The dragonn package implements Deep RegulAtory GenOmic Neural Networks (DragoNNs) for predictive modeling of regulatory genomics, nucleotide-resolution feature discovery, and simulations for systematic development and benchmarking.

The dragonn package provides a simple command line interface to train DragoNN models, test them, and predict on sequence data.

Train an example model by running:

dragonn train --pos-sequences examples/example_pos_sequences.fa --neg-sequences examples/example_neg_sequences.fa --prefix training_example

This will store a model file, training_example.model.json, with the model architecture and a weights file, training_example.weights.hd5, with the parameters of the trained model.

Test the model by running:

dragonn test --pos-sequences examples/example_pos_sequences.fa --neg-sequences examples/example_neg_sequences.fa --model-file training_example.model.json --weights-file training_example.weights.hd5

This will print the model's test performance metrics.

Finally, get model predictions on sequence data by running:

dragonn predict --sequences examples/example_pos_sequences.fa --model-file training_example.model.json --weights-file training_example.weights.hd5 --output-file example_predictions.txt

This will store the model predictions for sequences in example_pos_sequences.fa in the output file example_predictions.txt.

We encourage DragoNN users to share models in the Model Zoo. Enjoy!