metalncrna 1.1.8

A portable and modular meta-predictor for identifying Long Non-coding RNAs (lncRNAs).

metaLncRNA v1.1.8 🧬🤖

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metaLncRNA is a modular, high-performance Python framework designed to identify Long Non-coding RNAs (lncRNAs) by orchestrating an ensemble of seven diverse computational tools. It resolves the "reproducibility gap" by automating environment management and providing a robust consensus prediction through weighted soft-voting.

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📂 Repository Structure

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├── conda/                   # Bioconda recipe and metadata
├── deploy/                  # Containerization (Dockerfile, Singularity.def)
├── docs/                    # Technical documentation and user guides
├── examples/                # Quick-start samples (FASTA, config templates)
├── galaxy/                  # Galaxy Tool wrapper and test data
├── INPI_Registration/       # Legal software registration assets
├── paper/                   # JOSS publication manuscript and bibliography
├── scripts/                 # Bash scripts for HPC/Batch processing
├── src/
│   └── metalncrna/          # Main Python Package
│       ├── cli.py           # Command-line interface entry point
│       ├── adapters/        # Wrappers for 7 lncRNA predictors
│       ├── engine/          # Core logic (Consensus, Dispatcher, Trainer)
│       ├── utils/           # AI Agent, Env management, Reports, FASTA handling
│       ├── data/            # Built-in weights and default configurations
│       └── third_party/     # Bundled legacy tools (CNCI, CPPred, LGC)
├── tests/                   # Comprehensive Unit and Integration tests
├── pyproject.toml           # Build system and dependency definitions
└── pixi.toml                # Environment management configuration

🧩 Core Components Detail

• src/metalncrna/adapters/: Orchestrates external tools like RNAsamba, CPAT, CPC2, etc., providing a unified interface for prediction.
• src/metalncrna/engine/:
  • consensus.py: Implements the weighted soft-voting algorithm.
  • dispatcher.py: Manages parallel execution of the ensemble.
• src/metalncrna/utils/agent.py: Integrates with local LLMs (Ollama) for automated biological interpretation of results.
• galaxy/: Allows metaLncRNA to be integrated into Galaxy instances, supporting reproducible web-based workflows.

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🔧 Recent Fixes (v1.1.8)

• CPC2 Integration: Fixed a critical parsing error where coding probability and label columns were mismatched in the final report.
• Consensus Accuracy: Improved consensus support calculation by ensuring all functional tools are correctly accounted for.
• Cleanup: Removed unimplemented/experimental adapters to ensure stability.

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⚙️ Configuration

metaLncRNA follows a robust configuration loading order:

1. Internal Defaults: Built-in weights and paths in src/metalncrna/data/default_config.yaml.
2. Local Config: metaLncRNA_config.yaml in your current working directory.
3. User Home: ~/.metalncrna/config.yaml.
4. Explicit Path: Provided via the -c or --config flag.

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🚀 Key Features

• Ensemble Prediction: Combines 7 tools (RNAsamba, CPAT, CPC2, PLEK, CNCI, CPPred, LGC).
• Interactive AI Agent: Integrated local LLM assistant (Llama-3.2 or OpenBioLLM) to interpret results and explain classification decisions.
• Reproducibility First: Built-in environment isolation via Mamba and Pixi.
• Standardized Reports: Comprehensive TSV reports with tool congruence metrics.
• Publication Ready: Formatted according to JOSS standards for scientific software.

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📖 Documentation

For detailed instructions, please refer to our Documentation Hub:

• 🛠️ User Guide: Installation, common commands, and AI Chat usage.
• 🏗️ Technical Architecture: Ensemble methodology and AI-driven interpretation layer.
• 🔧 Troubleshooting: Common issues and hardware requirements.

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🛠️ Quick Start

1. Installation

Option A: via pip (Fastest)

We recommend using a virtual environment:

python3 -m venv venv
source venv/bin/activate
pip install "metalncrna[agent]"
metalncrna setup

Option B: via Conda / Mamba

Perfect for bioinformaticians using Bioconda:

# Create environment from the provided file
mamba env create -f environment.yml
conda activate metalncrna

# Finalize setup
metalncrna setup

2. Run Integrated Pipeline

metalncrna predict -i transcripts.fasta -o ./results -p MyAnalysis

3. Ask the AI Agent

# Get a summary of your findings
metalncrna ask "Summarize the analysis results" -r ./results/MyAnalysis/metalncrna_results.tsv

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🐳 Deployment

Pre-configured definitions are available for Docker and Singularity/Apptainer in the deploy/ directory.

🤝 Contributing

Contributions are welcome! Please see our CONTRIBUTING.md for details.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Developed by LaBiOmicS - Laboratory of Bioinformatics and Omics Sciences. Institution: Universidade de Mogi das Cruzes (UMC)