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import os
import tensorflow as tf
import pandas as pd
GUIDE_LEN = 23
CONTEXT_5P = 3
CONTEXT_3P = 0
TARGET_LEN = CONTEXT_5P + GUIDE_LEN + CONTEXT_3P
NUCLEOTIDE_TOKENS = dict(zip(['A', 'C', 'G', 'T'], [0, 1, 2, 3]))
NUCLEOTIDE_COMPLEMENT = dict(zip(['A', 'C', 'G', 'T'], ['T', 'G', 'C', 'A']))
def process_data(transcript_seq: str):
# convert to upper case
transcript_seq = transcript_seq.upper()
# get all target sites
target_seq = [transcript_seq[i: i + TARGET_LEN] for i in range(len(transcript_seq) - TARGET_LEN)]
# prepare guide sequences
guide_seq = [seq[CONTEXT_5P:len(seq) - CONTEXT_3P] for seq in target_seq]
guide_seq = [''.join([NUCLEOTIDE_COMPLEMENT[nt] for nt in list(seq)]) for seq in guide_seq]
# tokenize sequence
nucleotide_table = tf.lookup.StaticVocabularyTable(
initializer=tf.lookup.KeyValueTensorInitializer(
keys=tf.constant(list(NUCLEOTIDE_TOKENS.keys()), dtype=tf.string),
values=tf.constant(list(NUCLEOTIDE_TOKENS.values()), dtype=tf.int64)),
num_oov_buckets=1)
target_tokens = nucleotide_table.lookup(tf.stack([list(t) for t in target_seq], axis=0))
guide_tokens = nucleotide_table.lookup(tf.stack([list(g) for g in guide_seq], axis=0))
pad_5p = 255 * tf.ones([guide_tokens.shape[0], CONTEXT_5P], dtype=guide_tokens.dtype)
pad_3p = 255 * tf.ones([guide_tokens.shape[0], CONTEXT_3P], dtype=guide_tokens.dtype)
guide_tokens = tf.concat([pad_5p, guide_tokens, pad_3p], axis=1)
# model inputs
model_inputs = tf.concat([
tf.reshape(tf.one_hot(target_tokens, depth=4), [len(target_seq), -1]),
tf.reshape(tf.one_hot(guide_tokens, depth=4), [len(guide_tokens), -1]),
], axis=-1)
return target_seq, guide_seq, model_inputs
def tiger_predict(transcript_seq: str):
# load model
if os.path.exists('model'):
tiger = tf.keras.models.load_model('model')
else:
print('no saved model!')
exit()
# parse transcript sequence
target_seq, guide_seq, model_inputs = process_data(transcript_seq)
# get predictions
normalized_lfc = tiger.predict_step(model_inputs)
predictions = pd.DataFrame({'Guide': guide_seq, 'Normalized LFC': tf.squeeze(normalized_lfc).numpy()})
return predictions
if __name__ == '__main__':
# simple test case
transcript_sequence = 'ACGTACGTACGTACGTACGTACGTACGTACGT'.lower()
df = tiger_predict(transcript_sequence)
print(df)
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