gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/tooluniverse/references/domains.md

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:

# 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