DeepMEL2#
The DeepMEL2 model is a topic classification model that is very similar to the original DeepMEL1 model, but trained on more ATAC-seq samples (n=30) with a larger model architecture.
Using pycistopic, binarized topics per region were extracted for 47 target topics representing melanoma-specific coaccessible regions.
These sets of regions were used as input for a DL model, where 500bp one-hot encoded (ACGT) DNA sequences were used to predict the topic set to which the region belongs.
The model is a hybrid CNN-RNN multiclass classifier which is very similar to deeptopic_lstm()
with addition of a reverse complement layer in the first layer of the model.
DeepMEL2 uses 300 filters in its first convolutional layer instead of 128 in DeepMEL1, this time also initialized with JASPAR PWMs.
Details of the data and model can be found in the original publication.
Citation
Atak, Z.K., Taskiran, I.I. et al. Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning. Genome Res. 31, 1082–1096 (2021). https://doi.org/10.1101/gr.260851.120
Usage#
1import crested
2import keras
3
4# download model
5model_path, output_names = crested.get_model("DeepMEL2")
6
7# load model
8model = keras.models.load_model(model_path)
9
10# make predictions
11sequence = "A" * 500
12predictions = crested.tl.predict(sequence, model)
13print(predictions.shape)