundaries of rigid structural regions, and likely disordered regions. Downloaded structure files and error estimates support further examination of the results.

中文简介正在准备中。当前显示英语简介。

  • Check confidence in a predicted protein structure
  • Identify boundaries of rigid structural regions
  • Assess regions likely to lack a stable structure
google-deepmind/science-skills33262026年10月23日 更新

Analyze a genetic variant with the AlphaGenome API to predict changes in gene activity, DNA accessibility, and RNA splicing, the joining of RNA segments. Produce comparison plots and a report highlighting effects across tissues.

中文简介正在准备中。当前显示英语简介。

  • Explore predicted effects of non-coding genetic variants
  • Compare predicted gene activity changes across tissues
  • Visualize changes in RNA splicing
google-deepmind/science-skills33262026年10月23日 更新

Use AlphaGenome scores to investigate predicted molecular effects of genetic variants, annotate variant files, and rank research candidates. Scan genomic regions for single nucleotide substitutions and retrieve matching gene structures, including exon and splice junction coordinates.

中文简介正在准备中。当前显示英语简介。

  • Inspecting predicted functional effects of individual variants
  • Annotating variant files and ranking research candidates
  • Scanning genomic regions for impactful single nucleotide substitutions
google-deepmind/science-skills33262026年10月23日 更新

Search ChEMBL for compounds, drug targets, and measured biological activity. Retrieve drug mechanisms, compare activity values in a common unit, find similar chemical structures, and download structure files or images for further analysis.

中文简介正在准备中。当前显示英语简介。

  • Find compounds and their drug targets
  • Compare IC50 values in a common unit
  • Check drug mechanisms and indications
google-deepmind/science-skills33262026年10月23日 更新

Search ClinicalTrials.gov for studies by condition, treatment, location, recruitment status, or trial phase. Retrieve individual trial details and participation criteria, count matching studies, and explore trials run by a particular company or institution.

中文简介正在准备中。当前显示英语简介。

  • Finding recruiting trials by condition and location
  • Checking trial participation and exclusion criteria
  • Counting studies by condition, phase, or time period
google-deepmind/science-skills33262026年10月23日 更新

Look up human genetic variants in ClinVar to retrieve disease-related classifications, review status, associated conditions, and evidence from submitting laboratories. Compare conflicting interpretations or find variants classified as pathogenic for benchmark controls.

中文简介正在准备中。当前显示英语简介。

  • Checking a variant's disease-related classification
  • Comparing interpretations from different laboratories
  • Finding pathogenic variants for benchmark controls
google-deepmind/science-skills33262026年10月23日 更新

查看 google-deepmind 的全部技能

报告此技能的问题