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:

•Target disambiguation FIRST - Resolve IDs, synonyms, naming collisions before literature search
•Report-only output - No search process in the report; methodology in separate appendix only if requested
•Evidence grading - Grade every claim by evidence strength (mechanistic paper vs screen hit vs review vs text-mined)
•Mandatory completeness - All checklist sections must exist, even if "unknown/limited evidence"
•Source attribution - Every piece of information traceable to database/tool

Workflow Overview

code

User Query
  ↓
Phase 0: CLARIFY (Is this a biological target? What scope?)
  ↓
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

•Target type: Is this a biological target (gene/protein), a general topic, or a disease?
•Scope: Comprehensive review, druggability focus, mechanism focus, or quick overview?
•Known aliases: Any specific gene symbols or protein names you use?
•Constraints: Open access only? Include preprints? Specific organisms?
•Methods appendix: Do you want methodology details in a separate file?

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:

code

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:

markdown

## 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:

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

Output for report:

markdown

### 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):

code

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:

markdown

### 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

code

HPA_get_subcellular_location → Human Protein Atlas localization
UniProt_get_subcellular_location_by_accession → UniProt annotation

Output for report:

markdown

### 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

code

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

Output for report:

markdown

### 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

code

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:

markdown

### 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)

code

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:

code

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

code

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

Example for bacterial TraG collision:

code

"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 Tool Failure Handling

Automatic retry strategy:

code

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

2.4 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:

markdown

*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:

markdown

### 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:

markdown

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

[Theme content...]

Report Structure: Mandatory Completeness Checklist

CRITICAL: ALL sections below must exist in the report, even if populated with "Limited evidence available" or "Unknown - no studies identified".

Output Files

•[topic]_report.md - Main narrative report (default deliverable)
•[topic]_bibliography.json - Full deduplicated bibliography (always created)
•methods_appendix.md - Methodology details (ONLY if user requests)

Report Template

markdown

# [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]*

### 6.1 Molecular Function
[What the protein does biochemically]
**Evidence Quality**: [Strong/Moderate/Limited]

### 6.2 Biological Role
[Role in cellular/organismal context]
**Evidence Quality**: [Strong/Moderate/Limited]

### 6.3 Key Pathways
[Pathway involvement with evidence grades]

### 6.4 Regulation
[How the target is regulated]

---

## 7. Model Organism Evidence
*[MANDATORY]*

### 7.1 Mouse Models
[Knockout/knockin phenotypes, if any]

### 7.2 Other Model Organisms
[Yeast, fly, zebrafish, worm data if relevant]

### 7.3 Cross-Species Conservation
[Conservation and functional studies]

---

## 8. Human Genetics & Variants
*[MANDATORY]*

### 8.1 Constraint Scores
[pLI, LOEUF, missense Z - with interpretation]

### 8.2 Disease-Associated Variants
[ClinVar pathogenic variants]

### 8.3 Population Variants
[gnomAD notable variants]

### 8.4 GWAS Associations
[Any GWAS hits for the locus]

---

## 9. Disease Links
*[MANDATORY - include evidence strength]*

### 9.1 Strong Evidence (Genetic + Functional)
[Diseases with causal evidence]

### 9.2 Moderate Evidence (Association + Mechanism)
[Diseases with supporting evidence]

### 9.3 Weak Evidence (Association Only)
[Diseases with correlation/association only]

### 9.4 Evidence Summary Table

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

---

## 10. Pathogen Involvement
*[MANDATORY - state "None identified" if not applicable]*

### 10.1 Viral Interactions
[Any viral exploitation or targeting]

### 10.2 Bacterial Interactions
[Any bacterial relevance]

### 10.3 Host Defense Role
[Role in immune response if any]

---

## 11. Key Assays & Readouts
*[MANDATORY]*

### 11.1 Biochemical Assays
[Available assays for target activity]

### 11.2 Cellular Readouts
[Cell-based assays and phenotypes]

### 11.3 In Vivo Models
[Animal models and endpoints]

---

## 12. Research Themes
*[MANDATORY - structured theme extraction]*

### 12.1 [Theme 1 Name] (N papers)
**Evidence Quality**: [Strong/Moderate/Limited]
**Representative Papers**: [≥3 papers or state "insufficient"]

[Theme description with evidence-graded citations]

### 12.2 [Theme 2 Name] (N papers)
[Same structure]

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

---

## 13. Open Questions & Research Gaps
*[MANDATORY]*

### 13.1 Mechanistic Unknowns
[What we don't understand about the target]

### 13.2 Therapeutic Unknowns
[What we don't know for drug development]

### 13.3 Suggested Priority Questions
[Ranked list of important unanswered questions]

---

## 14. Biological Model & Testable Hypotheses
*[MANDATORY - synthesis section]*

### 14.1 Integrated Biological Model
[3-5 paragraph synthesis integrating all evidence into coherent model]

### 14.2 Testable Hypotheses

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

### 14.3 Suggested Experiments
[Brief description of key experiments to test hypotheses]

---

## 15. Conclusions & Recommendations
*[MANDATORY]*

### 15.1 Key Takeaways
[Bullet points of most important findings]

### 15.2 Confidence Assessment
[Overall confidence in the findings: High/Medium/Low with justification]

### 15.3 Recommended Next Steps
[Prioritized action items]

---

## References

*[Summary reference list in report - full bibliography in separate file]*

### Key Papers (Must-Read)
1. [Citation with PMID] - [Why important] [Grade: ★★★]
2. ...

### By Theme
[Organized reference lists]

---

## Data Limitations

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

*Full methodology available in methods_appendix.md upon request.*

Bibliography File Format

File: [topic]_bibliography.json

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", "OpenAlex"],
      "evidence_tier": "T1",
      "themes": ["lysosomal_acidification", "autophagy"],
      "oa_status": "gold",
      "oa_url": "https://...",
      "citation_count": 45,
      "in_core_set": true
    }
  ]
}

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

Theme Extraction Protocol

Standardized Theme Clustering

•Extract keywords from titles and abstracts
•Cluster into themes using semantic similarity
•Require minimum N papers per theme (default N=3)
•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

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..."

DO NOT expose:

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

The report is the deliverable. Methodology stays internal.

Summary

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

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

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