
tooluniverse-structural-variant-analysis
Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structural-variant-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads tooluniverse-structural-variant-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Structural Variant Analysis Workflow
Systematic analysis of structural variants (deletions, duplications, inversions, translocations, complex rearrangements) for clinical genomics interpretation using ACMG-adapted criteria.
LOOK UP DON'T GUESS - Always retrieve ClinGen HI/TS scores, gnomAD frequencies, and ClinVar evidence from tools. Do not infer dosage sensitivity from gene function alone.
KEY PRINCIPLES:
- Report-first approach - Create SV_analysis_report.md FIRST, then populate progressively
- ACMG-style classification - Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign with explicit evidence
- Evidence grading - Grade all findings by confidence level (High/Moderate/Limited)
- Dosage sensitivity critical - Gene dosage effects drive SV pathogenicity
- Breakpoint precision matters - Exact gene disruption vs dosage-only effects
- Population context essential - gnomAD SVs for frequency assessment
- English-first queries - Always use English terms in tool calls. Respond in the user's language
Triggers
Use this skill when users:
- Ask about structural variant interpretation
- Have CNV data from array or sequencing
- Ask "is this deletion/duplication pathogenic?"
- Need ACMG classification for SVs
- Want to assess gene dosage effects
- Ask about chromosomal rearrangements
- Have large-scale genomic alterations requiring interpretation
SV Pathogenicity Reasoning (Start Here)
Before any tool call, apply this reasoning to frame the analysis:
SV pathogenicity depends on what the SV disrupts. A deletion removing an entire gene is likely pathogenic if the gene is haploinsufficient. A duplication is pathogenic if the gene is dosage-sensitive. An inversion is pathogenic only if it disrupts a coding region or regulatory element at the breakpoint.
Work through these questions in order:
1. What type is the SV, and what disruption mechanism does it cause?
- Deletion: loss of one copy. Pathogenic if any contained gene is haploinsufficient (ClinGen HI score 3, pLI >= 0.9). A deletion of a dosage-insensitive gene in a gene-dense region may be benign even if large.
- Duplication: gain of one copy. Pathogenic if any contained gene is dosage-sensitive (ClinGen TS score 3). Duplications can also disrupt gene regulation if tandem (disrupts reading frame at junction) or if they separate a gene from its enhancer.
- Inversion: no copy number change. Pathogenic only at the breakpoints: if one breakpoint falls within an exon (truncation) or separates a gene from its regulatory element. Inversions entirely within gene-poor, regulatory-poor regions are often benign.
- Translocation: pathogenic if a breakpoint disrupts a coding region or creates a pathogenic fusion gene. Balanced translocations in parents of affected children warrant special scrutiny.
- Complex rearrangements: assess each segment and each breakpoint independently.
2. Is the disrupted gene dosage-sensitive?
- ClinGen HI score 3 = definitive haploinsufficiency (deletion of this gene is pathogenic)
- ClinGen HI score 2 = likely haploinsufficient
- pLI >= 0.9 = strong LoF intolerance (supporting haploinsufficiency)
- ClinGen TS score 3 = definitive triplosensitivity (duplication is pathogenic)
- If no ClinGen data: use OMIM inheritance (autosomal dominant = often dosage-sensitive) as weaker evidence
3. Does the population frequency contextualize the SV?
-
=1% frequency in gnomAD SV = BA1 (likely benign unless phenotype is extreme)
- <0.01% = supports pathogenicity (PM2)
- Present in unaffected parents = weak evidence against pathogenicity, but not conclusive
4. Is there clinical precedent?
- Identical SV in ClinVar as Pathogenic/Likely Pathogenic = strong evidence (PS1)
- De novo occurrence = strong evidence for pathogenicity (PS2)
- Phenotype match to known gene-disease association = supporting evidence (PP4)
Document this reasoning before computing the final score.
Workflow Overview
Phase 1: SV IDENTITY & CLASSIFICATION
Normalize coordinates (hg19/hg38), determine type (DEL/DUP/INV/TRA/CPX),
calculate size, assess breakpoint precision
Phase 2: GENE CONTENT ANALYSIS
Identify fully contained genes, partially disrupted genes (breakpoint within),
flanking genes (within 1 Mb), annotate function and disease associations
Phase 3: DOSAGE SENSITIVITY ASSESSMENT
ClinGen HI/TS scores, pLI scores, OMIM inheritance patterns,
gene-disease validity levels
Phase 4: POPULATION FREQUENCY CONTEXT
gnomAD SV database, ClinVar known SVs, DECIPHER patient cases,
reciprocal overlap calculation (>=70% = same SV)
Phase 5: PATHOGENICITY SCORING
Quantitative 0-10 scale: gene content (40%), dosage sensitivity (30%),
population frequency (20%), clinical evidence (10%)
Phase 6: LITERATURE & CLINICAL EVIDENCE
PubMed searches, DECIPHER cohort analysis, functional evidence
Phase 7: ACMG-ADAPTED CLASSIFICATION
Apply SV-specific evidence codes, calculate final classification,
generate clinical recommendations
Phase 1: SV Identity & Classification
Goal: Standardize SV notation and classify type.
Capture: chromosome(s), coordinates (start/end in hg19/hg38), SV size, SV type (DEL/DUP/INV/TRA/CPX), breakpoint precision, inheritance pattern (de novo/inherited/unknown).
For SV type definitions, scoring tables, and ACMG code details, see CLASSIFICATION_GUIDE.md.
