tooluniverse-literature-deep-research

name	tooluniverse-literature-deep-research
description	Conduct comprehensive literature research with target disambiguation, evidence grading, and structured theme extraction. Creates a detailed report with mandatory completeness checklist, biological model synthesis, and testable hypotheses. For biological targets, resolves offic...
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Literature Deep Research Strategy (Enhanced)

A systematic approach to comprehensive literature research that starts with target disambiguation to prevent missing details, uses evidence grading to separate signal from noise, and produces a content-focused report with mandatory completeness sections.

KEY PRINCIPLES:

1. Target disambiguation FIRST - Resolve IDs, synonyms, naming collisions before literature search
2. Right-size the deliverable - Use Factoid / Verification Mode for single, answerable questions; use full report mode for “deep research”
3. Report-first output - Default deliverable is a report file; an inline answer is allowed (and recommended) for Factoid / Verification Mode
4. Evidence grading - Grade every claim by evidence strength (mechanistic paper vs screen hit vs review vs text-mined)
5. Mandatory completeness - All checklist sections must exist, even if "unknown/limited evidence"
6. Source attribution - Every piece of information traceable to database/tool
7. English-first queries - Always use English terms for literature searches and tool calls, even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language

---

Workflow Overview

User Query
  ↓
Phase 0: CLARIFY + MODE SELECT (factoid vs deep report)
  ↓
Phase 1: TARGET DISAMBIGUATION + PROFILE (default ON for biological targets)
  ├─ Resolve official IDs (Ensembl, UniProt, HGNC)
  ├─ Gather synonyms/aliases + known naming collisions
  ├─ Get protein length, isoforms, domain architecture
  ├─ Get subcellular location, expression, GO terms, pathways
  └─ Output: Target Profile section + Collision-aware search plan
  ↓
Phase 2: LITERATURE SEARCH (internal methodology, not shown)
  ├─ High-precision seed queries (build mechanistic core)
  ├─ Citation network expansion from seeds
  ├─ Collision-filtered broader queries
  └─ Theme clustering + evidence grading
  ↓
Phase 3: REPORT SYNTHESIS
  ├─ Progressive writing to [topic]_report.md
  ├─ Mandatory completeness checklist validation
  └─ Biological model + testable hypotheses
  ↓
Optional: methods_appendix.md (only if user requests)

---

Phase 0: Initial Clarification

Mandatory Questions

1. Target type: Is this a biological target (gene/protein), a general topic, or a disease?
2. Scope: Is this a single factoid to verify (“Which antibiotic?”, “Which strain?”, “Which year?”) or a comprehensive/deep review?
3. Known aliases: Any specific gene symbols or protein names you use?
4. Constraints: Open access only? Include preprints? Specific organisms?
5. Methods appendix: Do you want methodology details in a separate file?

Mode Selection (CRITICAL)

Pick exactly one mode based on the user’s intent and the question structure:

1. Factoid / Verification Mode (single concrete question; answer should be a short phrase/sentence)
2. Mini-review Mode (narrow topic; 1–3 pages of synthesis)
3. Full Deep-Research Mode (use the full template + completeness checklist)

Heuristic:

• If the user asks “X has been evolved to be resistant to which antibiotic?” → Factoid / Verification Mode
• If the user asks “What does the literature say about X?” → Full Deep-Research Mode

Factoid / Verification Mode (Fast Path)

Goal: Provide a correct, source-verified single answer, with minimal but explicit evidence attribution.

Deliverables (still file-backed):

1. [topic]_factcheck_report.md (≤ 1 page)
2. [topic]_bibliography.json (+ CSV) containing the key paper(s)

Fact-check report template:

# [TOPIC]: Fact-check Report

*Generated: [Date]*
*Evidence cutoff: [Date]*

## Question
[User question]

## Answer
**[One-sentence answer]** [Evidence: ★★★/★★☆/★☆☆/☆☆☆]

## Source(s)
- [Primary paper citation: journal/year/PMID/DOI as available]

## Verification Notes
- [1–3 bullets: where in the paper the statement appears (Abstract/Results/Methods), and any key constraints]

## Limitations
- [If full text not available, or if only review evidence exists]

Required verification behavior:

• Prefer ToolUniverse literature tools (Europe PMC / PubMed / PMC / Semantic Scholar) over general web browsing.
• Use full-text snippet verification when possible (Europe PMC auto-snippet tier is ideal).
• Avoid adding extra claims (e.g., “not X”) unless the paper explicitly supports them.

