# ToolUniverse Tool Domains and Categories

## Overview

ToolUniverse integrates 600+ scientific tools across multiple research domains. This document categorizes tools by scientific discipline and use case.

## Major Scientific Domains

### Bioinformatics

**Sequence Analysis:**
- Sequence alignment and comparison
- Multiple sequence alignment (MSA)
- BLAST and homology searches
- Motif finding and pattern matching

**Genomics:**
- Gene expression analysis
- RNA-seq data processing
- Variant calling and annotation
- Genome assembly and annotation
- Copy number variation analysis

**Functional Analysis:**
- Gene Ontology (GO) enrichment
- Pathway analysis (KEGG, Reactome)
- Gene set enrichment analysis (GSEA)
- Protein domain analysis

**Example Tools:**
- GEO data download and analysis
- DESeq2 differential expression
- KEGG pathway enrichment
- UniProt sequence retrieval
- VEP variant annotation

### Cheminformatics

**Molecular Descriptors:**
- Chemical property calculation
- Molecular fingerprints
- SMILES/InChI conversion
- 3D conformer generation

**Drug Discovery:**
- Virtual screening
- Molecular docking
- ADMET prediction
- Drug-likeness assessment (Lipinski's Rule of Five)
- Toxicity prediction

**Chemical Databases:**
- PubChem compound search
- ChEMBL bioactivity data
- ZINC compound libraries
- DrugBank drug information

**Example Tools:**
- RDKit molecular descriptors
- AutoDock molecular docking
- ZINC library screening
- ChEMBL target-compound associations

### Structural Biology

**Protein Structure:**
- AlphaFold structure prediction
- PDB structure retrieval
- Structure alignment and comparison
- Binding site prediction
- Protein-protein interaction prediction

**Structure Analysis:**
- Secondary structure prediction
- Solvent accessibility calculation
- Structure quality assessment
- Ramachandran plot analysis

**Example Tools:**
- AlphaFold structure prediction
- PDB structure download
- Fpocket binding site detection
- DSSP secondary structure assignment

### Proteomics

**Protein Analysis:**
- Mass spectrometry data analysis
- Protein identification
- Post-translational modification analysis
- Protein quantification

**Protein Databases:**
- UniProt protein information
- STRING protein interactions
- IntAct interaction databases

**Example Tools:**
- UniProt data retrieval
- STRING interaction networks
- Mass spec peak analysis

### Machine Learning

**Model Types:**
- Classification models
- Regression models
- Clustering algorithms
- Neural networks
- Deep learning models

**Applications:**
- Predictive modeling
- Feature selection
- Dimensionality reduction
- Pattern recognition
- Biomarker discovery

**Example Tools:**
- Scikit-learn models
- TensorFlow/PyTorch models
- XGBoost predictors
- Random forest classifiers

### Medical/Clinical

**Disease Databases:**
- OpenTargets disease-target associations
- OMIM genetic disorders
- ClinVar pathogenic variants
- DisGeNET disease-gene associations

**Clinical Data:**
- Electronic health records analysis
- Clinical trial data
- Diagnostic tools
- Treatment recommendations

**Example Tools:**
- OpenTargets disease queries
- ClinVar variant classification
- OMIM disease lookup
- FDA drug approval data

### Neuroscience

**Brain Imaging:**
- fMRI data analysis
- Brain atlas mapping
- Connectivity analysis
- Neuroimaging pipelines

**Neural Data:**
- Electrophysiology analysis
- Spike train analysis
- Neural network simulation

### Image Processing

**Biomedical Imaging:**
- Microscopy image analysis
- Cell segmentation
- Object detection
- Image enhancement
- Feature extraction

**Image Analysis:**
- ImageJ/Fiji tools
- CellProfiler pipelines
- Deep learning segmentation

### Systems Biology

**Network Analysis:**
- Biological network construction
- Network topology analysis
- Module identification
- Hub gene identification

**Modeling:**
- Systems biology models
- Metabolic network modeling
- Signaling pathway simulation

## Tool Categories by Use Case

### Literature and Knowledge

**Literature Search:**
- PubMed article search
- Article summarization
- Citation analysis
- Knowledge extraction

**Knowledge Bases:**
- Ontology queries (GO, DO, HPO)
- Database cross-referencing
- Entity recognition

### Data Access

**Public Repositories:**
- GEO (Gene Expression Omnibus)
- SRA (Sequence Read Archive)
- PDB (Protein Data Bank)
- ChEMBL (Bioactivity database)

**API Access:**
- RESTful API clients
- Database query tools
- Batch data retrieval

### Visualization

**Plot Generation:**
- Heatmaps
- Volcano plots
- Manhattan plots
- Network graphs
- Molecular structures

### Utilities

**Data Processing:**
- Format conversion
- Data normalization
- Statistical analysis
- Quality control

**Workflow Management:**
- Pipeline construction
- Task orchestration
- Result aggregation

## Finding Tools by Domain

Use domain-specific keywords with Tool_Finder:

```python
# Bioinformatics
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "RNA-seq genomics", "limit": 10}
})

# Cheminformatics
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "molecular docking SMILES", "limit": 10}
})

# Structural biology
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "protein structure PDB", "limit": 10}
})

# Clinical
tools = tu.run({
    "name": "Tool_Finder_Keyword",
    "arguments": {"description": "disease clinical variants", "limit": 10}
})
```

## Cross-Domain Applications

Many scientific problems require tools from multiple domains:

- **Precision Medicine**: Genomics + Clinical + Proteomics
- **Drug Discovery**: Cheminformatics + Structural Biology + Machine Learning
- **Cancer Research**: Genomics + Pathways + Literature
- **Neurodegenerative Diseases**: Genomics + Proteomics + Imaging