Phase 2: Gene Content Analysis
Goal: Annotate all genes affected by the SV.
Tools:
ensembl_lookup_gene- gene structure, coordinates, exonsNCBIGene_search- official symbol, aliases, descriptionGO_get_term_details- biological process, molecular functionOMIM_search,OMIM_get_entry- disease associations, inheritanceDisGeNET_search_gene- gene-disease association scores
Classify genes as: fully contained (entire gene in SV), partially disrupted (breakpoint within gene), or flanking (within 1 Mb of breakpoints).
For implementation pseudocode, see ANALYSIS_PROCEDURES.md Phase 2.
Phase 3: Dosage Sensitivity Assessment
Goal: Determine if affected genes are dosage-sensitive.
Tools:
ClinGen_search_dosage_sensitivity- HI/TS scores (0-3, gold standard)ClinGen_search_gene_validity- gene-disease validity levelgnomad_search_variants- pLI scores for LoF intoleranceOMIM_get_entry- inheritance pattern (AD suggests dosage sensitivity)
Interpret scores using the reasoning above. ClinGen HI/TS score 3 = definitive; score 2 = likely; score 1 = little evidence; score 0 = no evidence. Do not equate AD inheritance with haploinsufficiency without ClinGen support.
Phase 4: Population Frequency Context
Goal: Determine if SV is common (likely benign) or rare (supports pathogenicity).
Tools:
gnomad_search_variants- population SV frequenciesClinVar_search_variants- known pathogenic/benign SVsClinGen_search_dosage_sensitivity- patient SVs with phenotypes
Use >=70% reciprocal overlap to define "same" SV for comparison. A frequency >=1% triggers BA1 unless there is very strong clinical evidence to override.
Phase 5: Pathogenicity Scoring
Goal: Quantitative pathogenicity assessment on 0-10 scale.
Four components weighted: gene content (40%), dosage sensitivity (30%), population frequency (20%), clinical evidence (10%).
Score mapping: 9-10 = Pathogenic, 7-8 = Likely Pathogenic, 4-6 = VUS, 2-3 = Likely Benign, 0-1 = Benign.
For detailed scoring breakdowns and implementation, see CLASSIFICATION_GUIDE.md and ANALYSIS_PROCEDURES.md Phase 5.
Phase 6: Literature & Clinical Evidence
Goal: Find case reports, functional studies, and clinical validation.
Tools:
PubMed_search_articles- peer-reviewed literatureEuropePMC_search_articles- additional coverageClinGen_search_dosage_sensitivity- patient case database
Search strategies: gene-specific dosage sensitivity papers, SV-specific case reports, phenotype-gene associations. See ANALYSIS_PROCEDURES.md Phase 6.
Phase 7: ACMG-Adapted Classification
Goal: Apply ACMG/ClinGen criteria adapted for SVs and generate a final classification with explicit evidence summary.
The LLM knows the ACMG criteria codes and combination rules. Apply them to the evidence gathered in Phases 1-6. Key points to verify with tool data:
- PVS1 applies to deletions of genes with ClinGen HI score >= 2 or pLI >= 0.9
- PS2 requires confirmed de novo status (check parental genotypes if available)
- PM2 requires absence from population databases at >=70% reciprocal overlap
For complete evidence code tables and classification algorithm, see CLASSIFICATION_GUIDE.md.
Output
Create report using the template in REPORT_TEMPLATE.md. Name files as:
SV_analysis_[TYPE]_chr[CHR]_[START]_[END]_[GENES].md
Required Tools Reference
ClinGen_search_dosage_sensitivity- HI/TS scores (required for all deletions/duplications)ClinGen_search_gene_validity- gene-disease validity (required)ClinVar_search_variants- known pathogenic/benign SVs (required)ensembl_lookup_gene- gene coordinates, structure (required)OMIM_search,OMIM_get_entry- gene-disease associations (required)gnomad_search_variants- population frequency and pLI (required)DisGeNET_search_gene- additional disease associations (recommended)PubMed_search_articles- literature evidence (recommended)GO_get_term_details- gene function (supporting)
When NOT to Use This Skill
- Single nucleotide variants (SNVs) - Use
tooluniverse-variant-interpretation - Small indels (<50 bp) - Use variant interpretation skill
- Somatic variants in cancer - Different framework needed
- Mitochondrial variants - Specialized interpretation required
- Repeat expansions - Different mechanism
Use this skill for structural variants >=50 bp requiring dosage sensitivity assessment and ACMG-adapted classification.
Reference Files
EXAMPLES.md- Sample SV interpretations with worked examplesCLASSIFICATION_GUIDE.md- ACMG criteria, scoring system, evidence codes, special scenarios, clinical recommendationsREPORT_TEMPLATE.md- Full report template with section structure and file namingANALYSIS_PROCEDURES.md- Detailed implementation pseudocode for each phase
External References
- ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/
- ACMG SV Guidelines: Riggs et al., Genet Med 2020 (PMID: 31690835)
tooluniverse-variant-interpretation- For SNVs and small indels
FAQ
What does the tooluniverse-structural-variant-analysis skill do?
Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.
How do I install tooluniverse-structural-variant-analysis?
Run npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structural-variant-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tooluniverse-structural-variant-analysis/ (personal) or .claude/skills/tooluniverse-structural-variant-analysis/ (project).
Which AI agents can use tooluniverse-structural-variant-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is tooluniverse-structural-variant-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains tooluniverse-structural-variant-analysis?
It is published by mims-harvard in mims-harvard/tooluniverse. It has 373 recorded installs.
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