Suggested tool pattern:

• EuropePMC_search_articles(query=..., extract_terms_from_fulltext=[...]) to pull OA full-text snippets for the key terms.
• If OA snippets unavailable: fall back to PMC_search_papers (if in PMC) or SemanticScholar_search_papers → SemanticScholar_get_pdf_snippets.

Evidence grading (factoid):

• If the statement is explicitly made in a primary experimental paper (Results/Methods/Abstract): label T1 (★★★).
• If it’s only in a review: label T4 (☆☆☆) and try to locate the primary source.

Detect Target Type

Query Pattern	Type	Action
Gene symbol (EGFR, TP53, ATP6V1A)	Biological target	Phase 1 required
Protein name ("V-ATPase", "kinase")	Biological target	Phase 1 required
UniProt ID (P00533, Q93050)	Biological target	Phase 1 required
Disease, pathway, method	General topic	Phase 1 optional
"Literature on X"	Depends on X	Assess X

---

Phase 1: Target Disambiguation + Profile (Default ON)

CRITICAL: This phase prevents "missing target details" when literature is sparse or noisy.

1.1 Resolve Official Identifiers

Use these tools to establish canonical identity:

UniProt_search → Get UniProt accession for human protein
UniProt_get_entry_by_accession → Full entry with cross-references
UniProt_id_mapping → Map between ID types
ensembl_lookup_gene → Ensembl gene ID, biotype
MyGene_get_gene_annotation → NCBI Gene ID, aliases, summary

Output for report:

## Target Identity

| Identifier | Value | Source |
|------------|-------|--------|
| Official Symbol | ATP6V1A | HGNC |
| UniProt | P38606 | UniProt |
| Ensembl Gene | ENSG00000114573 | Ensembl |
| NCBI Gene ID | 523 | NCBI |
| ChEMBL Target | CHEMBL2364682 | ChEMBL |

**Full Name**: V-type proton ATPase catalytic subunit A
**Synonyms/Aliases**: ATP6A1, VPP2, Vma1, VA68

1.2 Identify Naming Collisions

CRITICAL: Many gene names have collisions. Examples:

• TRAG: T-cell regulatory gene vs bacterial TraG conjugation protein
• WDR7-7: Could match gene WDR7 vs lncRNA
• JAK: Janus kinase vs Just Another Kinase
• CAT: Catalase vs chloramphenicol acetyltransferase

Detection strategy:

1. Search PubMed for "[SYMBOL]"[Title] - review first 20 titles
2. If >20% off-topic, identify collision terms
3. Build negative filter: NOT [collision_term1] NOT [collision_term2]

Output for report:

### Known Naming Collisions

- Symbol "ATP6V1A" is unambiguous (no major collisions detected)
- Related but distinct: ATP6V0A1-4 (V0 subunits vs V1 subunits)
- Search filter applied: Include "vacuolar" OR "V-ATPase", exclude "V0 domain" when V1-specific

1.3 Protein Architecture & Domains

Use annotation tools (not literature):

InterPro_get_protein_domains → Domain architecture
UniProt_get_ptm_processing_by_accession → PTMs, active sites
proteins_api_get_protein → Additional protein features

Output for report:

### Protein Architecture

| Domain | Position | InterPro ID | Function |
|--------|----------|-------------|----------|
| V-ATPase A subunit, N-terminal | 1-90 | IPR022879 | ATP binding |
| V-ATPase A subunit, catalytic | 91-490 | IPR005725 | Catalysis |
| V-ATPase A subunit, C-terminal | 491-617 | IPR022878 | Complex assembly |

**Length**: 617 aa | **Isoforms**: 2 (canonical P38606-1, variant P38606-2 missing aa 1-45)
**Active sites**: Lys-168 (ATP binding), Glu-261 (catalytic)

*Sources: InterPro, UniProt*

1.4 Subcellular Location

HPA_get_subcellular_location → Human Protein Atlas localization
UniProt_get_subcellular_location_by_accession → UniProt annotation

Output for report:

### Subcellular Localization

| Location | Confidence | Source |
|----------|------------|--------|
| Lysosome membrane | High | HPA + UniProt |
| Endosome membrane | High | UniProt |
| Golgi apparatus | Medium | HPA |
| Plasma membrane (subset) | Low | Literature |

