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LimbLab Development Roadmap

This document outlines the development roadmap for LimbLab, including planned features, improvements, and long-term vision.

Vision Statement

LimbLab aims to become the leading open-source platform for 3D limb development analysis, providing researchers with powerful, accessible, and scientifically rigorous tools for understanding limb development at the molecular and morphological levels.

Current Status (Q2 2025)

Completed Features

  • Core Pipeline: Volume processing, surface extraction, staging, alignment
  • CLI Interface: Command-line tools for all major functions
  • Basic Visualization: 3D isosurfaces, slices, raycasting, probe tools
  • Documentation: Comprehensive tutorials and user guides
  • Custom Parameters: Advanced volume processing options

In Progress

  • Performance Optimization: Memory usage and processing speed improvements
  • Testing Suite: Comprehensive unit and integration tests
  • Community Building: User engagement and feedback collection

Short Term Roadmap (Q1 2026)

"Performance & Usability"

Primary Goals

  • Performance improvements for large datasets
  • Enhanced user experience with better feedback

Medium Term Roadmap (Q3-Q4 2026)

Version 0.4.0 - "Advanced Analytics"

Primary Goals

  • Statistical analysis tools for comparative studies
  • Data management and version control

Data Management 

# Version control
limb version-control experiment

# Data backup
limb backup experiment --remote s3://bucket

# Experiment database
limb database add experiment
limb database search --stage 25 --gene HOXA11

Improvements

  • Scalability: Support for 1000+ experiments
  • Collaboration: Multi-user support
  • Integration: Better integration with other tools
  • Automation: Reduced manual intervention

Future Vision (2027+) 🔮

Version 2.0.0 - "AI-First Platform"

Vision

Transform limb development research through AI-driven insights and automated discovery.

Revolutionary Features

AI-Driven Discovery - Automated hypothesis generation from data patterns - Predictive modeling of developmental outcomes - Cross-species analysis and comparison

MCP Literature Cross-Reference & Model Integration - Automated literature cross-referencing using the Model Context Protocol (MCP) to link experimental results with published studies - Database search and retrieval: Seamless querying of limb development literature and datasets via MCP

  • Context-aware recommendations: Suggest relevant papers, datasets, and models based on experiment metadata
  • Collaborative annotation: Enable users to annotate and share literature links and model references within the platform

Feature Priorities

High Priority (Must Have)

  1. Performance optimization for large datasets
  2. Batch processing capabilities
  3. Advanced visualization options
  4. Quality control tools
  5. Comprehensive testing suite

Medium Priority (Should Have)

  1. Statistical analysis tools
  2. Web interface for non-programmers
  3. Data management and version control
  4. Machine learning integration
  5. Collaborative features

Low Priority (Nice to Have)

  1. Advanced AI/ML capabilities
  2. Virtual reality visualization

This roadmap is a living document that evolves based on user feedback, technological advances, and scientific needs. We welcome your input and suggestions for improving LimbLab's development direction.