Merge pull request #59 from sixiang-svg/update-research-part-2

feat: update research skills part 2

Author: markusha77

skills/Research/cancel/SKILL.md

@@ -1,8 +1,12 @@
 ---
-category: Research
 id: chembl-database
 name: ChEMBL Database
-description: Guidance and answers for chembl database.
+description: Guidance for retrieving compound information, bioactivity data, and identifier mapping in ChEMBL.
+category: Research
+requires: []
+examples:
+  - Search for bioactivity data of Geldanamycin in the ChEMBL database.
+  - Map this KEGG compound ID to its corresponding ChEMBL ID.
 ---
 
 # BioServices
@@ -24,334 +28,31 @@ This skill should be used when:
 - Mining genomic data (BioMart, ArrayExpress, ENA)
 - Integrating data from multiple bioinformatics resources in a single workflow
 
-## Core Capabilities
-
-### 1. Protein Analysis
-
-Retrieve protein information, sequences, and functional annotations:
-
-```python
-from bioservices import UniProt
-
-u = UniProt(verbose=False)
-
-# Search for protein by name
-results = u.search("ZAP70_HUMAN", frmt="tab", columns="id,genes,organism")
-
-# Retrieve FASTA sequence
-sequence = u.retrieve("P43403", "fasta")
-
-# Map identifiers between databases
-kegg_ids = u.mapping(fr="UniProtKB_AC-ID", to="KEGG", query="P43403")
-```
-
-**Key methods:**
-- `search()`: Query UniProt with flexible search terms
-- `retrieve()`: Get protein entries in various formats (FASTA, XML, tab)
-- `mapping()`: Convert identifiers between databases
-
-Reference: `references/services_reference.md` for complete UniProt API details.
-
-### 2. Pathway Discovery and Analysis
-
-Access KEGG pathway information for genes and organisms:
-
-```python
-from bioservices import KEGG
-
-k = KEGG()
-k.organism = "hsa"  # Set to human
-
-# Search for organisms
-k.lookfor_organism("droso")  # Find Drosophila species
-
-# Find pathways by name
-k.lookfor_pathway("B cell")  # Returns matching pathway IDs
-
-# Get pathways containing specific genes
-pathways = k.get_pathway_by_gene("7535", "hsa")  # ZAP70 gene
-
-# Retrieve and parse pathway data
-data = k.get("hsa04660")
-parsed = k.parse(data)
-
-# Extract pathway interactions
-interactions = k.parse_kgml_pathway("hsa04660")
-relations = interactions['relations']  # Protein-protein interactions
-
-# Convert to Simple Interaction Format
-sif_data = k.pathway2sif("hsa04660")
-```
-
-**Key methods:**
-- `lookfor_organism()`, `lookfor_pathway()`: Search by name
-- `get_pathway_by_gene()`: Find pathways containing genes
-- `parse_kgml_pathway()`: Extract structured pathway data
-- `pathway2sif()`: Get protein interaction networks
-
-Reference: `references/workflow_patterns.md` for complete pathway analysis workflows.
-
-### 3. Compound Database Searches
-
-Search and cross-reference compounds across multiple databases:
-
-```python
-from bioservices import KEGG, UniChem
-
-k = KEGG()
-
-# Search compounds by name
-results = k.find("compound", "Geldanamycin")  # Returns cpd:C11222
-
-# Get compound information with database links
-compound_info = k.get("cpd:C11222")  # Includes ChEBI links
-
-# Cross-reference KEGG → ChEMBL using UniChem
-u = UniChem()
-chembl_id = u.get_compound_id_from_kegg("C11222")  # Returns CHEMBL278315
-```
-
-**Common workflow:**
-1. Search compound by name in KEGG
-2. Extract KEGG compound ID
-3. Use UniChem for KEGG → ChEMBL mapping
-4. ChEBI IDs are often provided in KEGG entries
-
-Reference: `references/identifier_mapping.md` for complete cross-database mapping guide.
-
-### 4. Sequence Analysis
-
-Run BLAST searches and sequence alignments:
-
-```python
-from bioservices import NCBIblast
-
-s = NCBIblast(verbose=False)
-
-# Run BLASTP against UniProtKB
-jobid = s.run(
-    program="blastp",
-    sequence=protein_sequence,
-    stype="protein",
-    database="uniprotkb",