**Primary location**: Lysosomal/endosomal membranes (vacuolar ATPase complex)
*Sources: Human Protein Atlas, UniProt*

1.5 Baseline Expression

GTEx_get_median_gene_expression → Tissue expression (TPM)
HPA_get_rna_expression_by_source → HPA expression data

Output for report:

### Baseline Tissue Expression

| Tissue | Expression (TPM) | Specificity |
|--------|------------------|-------------|
| Kidney cortex | 145.3 | Elevated |
| Liver | 98.7 | Medium |
| Brain - Cerebellum | 87.2 | Medium |
| Lung | 76.4 | Medium |
| Ubiquitous baseline | ~50 | Broad |

**Tissue Specificity**: Low (τ = 0.28) - broadly expressed housekeeping gene
*Source: GTEx v8*

1.6 GO Terms & Pathway Placement

GO_get_annotations_for_gene → GO annotations
Reactome_map_uniprot_to_pathways → Reactome pathways
kegg_get_gene_info → KEGG pathways
OpenTargets_get_target_gene_ontology_by_ensemblID → Open Targets GO

Output for report:

### Functional Annotations (GO)

**Molecular Function**:
- ATP hydrolysis activity (GO:0016887) [Evidence: IDA]
- Proton-transporting ATPase activity (GO:0046961) [Evidence: IDA]

**Biological Process**:
- Lysosomal acidification (GO:0007041) [Evidence: IMP]
- Autophagy (GO:0006914) [Evidence: IMP]
- Bone resorption (GO:0045453) [Evidence: IMP]

**Cellular Component**:
- Vacuolar proton-transporting V-type ATPase, V1 domain (GO:0000221) [Evidence: IDA]

### Pathway Involvement

| Pathway | Database | Significance |
|---------|----------|--------------|
| Lysosome | KEGG hsa04142 | Core component |
| Phagosome | KEGG hsa04145 | Acidification |
| Autophagy - animal | Reactome R-HSA-9612973 | mTORC1 regulation |

*Sources: GO Consortium, Reactome, KEGG*

---

Phase 2: Literature Search (Internal Methodology)

NOTE: This methodology is kept internal. The report shows findings, not process.

2.1 Query Strategy: Collision-Aware Synonym Plan

Step 1: High-Precision Seed Queries (Build Mechanistic Core)

Query 1: "[GENE_SYMBOL]"[Title] AND (mechanism OR function OR structure)
Query 2: "[FULL_PROTEIN_NAME]"[Title] 
Query 3: "[UNIPROT_ID]" (catches supplementary materials)

Purpose: Get 15-30 high-confidence, mechanistic papers that are definitely on-target.

Step 2: Citation Network Expansion (Especially for Sparse Targets)

Once you have 5-15 core PMIDs:

PubMed_get_cited_by → Papers citing each seed
PubMed_get_related → Computationally related papers  
EuropePMC_get_citations → Alternative citation source
EuropePMC_get_references → Backward citations from seeds

Citation-network first option: For older targets with deprecated terminology, citation expansion often outperforms keyword searching.

Step 3: Collision-Filtered Broader Queries

Broader query: "[GENE_SYMBOL]" AND ([pathway1] OR [pathway2] OR [function])
Apply collision filter: NOT [collision_term1] NOT [collision_term2]

Example for bacterial TraG collision:

"TRAG" AND (T-cell OR immune OR cancer) NOT plasmid NOT conjugation NOT bacterial

2.2 Database Tools

Literature Search (use all relevant):

• PubMed_search_articles - Primary biomedical
• PMC_search_papers - Full-text
• EuropePMC_search_articles - European coverage
• openalex_literature_search - Broad academic
• Crossref_search_works - DOI registry
• SemanticScholar_search_papers - AI-ranked
• BioRxiv_search_preprints / MedRxiv_search_preprints - Preprints

Citation Tools (with failure handling):

• PubMed_get_cited_by - Primary (NCBI elink can be flaky)
• EuropePMC_get_citations - Fallback when PubMed fails
• PubMed_get_related - Related articles
• EuropePMC_get_references - Reference lists

Annotation Tools (not literature, but fill gaps):

• UniProt_* tools - Protein data
• InterPro_get_protein_domains - Domains
• GTEx_* tools - Expression
• HPA_* tools - Human Protein Atlas
• OpenTargets_* tools - Target-disease associations
• GO_get_annotations_for_gene - GO terms

2.3 Full-Text Verification Strategy

WHEN TO USE: Abstracts lack critical experimental details (exact drugs, cell lines, concentrations, specific protocols).

