STEAM_evolution_aware#
The STEAM_evolution_aware model is an intermediate model from the evolutionary transfer learning study. Like STEAM_v1, it predicts cell-class-specific chromatin accessibility across 32 cell classes of the developing mouse embryo (E10–P0), but it is trained only on the mouse windows that survive evolutionary-coherence filtering against syntenic orthologs across 240 mammalian genomes, before the full synteny-supervised ortholog expansion of STEAM_v1.
It is provided to illustrate the contribution of evolutionary filtering to the final STEAM approach. Evolutionary information enters this model only through window selection — the training sequences themselves are all mm10. 2114bp one-hot encoded DNA sequences are used to predict normalized Tn5 cut-site accessibility (log-scaled) over the central region of each window, per cell class.
The model is a CNN multiclass regression model using the dilated_cnn() architecture. Like the other STEAM models, it maps DNA sequence directly to accessibility and is genome-agnostic at inference (applicable to any mammalian genome).
Details of the data and the model can be found in the original publication.
Citation
Qiu, C., Daza, R.M., Welsh, I.C. et al. Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes (2026). https://doi.org/10.62329/hxkk6249
Usage#
1import crested
2import keras
3
4# download model
5model_path, output_names = crested.get_model("STEAM_evolution_aware")
6
7# load model
8model = crested.utils.load_model(model_path)
9
10# make predictions
11sequence = "A" * 2114
12predictions = crested.tl.predict(sequence, model)
13print(predictions.shape)