TranscriptML is a toolkit for training, evaluating, and interpreting RNA sequence-to-function models. It provides command-line tools and reusable Python APIs for preparing sequence datasets, training models, evaluating held-out predictions, and investigating learned sequence features with analyses such as in silico mutagenesis, motif ablation, context scans, etc.
TranscriptML currently supports two main workflows:
- Saluki predicts transcriptome-wide RNA stability from transcript sequence, coding-frame annotations, and splice sites.
- MPRA-LegNet models MPRA measurements from variable sequence inserts and supports targets such as RNA stability, translation, protein output, etc.
In the future, I plan to also support RiboNN modeling of translation efficiency measurements and RBPNet modeling of RBP binding assays like eCLIP.
TranscriptML requires Python 3.10 or newer and PyTorch. Install the appropriate PyTorch build for your system using the official PyTorch installation guide, then install TranscriptML from source:
git clone https://github.com/kundajelab/TranscriptML.git
cd TranscriptML
python -m pip install -e .Optional dependencies and development installation instructions are described in the installation guide.
Full documentation, including usage guides and the Python API reference, is available at https://kundajelab.github.io/TranscriptML/.
This package is under active development, and as such I am actively working to expand and evolve TranscriptML's core functionalities and documentation.
If you use either implemented model, please cite the corresponding publication:
- MPRA-LegNet: Agarwal, V., Inoue, F., Schubach, M. et al. Massively parallel characterization of transcriptional regulatory elements. Nature 639, 411–420 (2025). doi:10.1038/s41586-024-08430-9
- Saluki: Agarwal, V. & Kelley, D. R. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biology 23, 245 (2022). doi:10.1186/s13059-022-02811-x