Three-Tier Strategy:

Tier 1: Auto-Snippet Mode (Europe PMC) - FASTEST

Use for: Exploratory queries with 3-5 specific terms

results = EuropePMC_search_articles(
    query="bacterial antibiotic resistance evolution",
    limit=10,
    extract_terms_from_fulltext=["ciprofloxacin", "meropenem", "A. baumannii", "MIC"]
)

# Check which articles have full-text snippets
for article in results:
    if "fulltext_snippets" in article:
        # Snippets automatically extracted from OA full text
        for snippet in article["fulltext_snippets"]:
            # Use snippet["term"] and snippet["snippet"] for verification
            pass

Advantages:

• ✅ Single tool call (search + snippets)
• ✅ Bounded latency (max 3 OA articles, ~3-5 seconds total)
• ✅ No manual URL extraction
• ✅ Max 5 search terms

Limitations:

• ❌ Only works for OA articles with fullTextXML
• ❌ Limited to first 3 OA articles
• ❌ Europe PMC coverage only (~30-40% OA)

When to use: Initial exploration, quick verification of 1-2 papers

Tier 2: Manual Two-Step (Semantic Scholar, ArXiv) - TARGETED

Use for: Specific high-value papers you identified from search

# Step 1: Search
papers = SemanticScholar_search_papers(
    query="machine learning interpretability",
    limit=10
)

# Step 2: Extract from specific OA papers
for paper in papers:
    if paper.get("open_access_pdf_url"):
        snippets = SemanticScholar_get_pdf_snippets(
            open_access_pdf_url=paper["open_access_pdf_url"],
            terms=["SHAP", "gradient attribution", "layer-wise relevance"],
            window_chars=300
        )
        if snippets["status"] == "success":
            # Process snippets["snippets"]
            pass

ArXiv variant (100% OA, no paywall):

# All arXiv papers are freely available
snippets = ArXiv_get_pdf_snippets(
    arxiv_id="2301.12345",
    terms=["attention mechanism", "self-attention", "layer normalization"],
    max_snippets_per_term=5
)

Advantages:

• ✅ Full control over which papers to process
• ✅ Adjustable window size (20-2000 chars)
• ✅ Works for Semantic Scholar (~15-20% OA PDFs) and ArXiv (100%)
• ✅ Can process any number of papers

Limitations:

• ❌ Two tool calls per article (search → extract)
• ❌ Manual loop needed
• ❌ Slower than auto-snippet mode

When to use: Thorough review of key papers, preprint analysis

Tier 3: Manual Download + Parse (Fallback) - SLOWEST

Use for: Paywalled content via institutional access

# For paywalled PDFs accessible via institution
webpage_text = get_webpage_text_from_url(
    url="https://doi.org/10.1016/...",
    # Requires institutional proxy or VPN
)

# Extract relevant sections manually
if "Methods" in webpage_text:
    # Parse methods section
    pass

Limitations:

• ❌ Requires institutional access
• ❌ No snippet extraction (full HTML)
• ❌ Quality varies by publisher
• ❌ Slowest approach

When to use: Last resort for critical paywalled papers

Decision Matrix

Scenario	Recommended Tier	Rationale
Quick verification ("Which antibiotic?")	Tier 1 (Auto-snippet)	Fast, single call
Preprint deep-dive (arXiv, bioRxiv)	Tier 2 (Manual ArXiv)	100% coverage, no paywall
High-value paper deep analysis	Tier 2 (Manual S2)	Precise control
Systematic review (50+ papers)	Tier 1 + Tier 2	Auto for OA, manual for key papers
Paywalled critical paper	Tier 3 (Manual download)	Only option

Best Practices

1. Limit search terms to 3-5 specific keywords:

• ✅ Good: ["ciprofloxacin 5 μg/mL", "HEK293 cells", "RNA-seq"]
• ❌ Bad: ["drug", "method", "significant"] (too broad)

2. Check OA status before extraction:

if article.get("open_access") and article.get("fulltext_xml_url"):
    # Proceed with extraction
    pass