-    email="your.email@example.com"  # Required by NCBI
-)
-
-# Check job status and retrieve results
-s.getStatus(jobid)
-results = s.getResult(jobid, "out")
-```
-
-**Note:** BLAST jobs are asynchronous. Check status before retrieving results.
-
-### 5. Identifier Mapping
-
-Convert identifiers between different biological databases:
-
-```python
-from bioservices import UniProt, KEGG
-
-# UniProt mapping (many database pairs supported)
-u = UniProt()
-results = u.mapping(
-    fr="UniProtKB_AC-ID",  # Source database
-    to="KEGG",              # Target database
-    query="P43403"          # Identifier(s) to convert
-)
-
-# KEGG gene ID → UniProt
-kegg_to_uniprot = u.mapping(fr="KEGG", to="UniProtKB_AC-ID", query="hsa:7535")
-
-# For compounds, use UniChem
-from bioservices import UniChem
-u = UniChem()
-chembl_from_kegg = u.get_compound_id_from_kegg("C11222")
-```
-
-**Supported mappings (UniProt):**
-- UniProtKB ↔ KEGG
-- UniProtKB ↔ Ensembl
-- UniProtKB ↔ PDB
-- UniProtKB ↔ RefSeq
-- And many more (see `references/identifier_mapping.md`)
-
-### 6. Gene Ontology Queries
-
-Access GO terms and annotations:
-
-```python
-from bioservices import QuickGO
-
-g = QuickGO(verbose=False)
-
-# Retrieve GO term information
-term_info = g.Term("GO:0003824", frmt="obo")
-
-# Search annotations
-annotations = g.Annotation(protein="P43403", format="tsv")
-```
-
-### 7. Protein-Protein Interactions
-
-Query interaction databases via PSICQUIC:
-
-```python
-from bioservices import PSICQUIC
-
-s = PSICQUIC(verbose=False)
-
-# Query specific database (e.g., MINT)
-interactions = s.query("mint", "ZAP70 AND species:9606")
-
-# List available interaction databases
-databases = s.activeDBs
-```
-
-**Available databases:** MINT, IntAct, BioGRID, DIP, and 30+ others.
-
-## Multi-Service Integration Workflows
-
-BioServices excels at combining multiple services for comprehensive analysis. Common integration patterns:
-
-### Complete Protein Analysis Pipeline
-
-Execute a full protein characterization workflow:
-
-```bash
-python scripts/protein_analysis_workflow.py ZAP70_HUMAN your.email@example.com
-```
-
-This script demonstrates:
-1. UniProt search for protein entry
-2. FASTA sequence retrieval
-3. BLAST similarity search
-4. KEGG pathway discovery
-5. PSICQUIC interaction mapping
-
-### Pathway Network Analysis
-
-Analyze all pathways for an organism:
-
-```bash
-python scripts/pathway_analysis.py hsa output_directory/
-```
-
-Extracts and analyzes:
-- All pathway IDs for organism
-- Protein-protein interactions per pathway
-- Interaction type distributions
-- Exports to CSV/SIF formats
-
-### Cross-Database Compound Search
-
-Map compound identifiers across databases:
-
-```bash
-python scripts/compound_cross_reference.py Geldanamycin
-```
-
-Retrieves:
-- KEGG compound ID
-- ChEBI identifier
-- ChEMBL identifier
-- Basic compound properties
-
-### Batch Identifier Conversion
-
-Convert multiple identifiers at once:
-
-```bash
-python scripts/batch_id_converter.py input_ids.txt --from UniProtKB_AC-ID --to KEGG
-```
-
-## Best Practices
-
-### Output Format Handling
-
-Different services return data in various formats:
-- **XML**: Parse using BeautifulSoup (most SOAP services)
-- **Tab-separated (TSV)**: Pandas DataFrames for tabular data
-- **Dictionary/JSON**: Direct Python manipulation
-- **FASTA**: BioPython integration for sequence analysis
-
-### Rate Limiting and Verbosity
-
-Control API request behavior:
-
-```python
-from bioservices import KEGG
-
-k = KEGG(verbose=False)  # Suppress HTTP request details
-k.TIMEOUT = 30  # Adjust timeout for slow connections
-```
-
-### Error Handling
-
-Wrap service calls in try-except blocks:
-
-```python
-try:
-    results = u.search("ambiguous_query")
-    if results:
-        # Process results
-        pass
-except Exception as e:
-    print(f"Search failed: {e}")
