seq2ribo

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

[Read the Paper on bioRxiv]

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	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 software is licensed for Academic or Non-Profit Organization Noncommercial Research Use Only.

See the LICENSE file for the full terms.

For commercial use or any use not permitted by the academic license, please contact the options below to discuss licensing:

• Carl Kingsford (carlk@cs.cmu.edu)

Citation

If you use seq2ribo in your research, please cite:

@article{kaynar2026seq2ribo,
	title = {seq2ribo: Structure-aware integration of machine learning and
	         simulation to predict ribosome locations profiles from {RNA}
	         sequences},
	author = {G{\"u}n Kaynar and Carl Kingsford},
	year = {2026},
	journal = {bioRxiv},
	url = {https://www.biorxiv.org/content/10.64898/2026.02.08.700508v1},
}