3. Adjust window size for context:

• Methods: 400-500 chars (full sentences)
• Quick verification: 150-200 chars
• Default: 220 chars (balanced)

4. Handle failures gracefully:

if "fulltext_snippets" not in article:
    # Fallback: use abstract or skip
    print(f"No full text available: {article['title']}")

5. Document full-text sources in report:

## Methods Verification

**Antibiotic concentrations** (verified from full text):
- Study A: Ciprofloxacin 5 μg/mL [PMC12345, Methods section]
- Study B: Meropenem 8 μg/mL [arXiv:2301.12345, Experimental Design]

*Note: Full-text verification performed on 8/15 OA papers (53% coverage)*

2.5 Tool Failure Handling

Automatic retry strategy:

Attempt 1: Call tool
If timeout/error:
  Wait 2 seconds
  Attempt 2: Retry
If still fails:
  Wait 5 seconds  
  Attempt 3: Try fallback tool
If fallback fails:
  Document "Data unavailable" in report

Fallback chains:

Primary Tool	Fallback 1	Fallback 2
PubMed_get_cited_by	EuropePMC_get_citations	OpenAlex citations
PubMed_get_related	SemanticScholar recommendations	Manual keyword search
GTEx_get_median_gene_expression	HPA_get_rna_expression_by_source	Document as unavailable
Unpaywall_check_oa_status	Europe PMC OA flags	OpenAlex OA field

2.6 Open Access Handling (Best-Effort)

If Unpaywall email provided: Check OA status for all papers with DOIs

If no Unpaywall email: Use best-effort OA signals:

• Europe PMC: isOpenAccess field
• PMC: All PMC papers are OA
• OpenAlex: is_oa field
• DOAJ: All DOAJ papers are OA

Label in report:

*OA Status: Best-effort (Unpaywall not configured)*

---

Phase 3: Evidence Grading

CRITICAL: Grade every claim by evidence strength to prevent low-signal mentions from diluting the report.

Evidence Tiers

Tier	Label	Description	Example
T1	★★★ Mechanistic	In-target mechanistic study with direct experimental evidence	CRISPR KO + rescue
T2	★★☆ Functional	Functional study showing role (may be in pathway context)	siRNA knockdown phenotype
T3	★☆☆ Association	Screen hit, GWAS association, correlation	High-throughput screen
T4	☆☆☆ Mention	Review mention, text-mined interaction, peripheral reference	Review article

How to Apply

In report, label sections and claims:

### Mechanism of Action

ATP6V1A is the catalytic subunit responsible for ATP hydrolysis in the V-ATPase 
complex [★★★ Mechanistic: PMID:12345678]. Loss-of-function mutations cause 
vacuolar pH dysregulation [★★★: PMID:23456789].

The target has been implicated in mTORC1 signaling through lysosomal amino acid 
sensing [★★☆ Functional: PMID:34567890], though direct interaction data is limited.

A genome-wide screen identified ATP6V1A as essential in cancer cell lines 
[★☆☆ Association: PMID:45678901, DepMap].

Theme-Level Grading

For each theme section, summarize evidence quality:

### 3.1 Lysosomal Acidification (12 papers)
**Evidence Quality**: Strong (8 mechanistic, 3 functional, 1 association)

[Theme content...]

---

Report Structure: Mandatory Completeness Checklist

CRITICAL: This checklist/template applies to Full Deep-Research Mode. For Factoid / Verification Mode, use a short fact-check report (see Phase 0) and do not force the full 15-section template.

Output Files

1. [topic]_report.md - Main narrative report (Full Deep-Research Mode)
2. [topic]_factcheck_report.md - Short verification report (Factoid / Verification Mode)
3. [topic]_bibliography.json - Full deduplicated bibliography (always created)
4. methods_appendix.md - Methodology details (ONLY if user requests)

Report Template

# [TARGET/TOPIC]: Comprehensive Research Report

*Generated: [Date]*
*Evidence cutoff: [Date]*
*Total unique papers: [N]*

---

## Executive Summary

[2-3 paragraphs synthesizing key findings across all sections]

