Please see updated repository for this project at https://github.com/kundajelab/MPRA-DragoNN/.
This project applies convolutional neural networks to predict output from massively parallel reporter assays (MPRAs), with the aim of systematically decoding regulatory sequence patterns and identifying disease-causing noncodign variants.
Most of the code involved with training and interpreting the models can be found in Jupyter notebooks (compiled with Python 2.7) in deeplearn/scripts/. The final model was trained in Keras 1.2.2 and can be loaded with the load_model or model_from_json methods using the weights (deeplearn/model_files/sharpr_znormed_jul23/record_13_model_bgGhy_modelWeights.h5) and/or architecture files (deeplearn/model_files/sharpr_znormed_jul23/record_13_model_bgGhy_modelJson.json).
Dependencies:
- numpy, scipy, pandas, seaborn, etc.
- theano (0.9.0)
- keras (1.2.2)
- deeplift (0.5.2)
Here are descriptions of the relevant Jupyter notebooks in deeplearn/scripts/:
Sharpr Model Interpretation.ipynb: Scatter plots for replicates (Figure 1C), prediction performances (Figure 2A), performances for specific chromatin states (Figure 2B), exploration of regulatory motifs and grammars learned by the model.GBM Performance Benchmarking.ipynb: Training and performance testing of gradient boosting tree models (Figure S2).Sharpr DeepLIFT Scoring Validation: CENTIPEDE TFBS validation (Figure 2C-D), DeepLIFT motif scores, comparison to Sharpr (Fig. 3).Regulatory Grammar Discovery with Sharpr Models.ipynb: Exploration of predictive TF motif PWMs by comparing to DeepLIFT score profiles (Fig. 4).Variant Scoring.ipynb: Evaluation of SNPpet ISM scores for variant prioritization (Figures 5 and 6, Supp. Figures).ggplot2 visualizations.ipynb: R notebook to generate several of the manuscript's figures.
Some code written to process the Sharpr-MPRA and SuRE-seq datasets is available in the folders "sharpr" and "sureseq" respectively. These scripts process the data and produce structured input/output matrices that are used to train the deep learning models.
Feel free to contact Rajiv Movva (rmovva at mit dot edu) with any questions.