TranscriptML is actively being developed and is subject to changes, some of which may not be backwards compatible
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.
For identifying learned motifs in either Saluki or MPRA-LegNet models, consider using RNA-MoDISco, my RNA-optimized fork of TF-MoDISco.
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