Borzoi Prime

Contents

Borzoi Prime#

The Borzoi Prime model is a large model trained on pseudobulked scRNA tracks as well as base Borzoi bulk ENCODE and FANTOM DNase, CAGE and RNA-seq data from a wide variety of human and mouse tissues. It predicts 12288 bins of 16bp, corresponding to the core 196608 bp of the input sequence.

It was originally provided based on the Baskerville package, and its weights and architecture have been ported to CREsted. The model was trained on sequences tiled across the genome, which can be found in the original Borzoi repository, with fold 4 as validation set, test 3 as test set, and the rest as training set. All replicates are trained on the same data folds. Note that the original human training used a modified hg38 genome, where the allele with maximum frequency in gnomAD was substituted at each position. The original model has a shared trunk and two organism-specific heads, which are provided as two specific models for human and mouse here, resulting in models borzoiprime_human_rep0-borzoiprime_human_rep3 and borzoiprime_mouse_rep0-borzoiprime_mouse_rep3.

The model is a CNN+Transformer+Upsampling model using the borzoi_prime() architecture.

Details of the data and the model can be found in the original publication.

Warning

The Borzoi (Prime) architecture uses custom layers that are serialized inside the CREsted package. To ensure that the model is loaded correctly, make sure that CREsted is imported before loading the model. If it still refuses to load, add MultiheadAttention as a custom object, as in the example.

Citation

Linder, J., Yuan, H. and Kelley, D.R. Predicting cell type-specific coverage profiles from DNA sequence. bioRxiv (2025). https://doi.org/10.1101/2025.06.10.658961

License

The original model is licensed under the Apache License, version 2.0.

Usage#

 1import crested
 2import keras
 3from crested.tl.zoo.utils._attention import MultiheadAttention
 4
 5# download model
 6model_path, output_names = crested.get_model("borzoiprime_human_rep0")
 7
 8# load model
 9model = keras.models.load_model(model_path, compile=False)
10
11# load the model with custom_objects as fallback
12# model = keras.models.load_model(
13#     model_path,
14#     custom_objects={
15#         'MultiheadAttention': MultiheadAttention
16#     }
17# )
18
19# make predictions
20sequence = "A" * 524288
21predictions = crested.tl.predict(sequence, model)
22print(predictions.shape)
Contents