dynamo-release answers a hard question in single-cell cancer biology: not just what state a cell is in, but where it’s headed. This open-source Python package infers RNA velocity from conventional or metabolic-labeling scRNA-seq, builds a continuous vector field of expression dynamics, and uses it to run in silico perturbations and predict cell fate. It’s well suited to mapping tumor cell-state transitions and the regulatory drivers behind therapy resistance and metastasis.
Sustainability Snapshot via Cancer Complexity Toolkit:
- Comprehensive documentation with README, declared dependencies, and an extensive tutorial and example suite alongside the repository
- Active development on GitHub (aristoteleo/dynamo-release)
- Quality indicators: JOSS Score 0.62 | Almanack 0.73
- All four Almanack checks pass, one of only 8 tools in the CCKP registry to do so
Why this matters: dynamo-release earns a “Maturing” Almanack grade (0.73), near the top of the CCKP catalog for sustainability, and clears all four Toolkit checks (complete README, declared dependencies, and test coverage present). For labs building single-cell workflows in cancer research, that means a documented, tested, reproducible path from raw scRNA-seq to predictive models of cell fate and perturbation response, not a one-off analysis script.
Attribution: Developed by Dr. Xiaojie Qiu and the Aristotle lab, Department of Genetics, Stanford University (aristoteleo/dynamo-release).
