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.

To install the latest released version of DragoNN, install the Anaconda python distribution. Then, run:

conda install dragonn -c kundajelab

DragoNN is compatible with Python2 and Python3. Specific optional features such as DeepLIFT and MOE are compatible with Python2 only.

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.h5, 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 --arch-file training_example.arch.json --weights-file training_example.weights.h5

This will print the model's test performance metrics. Model predictions on sequence data can be obtained by running:

dragonn predict --sequences examples/example_pos_sequences.fa --arch-file training_example.arch.json --weights-file training_example.weights.h5 --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. Interpret sequence data with a dragonn model by running:

dragonn interpret --sequences examples/example_pos_sequences.fa --arch-file training_example.arch.json --weights-file training_example.weights.h5 --prefix example_interpretation

This will write the most important subsequence in each input sequence along with its location in the input sequence in the file example_interpretation.task_0.important_sequences.txt. Note: by default, only examples with predicted positive class probability >0.5 are interpreted. Examples below this threshold yield important subsequence of Ns with location -1. This default can be changed with the flag --pos-threshold.

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

We provide trained models, data, and code in paper_supplement to reproduce results in the DragoNN manuscript.

To reproduce the plots with model performance on simulations for varying data size and model architectures, run:

python paper_supplement/simulation_performance_results.py --model-files-dir paper_supplement/simulation_models/ --data-files-dir paper_supplement/simulation_data/ --results-dir paper_supplement/simulation_results

This script will save pdf files with the performance plots in paper_supplement/simulation_results.

See our roadmap for an outline of upcoming features. Additional feature suggestions are always welcome!