-```
-
-### Organism Codes
-
-Use standard organism abbreviations:
-- `hsa`: Homo sapiens (human)
-- `mmu`: Mus musculus (mouse)
-- `dme`: Drosophila melanogaster
-- `sce`: Saccharomyces cerevisiae (yeast)
-
-List all organisms: `k.list("organism")` or `k.organismIds`
-
-### Integration with Other Tools
-
-BioServices works well with:
-- **BioPython**: Sequence analysis on retrieved FASTA data
-- **Pandas**: Tabular data manipulation
-- **PyMOL**: 3D structure visualization (retrieve PDB IDs)
-- **NetworkX**: Network analysis of pathway interactions
-- **Galaxy**: Custom tool wrappers for workflow platforms
-
-## Resources
-
-### scripts/
-
-Executable Python scripts demonstrating complete workflows:
-
-- `protein_analysis_workflow.py`: End-to-end protein characterization
-- `pathway_analysis.py`: KEGG pathway discovery and network extraction
-- `compound_cross_reference.py`: Multi-database compound searching
-- `batch_id_converter.py`: Bulk identifier mapping utility
-
-Scripts can be executed directly or adapted for specific use cases.
-
-### references/
-
-Detailed documentation loaded as needed:
-
-- `services_reference.md`: Comprehensive list of all 40+ services with methods
-- `workflow_patterns.md`: Detailed multi-step analysis workflows
-- `identifier_mapping.md`: Complete guide to cross-database ID conversion
-
-Load references when working with specific services or complex integration tasks.
-
-## Installation
-
-```bash
-uv pip install bioservices
-```
-
-Dependencies are automatically managed. Package is tested on Python 3.9-3.12.
-
-## Additional Information
-
-For detailed API documentation and advanced features, refer to:
-- Official documentation: https://bioservices.readthedocs.io/
-- Source code: https://github.com/cokelaer/bioservices
-- Service-specific references in `references/services_reference.md`
+## Instruction
+You are a Chemical Informatics and Bioactivity Specialist. When this skill is activated, you must guide the user through the retrieval and cross-referencing of compound data using the following behavioral logic:
+
+1. **Compound Identification Logic**: Guide the user in searching the ChEMBL repository for bioactive molecules using common names or structural identifiers.
+2. **Bioactivity & Assay Analysis**: 
+   - Instruct the user on how to retrieve quantitative data, such as IC50, Ki, and EC50 values, from specific assays. 
+   - Explain the logic of filtering results by Target Type and Organism to ensure scientific relevance.
+3. **Cross-Database Mapping**:
+   - Use the logic of UniChem to map identifiers between ChEMBL and other chemical repositories like KEGG, ChEBI, or PubChem.
+   - Describe the workflow for mapping KEGG compound IDs to ChEMBL IDs to bridge pathway analysis with drug discovery data.
+4. **Data Integration Flow**: 
+   - Guide the user in integrating ChEMBL data with protein repositories like UniProt to link ligands with their target receptors.
+   - Explain how to handle tabular data (TSV) or structured JSON returns for downstream analysis.
+
+## Output
+Your response must be structured to provide a professional chemoinformatics report:
+
+### 1. Compound & Bioactivity Summary
+- **Target Compound**: Standard name and ChEMBL identifier.
+- **Activity Profile**: A summary of key bioactivity metrics and the associated targets.
+
+### 2. Implementation Logic (Natural Language)
+- **Search Workflow**: Step-by-step guidance on querying compounds and filtering assay results.
+- **Mapping Logic**: A natural language description of how to bridge identifiers across databases.
+
+### 3. Best Practices & Data Interpretation
+- **Data Quality Warnings**: Reminders to check the "Confidence Score" of assays.
+- **Unit Standardization**: Advice on ensuring concentration units are consistent across data sets.