def main(args):
'''
args - parsed arguments that include pos_sequences, neg_sequences,
arch_file, and weights_file
'''
# encode fasta
print('Loading sequence data...')
pos_seq = encode_fasta_sequences(args.pos_sequences)
print('{} positive test sequences'.format(len(pos_seq)))
neg_seq = encode_fasta_sequences(args.neg_sequences)
print('{} negative test sequences\n'.format(len(neg_seq)))
# load model
prefix = args.arch_file.replace('.arch.json', '')
print('Loading {} model...'.format(prefix))
model = SequenceDNN.load(args.arch_file, args.weights_file)
# predict binding probability on test sequences
print('Getting predictions...')
pos_predictions = model.predict(pos_seq)
for index, pred in enumerate(pos_predictions):
print('positive_test_{}\tP(bound)={}'.format(index, pred[0]))
print('')
neg_predictions = model.predict(neg_seq)
for index, pred in enumerate(neg_predictions):
print('negative_test_{}\tP(bound)={}'.format(index, pred[0]))
print('')
# visualize trained model and motifs
print('Plotting deeplift scores on positive sequences...')
model.plot_deeplift(pos_seq, '{}_deeplift_positive'.format(prefix))
print('Plotting true motifs...')
motif_names = ['IRF_known1', 'NFKB_known1']
for index, motif in enumerate(motif_names):
fig = plot_motif(motif, figsize=(10, 4), ylab=motif)
fig.savefig('motif{}.png'.format(index + 1), bbox_inches='tight')
print('Plotting architecture...')
model.plot_architecture('{}_architecture.png'.format(prefix))
print('Plotting convolutional filters...')
plot_sequence_filters(model, prefix)
def plot_motifs(simulation_data):
for motif_name in simulation_data.motif_names:
plot_motif(motif_name, figsize=(10, 4), ylab=motif_name)
def plot_motifs(simulation_data):
for motif_name in simulation_data.motif_names:
plot_motif(motif_name, figsize=(10, 4), ylab=motif_name)