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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.

Completed Features (Q2 2025)

  • 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 (Q3-Q4 2025)

  • Testing Suite: Comprehensive unit and integration tests
  • Community Building: User engagement and feedback collection
  • Multiple Experiment Visualitzation: The user can visualize multiple experiment data in one command.

Short Term Roadmap (Q1 2026)

"Performance & Usability"

  • Improve Aesthetic costumization from the CLI
  • Chick support for surfaces
  • Automatic Aligment not a priority - but a nice to have

Medium Term Roadmap (Q3-Q4 2026)

Version 1.0.0 - "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

Vision

Keep up with the times and adapt limb development research to computer modeling driven insights and automated discovery.

Revolutionary Features

Hypothesis 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

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.