STEAM_v1#
The STEAM_v1 model is the final, evolution-augmented peak regression model from the evolutionary transfer learning study. It predicts cell-class-specific chromatin accessibility across 32 cell classes of the developing mouse embryo (E10–P0), profiled by single-cell ATAC-seq across 3.9 million nuclei.
STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling) expands the mm10 mouse training corpus with synteny-anchored enhancer orthologs from up to 241 mammalian genomes, in a synteny-supervised manner. The 2114bp mouse windows are lifted over to the 240 Zoonomia genomes; orthologs are retained when syntenically conserved (in at least half of the species) and concordant with the evolution-naive predictions (Pearson r ≥ 0.6), and are assigned the chromatin accessibility profile of their corresponding mouse window. This increases the effective training scale by up to ~195-fold (0.3M → ~58M sequences) and improves generalization for organism-wide inference of mammalian enhancer landscapes. 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.
Multi-species training is done with unmodified CREsted: the qualifying ortholog sequences are concatenated into a single custom genome FASTA, and added to the training AnnData as additional regions that inherit their parent mouse window’s labels. One register_genome() call over this combined genome then lets the standard AnnDataModule / Crested pipeline fetch and one-hot encode every species’ sequence on the fly, with chromosome-based train/validation/test splits shared between mouse and orthologs to avoid leakage.
The model is a CNN multiclass regression model using the dilated_cnn() architecture. Because it maps DNA sequence directly to accessibility, it is genome-agnostic at inference and can be applied to any mammalian genome — in the paper it is used to predict enhancer landscapes in human (hg38, hg19) and mouse (mm10).
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_v1")
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)