Implementation of "predicting in vitro transcription factor binding sites using DNA sequence + shape"

• To install Keras with Tensorflow backend, please refer to https://keras.io/#installation.

• Python 2.7

Firstly, using encode.sh script to preprocess DNA sequences and their corresponding shape features.

• Usage:

  bash encode.sh <pbmdata>
  

  'pbmdata' denotes the path of storing experimental data, e.g. /yourpath/pbmdata.

Run DeepBind_K or DeepCNN without using DNA shape information

• Usage: you can excute run.sh script directly, in which you should modify the python command accordingly, e.g.:

  python train_val_test.py -datadir ./pbmdata/$eachTF/data -run 'noshape' -model 'shallow' -batchsize 300 -k 5 -params 30 --train
  

The command '-model' can be a choice of {'shollow', 'deep'}, where 'shollow' means DeepBind_K, and 'deep' means DeepCNN.

Run DLBSS(shallow) or DLBSS(deep) using DNA shape information

• Usage: you can excute run.sh script directly, in which you should modify the python command accordingly, e.g.:

  python train_val_test_hybrid.py -datadir ./pbmdata/$eachTF/data -run 'shape' -model 'shallow' -batchsize 300 -k 5 -params 30 --train
  

The command '-run' can be a choice of {'shape', 'MGW', 'ProT', 'Roll', 'HelT'}, where 'shape' means using all shape features, 'MGW' means using MGW shape feature, and so on.
The command '-model' can be a choice of {'shollow', 'deep'}, where 'shollow' means DLBSS(shallow), and 'deep' means 'DLBSS(deep)'.
Note that you should change the ouput path in the run.sh script, the naming rule is: 'model_' + args.model + '_' + args.run.

• Type the following for details on other optional arguments:

  python train_val_test_hybrid.py -h