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🧪 MetaboT 🍵 Overview ✨

🧪 MetaboT 🍵 is an advanced metabolomics analysis tool that combines AI-powered agents, graph-based data management, and sophisticated query capabilities to analyze and interpret metabolomics data.

System Architecture 🏗️

Core Components ⚙️

graph TB
    A[User Query] --> B[Entry Agent]
    B --> G[Validator Agent]
    G --> C[Supervisor Agent]
    C <--> D[ENPKG Agent]
    C <--> E[SPARQL Agent]
    C <--> F[Interpreter Agent]
     E  --> H[Knowledge Graph]

  • Entry Agent 🚪

    • Accepts user queries and input files (if provided) and performs initial processing.

  • Validator Agent ✅

    • Validates user questions for knowledge graph.
    • Verifies plant names using the database.
    • Checks question content against the knowledge graph schema.

  • Supervisor Agent 🎛️

    • Orchestrates the workflow between agents.
    • Manages state and context throughout query processing.
    • Ensures proper sequencing of operations.

  • ENPKG Agent 🧪

    • Handles metabolomics-specific processing.
    • Provides resolutions to the entities mentioned in the question.

  • SPARQL Agent 🔎

    • Generates and executes queries against the RDF knowledge graph.
    • Optimizes query performance.
    • Handles complex graph traversals.

  • Interpreter Agent 📢

    • Processes and formats query results.
    • Generates human-readable outputs.
    • Handles data visualization requests.

Knowledge Graph Integration 🔗

🧪 MetaboT 🍵 utilizes a sophisticated RDF-based knowledge graph that:

  • Stores metabolomics data and relationships.
  • Enables complex query capabilities.
  • Supports data integration from multiple sources.
  • Maintains data provenance.

Key Features 🚀

Query Processing 🔍

🧪 MetaboT 🍵 supports various types of queries:

  • Standard Queries: Pre-defined queries for common analyses.
  • Custom Queries: User-defined natural language queries.
  • Knowledge Graph Integration: Access and analyze data from a comprehensive knowledge graph.
  • Visualization Tools: Generate visualizations to better understand your data.

For development updates, please refer to the dev branch.

AI-Powered Processing 🤖

🧪 MetaboT 🍵 leverages advanced AI capabilities through:

  • Language Models

    • Natural language query processing
    • Context-aware responses
    • Result interpretation

  • Agent Collaboration

    • Multi-agent workflow coordination
    • Specialized task processing
    • Adaptive response generation

Workflow Examples 🛠️

Basic Feature Analysis 📝

sequenceDiagram
    participant User
    participant Entry
    participant Validator
    participant Supervisor
    participant ENPKG
    participant SPARQL 
    participant Graph
    participant Interpreter

    User->>Entry: Submit feature query
    Entry->>Validator: Preprocess the query
    Validator->>Supervisor: Validate the question
    Supervisor->>ENPKG:Select the next agent 
    Supervisor->>SPARQL: Provide the question and resolved entities
    Supervisor->>Interpreter: Provide the results
    SPARQL->>Graph: Generate and execute SPARQL query 
    ENPKG-->>Supervisor: Provide resolved entities
    SPARQL-->>Supervisor: Provide the results
    Interpreter-->>Supervisor: Provide the interpreted results
    Supervisor-->>User: Present final results

Performances ⚡️

Query Optimization 🔧

  • Use highly targeted, knowledge-graph-centric queries that are clearly formatted
  • Leverage standard queries for common operations
  • Consider query complexity and data volume

Best Practices 👍

  1. Query Design

    • Start with standard queries when possible
    • Build custom queries incrementally
    • Test queries with smaller datasets first

  2. System Configuration

    • Keep environment variables updated
    • Monitor system resources
    • Regular maintenance of graph database

Integration Capabilities 🔌

🧪 MetaboT 🍵 can be integrated with:

  • External databases
  • Custom analysis pipelines
  • Visualization tools
  • Reporting systems

Future Developments 🔮

Planned enhancements include:

  • Enhanced visualization capabilities
  • Additional analysis algorithms
  • Expanded database integrations
  • Improved performance optimization

For detailed information about specific components, please refer to the respective sections in the documentation.