**Bottom Line**: [One-sentence actionable conclusion]

---

## 1. Target Identity & Aliases
*[MANDATORY - even for non-target topics, clarify scope]*

### 1.1 Official Identifiers
[Table of IDs or scope definition]

### 1.2 Synonyms and Aliases  
[List all known names - critical for complete literature coverage]

### 1.3 Known Naming Collisions
[Document collisions and how they were handled]

---

## 2. Protein Architecture
*[MANDATORY for protein targets; state "N/A - not a protein target" otherwise]*

### 2.1 Domain Structure
[Table of domains with positions, InterPro IDs]

### 2.2 Isoforms
[List isoforms, functional differences if known]

### 2.3 Key Structural Features
[Active sites, binding sites, PTMs]

### 2.4 Available Structures
[PDB entries, AlphaFold availability]

---

## 3. Complexes & Interaction Partners
*[MANDATORY]*

### 3.1 Known Complexes
[List complexes the protein participates in]

### 3.2 Direct Interactors
[Table of top interactors with evidence type and scores]

### 3.3 Functional Interaction Network
[Describe network context]

---

## 4. Subcellular Localization
*[MANDATORY]*

[Table of locations with confidence levels and sources]

---

## 5. Expression Profile
*[MANDATORY]*

### 5.1 Tissue Expression
[Table of top tissues with TPM values]

### 5.2 Cell-Type Expression
[If single-cell data available]

### 5.3 Disease-Specific Expression
[Expression changes in disease contexts]

---

## 6. Core Mechanisms
*[MANDATORY - this is the heart of the report]*


[What the protein does biochemically]
: [Strong/Moderate/Limited]


[Role in cellular/organismal context]
: [Strong/Moderate/Limited]


[Pathway involvement with evidence grades]


[How the target is regulated]

---





[Knockout/knockin phenotypes, if any]


[Yeast, fly, zebrafish, worm data if relevant]


[Conservation and functional studies]

---





[pLI, LOEUF, missense Z - with interpretation]


[ClinVar pathogenic variants]


[gnomAD notable variants]


[Any GWAS hits for the locus]

---





[Diseases with causal evidence]


[Diseases with supporting evidence]


[Diseases with correlation/association only]



| Disease | Evidence Type | Score | Key Papers | Grade |
|---------|---------------|-------|------------|-------|
| [Disease 1] | Genetic + Functional | 0.85 | PMID:xxx | ★★★ |
| [Disease 2] | GWAS + Expression | 0.45 | PMID:yyy | ★★☆ |

---





[Any viral exploitation or targeting]


[Any bacterial relevance]


[Role in immune response if any]

---





[Available assays for target activity]


[Cell-based assays and phenotypes]


[Animal models and endpoints]

---





: [Strong/Moderate/Limited]
: [≥3 papers or state "insufficient"]

[Theme description with evidence-graded citations]


[Same structure]

[Continue for all themes - require ≥3 representative papers per theme, or state "limited evidence"]

---





[What we don't understand about the target]


[What we don't know for drug development]


[Ranked list of important unanswered questions]

---





[3-5 paragraph synthesis integrating all evidence into coherent model]



| # | Hypothesis | Perturbation | Readout | Expected Result | Priority |
|---|------------|--------------|---------|-----------------|----------|
| 1 | [Hypothesis] | [Experiment] | [Measure] | [Prediction] | HIGH |
| 2 | [Hypothesis] | [Experiment] | [Measure] | [Prediction] | HIGH |
| 3 | [Hypothesis] | [Experiment] | [Measure] | [Prediction] | MEDIUM |


[Brief description of key experiments to test hypotheses]

---





[Bullet points of most important findings]


[Overall confidence in the findings: High/Medium/Low with justification]


[Prioritized action items]

---




This skill accepts requests that match the documented purpose of  and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:








 [Citation with PMID] - [Why important] [Grade: ★★★]
 ...


[Organized reference lists]

---



 [Any databases that failed or returned no data]
 [Any known gaps in coverage]
 [OA status method used]

---

Bibliography File Format

File: [topic]_bibliography.json

{
  "metadata": {
    "generated": "2026-02-04",
    "query": "ATP6V1A",
    "total_papers": 342,
    "unique_after_dedup": 287
  },
  "papers": [
    {
      "pmid": "12345678",
      "doi": "10.1038/xxx",
      "title": "Paper Title",
      "authors": ["Smith A", "Jones B"],
      "year": 2024,
      "journal": "Nature",
      "source_databases": ["PubMed", 
       
        
       
       
       
       
    
  

Also generate [topic]_bibliography.csv with same data in tabular format.

