seq2ribo

Structure-aware integration of machine learning and simulation to predict ribosome location profiles from RNA sequences.

Installation

Prerequisites

• Linux (required for mamba-ssm)
• NVIDIA GPU with CUDA support
• CUDA Toolkit 11.8+ (check with nvcc --version)
• Conda package manager

Quick Install

# Clone the repository
git clone https://github.com/Kingsford-Group/seq2ribo.git
cd seq2ribo

# Create conda environment
conda env create -f environment.yml

# Activate
conda activate seq2ribo

# Install mamba-ssm (compiles from source, ~5-10 min)
python -m pip install --no-build-isolation mamba-ssm causal-conv1d

# Install seq2ribo package
pip install -e .

Verify Installation

python -c "import RNA; import mamba_ssm; import torch; print('All imports OK!')"

Note: If your CUDA version differs from 11.8, edit pytorch-cuda=11.8 in environment.yml to match your system.
See INSTALL.md for detailed troubleshooting.

Usage

Python API

from seq2ribo import Seq2Ribo

# Initialize predictor
predictor = Seq2Ribo(cell_line="hek293", weights_dir="weights")

# Predict ribosome density
sequence = "AUGGCCAAGCUGAAG..."
results = predictor.predict(sequence, task="riboseq")

Command Line

# Predict from sequence
python scripts/run_inference.py --seq "AUGGCC..." --cell-line hek293 --task riboseq

# Predict from FASTA
python scripts/run_inference.py --fasta input.fa --cell-line ipsc --output results.json

Supported Tasks

Task	Description	Output
riboseq	Ribosome profiling density	Per-codon counts
te	Translation efficiency	Scalar
protein	Protein expression	Scalar

Supported Cell Lines

• hek293 - HEK293
• lcl - Lymphoblastoid Cell Line
• rpe - RPE-1
• ipsc - iPSC

Project Structure

seq2ribo/
├── seq2ribo/          # Core package
│   ├── inference.py   # Main API
│   ├── models.py      # Neural network models
│   ├── simulation.py  # sTASEP simulation
│   └── geometry.py    # RNA structure features
├── scripts/           # CLI scripts
├── weights/           # Model checkpoints
├── tests/             # Test suite
└── environment.yml    # Conda environment

License

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

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