tangermeme
Toolkit for using trained genomic deep learning models: batched predictions, DeepLIFT/SHAP attributions, variant effects, ablations, seqlet calling, motif annotation and DNA design.
| Category | Genomics & DNA models |
|---|---|
| GitHub | jmschrei/tangermeme |
| First public | 2024-02-04 (source) |
| Paper | https://doi.org/10.1038/s41592-026-03254-z · Nature Methods (2026) |
| Code license | MIT |
| What this allows | Permissive (commercial OK) Use, modify and ship it inside closed-source commercial products; keep the license text and copyright notices. What do these mean? |
| License source | jmschrei/tangermeme · repository |
| Pricing | Open source |
| Tags | #interpretability#regulatory#variant-effect#pytorch |
| Last checked | · links verified |
Can I use tangermeme commercially?
Yes. The recorded license is permissive, so closed-source commercial use is allowed if you keep the notices.
- Code: MIT
- Source: jmschrei/tangermeme · last checked 2026-10-19
What the license types mean · not legal advice.
Specs
| Input length / context | Any input length (memory-bound); any output type, from scalars to profiles |
|---|---|
| Resolution | — |
| Species | — |
| Tasks | Post-training analysis of sequence-to-function models (attribution, variant effects, seqlets/motifs, design) |
| GitHub stars | 324 |
| Interface | Package (pip install tangermeme) |
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