---

Theme Extraction Protocol

Standardized Theme Clustering

1. Extract keywords from titles and abstracts
2. Cluster into themes using semantic similarity
3. Require minimum N papers per theme (default N=3)
4. Label themes with standardized names

Standard Theme Categories (adapt to target)

For V-ATPase target example:

• lysosomal_acidification - Core function
• autophagy_regulation - mTORC1 signaling
• bone_resorption - Osteoclast function
• cancer_metabolism - Tumor acidification
• viral_infection - Viral entry mechanism
• neurodegenerative - Neuronal dysfunction
• kidney_function - Renal acid-base
• methodology - Assays/tools papers

Theme Quality Requirements

Papers	Theme Status
≥10	Major theme (full section)
3-9	Minor theme (subsection)
<3	Insufficient (note in "limited evidence" or merge)

---

Completeness Checklist (Verify Before Delivery)

ALL boxes must be checked or explicitly marked "N/A" or "Limited evidence"

Identity & Context

• Official identifiers resolved (UniProt, Ensembl, NCBI, ChEMBL)
• All synonyms/aliases documented
• Naming collisions identified and handled
• Protein architecture described (or N/A stated)
• Subcellular localization documented
• Baseline expression profile included

Mechanism & Function

• Core mechanism section with evidence grades
• Pathway involvement documented
• Model organism evidence (or "none found")
• Complexes/interaction partners listed
• Key assays/readouts described

Disease & Clinical

• Human genetic variants documented
• Constraint scores with interpretation
• Disease links with evidence strength grades
• Pathogen involvement (or "none identified")

Synthesis

• Research themes clustered with ≥3 papers each (or noted as limited)
• Open questions/gaps articulated
• Biological model synthesized
• ≥3 testable hypotheses with experiments
• Conclusions with confidence assessment

Technical

• All claims have source attribution
• Evidence grades applied throughout
• Bibliography file generated
• Data limitations documented

---

Quick Reference: Tool Categories

Literature Tools

PubMed_search_articles, PMC_search_papers, EuropePMC_search_articles, openalex_literature_search, Crossref_search_works, SemanticScholar_search_papers, BioRxiv_search_preprints, MedRxiv_search_preprints

Citation Tools

PubMed_get_cited_by, PubMed_get_related, EuropePMC_get_citations, EuropePMC_get_references

Protein/Gene Annotation Tools

UniProt_get_entry_by_accession, UniProt_search, UniProt_id_mapping, InterPro_get_protein_domains, proteins_api_get_protein

Expression Tools

GTEx_get_median_gene_expression, GTEx_get_gene_expression, HPA_get_rna_expression_by_source, HPA_get_comprehensive_gene_details_by_ensembl_id, HPA_get_subcellular_location

Variant/Disease Tools

gnomad_get_gene_constraints, gnomad_get_gene, clinvar_search_variants, OpenTargets_get_diseases_phenotypes_by_target_ensembl

Pathway Tools

GO_get_annotations_for_gene, Reactome_map_uniprot_to_pathways, kegg_get_gene_info, OpenTargets_get_target_gene_ontology_by_ensemblID

Interaction Tools

STRING_get_protein_interactions, intact_get_interactions, OpenTargets_get_target_interactions_by_ensemblID

OA Tools

Unpaywall_check_oa_status (if email provided), or use OA flags from Europe PMC/OpenAlex

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Communication with User

During research (brief updates):

• "Resolving target identifiers and gathering baseline profile..."
• "Building core paper set with high-precision queries..."
• "Expanding via citation network..."
• "Clustering into themes and grading evidence..."

When the question looks like a factoid:

• Ask (once) if the user wants just the verified answer or a full deep-research report.
• If the user doesn’t specify, default to Factoid / Verification Mode and keep it short + source-backed.

DO NOT expose:

• Raw tool outputs
• Deduplication counts
• Search round details
• Database-by-database results

The report is the deliverable. Methodology stays internal.

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Summary

This skill produces comprehensive, evidence-graded research reports that:

1. Start with disambiguation to prevent naming collisions and missing details
2. Use annotation tools to fill gaps when literature is sparse
3. Grade all evidence to separate signal from noise
4. Require completeness even if stating "limited evidence"
5. Synthesize into biological models with testable hypotheses
6. Separate narrative from bibliography for scalability
7. Keep methodology internal unless explicitly requested

The result is a detailed, actionable research report that reads like an expert synthesis, not a search log.